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1.  The Import of Trust in Regular Providers to Trust in Cancer Physicians among White, African American, and Hispanic Breast Cancer Patients 
Interpersonal trust is an important component of the patient-doctor relationship. Little is known about patients’ trust in the multiple providers seen when confronting serious illness.
To characterize breast cancer patients’ trust in their regular providers, diagnosing physicians, and cancer treatment team and examine whether high trust in one’s regular provider confers high trust to cancer physicians.
In-person interviews.
704 white, black, and Hispanic breast cancer patients, age 30 to 79, with a first primary in situ or invasive breast cancer who reported having a regular provider.
We measure trust in: (1) regular provider, (2) diagnosing doctors, and (3) cancer treatment team. Other variables include demographic variables, preventive health care, comorbidities, time with regular provider, time since diagnosis, cancer stage, and treatment modality.
Sixty-five percent of patients reported high trust in their regular provider, 84% indicated high trust in their diagnosing doctors, and 83% reported high trust in their treatment team. Women who reported high trust in their regular provider were significantly more likely to be very trusting of diagnosing doctors (OR: 3.44, 95% CI: 2.27–5.21) and cancer treatment team (OR: 3.09, 95% CI: 2.02–4.72 ). Black women were significantly less likely to be very trusting of their regular doctor (OR: 0.58, 95% CI: 0.38–0.88) and cancer treatment team (OR: 0.45, 95% CI: 0.25–0.80). English-speaking Hispanic women were significantly less trusting of their diagnosing doctors (OR: 0.29, 95% CI: 0.11–0.80).
Our results suggest that patients are very trusting of their breast cancer providers. This is an important finding given that research with other populations has shown an association between trust and patient satisfaction and treatment adherence. Our findings also suggest that a trusting relationship with a regular provider facilitates trusting relationships with specialists. Additional work is needed to increase interpersonal trust among black women.
PMCID: PMC3024096  PMID: 20811783
breast cancer; doctor-patient relationships; primary care; specialty care
2.  Trust in the Medical Profession: Conceptual and Measurement Issues 
Health Services Research  2002;37(5):1419-1439.
To develop and test a multi-item measure for general trust in physicians, in contrast with trust in a specific physician.
Data Sources
Random national telephone survey of 502 adult subjects with a regular physician and source of payment.
Study Design
Based on a multidimensional conceptual model, a large pool of candidate items was generated, tested, and revised using focus groups, expert reviewers, and pilot testing. The scale was analyzed for its factor structure, internal consistency, construct validity, and other psychometric properties.
Principal Findings
The resulting 11-item scale measuring trust in physicians generally is consistent with most aspects of the conceptual model except that it does not include the dimension of confidentiality. This scale has a single-factor structure, good internal consistency (alpha=.89), and good response variability (range=11–54; mean=33.5; SD=6.9). This scale is related to satisfaction with care, trust in one's physician, following doctors' recommendations, having no prior disputes with physicians, not having sought second opinions, and not having changed doctors. No association was found with race/ethnicity. While general trust and interpersonal trust are qualitatively similar, they are only moderately correlated with each other and general trust is substantially lower.
Emerging research on patients' trust has focused on interpersonal trust in a specific, known physician. Trust in physicians in general is also important and differs significantly from interpersonal physician trust. General physician trust potentially has a strong influence on important behaviors and attitudes, and on the formation of interpersonal physician trust.
PMCID: PMC1464022  PMID: 12479504
Trust; medical profession; scale development
3.  Physician Awareness of Drug Cost: A Systematic Review 
PLoS Medicine  2007;4(9):e283.
Pharmaceutical costs are the fastest-growing health-care expense in most developed countries. Higher drug costs have been shown to negatively impact patient outcomes. Studies suggest that doctors have a poor understanding of pharmaceutical costs, but the data are variable and there is no consistent pattern in awareness. We designed this systematic review to investigate doctors' knowledge of the relative and absolute costs of medications and to determine the factors that influence awareness.
Methods and Findings
Our search strategy included The Cochrane Library, EconoLit, EMBASE, and MEDLINE as well as reference lists and contact with authors who had published two or more articles on the topic or who had published within 10 y of the commencement of our review. Studies were included if: either doctors, trainees (interns or residents), or medical students were surveyed; there were more than ten survey respondents; cost of pharmaceuticals was estimated; results were expressed quantitatively; there was a clear description of how authors defined “accurate estimates”; and there was a description of how the true cost was determined. Two authors reviewed each article for eligibility and extracted data independently. Cost accuracy outcomes were summarized, but data were not combined in meta-analysis because of extensive heterogeneity. Qualitative data related to physicians and drug costs were also extracted. The final analysis included 24 articles. Cost accuracy was low; 31% of estimates were within 20% or 25% of the true cost, and fewer than 50% were accurate by any definition of cost accuracy. Methodological weaknesses were common, and studies of low methodological quality showed better cost awareness. The most important factor influencing the pattern and accuracy of estimation was the true cost of therapy. High-cost drugs were estimated more accurately than inexpensive ones (74% versus 31%, Chi-square p < 0.001). Doctors consistently overestimated the cost of inexpensive products and underestimated the cost of expensive ones (binomial test, 89/101, p < 0.001). When asked, doctors indicated that they want cost information and feel it would improve their prescribing but that it is not accessible.
Doctors' ignorance of costs, combined with their tendency to underestimate the price of expensive drugs and overestimate the price of inexpensive ones, demonstrate a lack of appreciation of the large difference in cost between inexpensive and expensive drugs. This discrepancy in turn could have profound implications for overall drug expenditures. Much more focus is required in the education of physicians about costs and the access to cost information. Future research should focus on the accessibility and reliability of medical cost information and whether the provision of this information is used by doctors and makes a difference to physician prescribing. Additionally, future work should strive for higher methodological standards to avoid the biases we found in the current literature, including attention to the method of assessing accuracy that allows larger absolute estimation ranges for expensive drugs.
From a review of data from 24 studies, Michael Allan and colleagues conclude that doctors often underestimate the price of expensive drugs and overestimate the price of those that are inexpensive.
Editors' Summary
Many medicines are extremely expensive, and the cost of buying them is a major (and increasing) proportion of the total cost of health care. Governments and health-care organizations try to find ways of keeping down costs without reducing the effectiveness of the health care they provide, but their efforts to control what is spent on medicines have not been very successful. There are often two or more equally effective drugs available for treating the same condition, and it would obviously help keep costs down if, when a doctor prescribes a medicine, he or she chose the cheapest of the effective drugs available. This choice could result in savings for whoever is paying for the drugs, be it the government, the patient, or a medical insurance organization.
Why Was This Study Done?
Doctors who prescribe drugs cannot be expected to know the exact cost of each drug on the market, but it would he helpful if they had some impression of the cost of a treatment and how the various alternatives compare in price. However, systems deciding how drugs are priced are often very complex. (This is particularly the case in the US.) The researchers wanted to find out how aware doctors are regarding drug costs and the difference between the alternatives. They also wanted to know what factors affected their awareness.
What Did the Researchers Do and Find?
They decided to do a systematic review of all the research already conducted that addressed this issue so that the evidence from all of them could be considered together. In order to do such a review they had to specify precise requirements for the type of study that they would include and then comprehensively search the medical literature for such studies. They found 24 studies that met their requirements. From these studies, they concluded that doctors were usually not accurate when asked to estimate the cost of drugs; doctors came up with estimates that were within 25% of the true cost less than one-third of the time. In particular doctors tended to underestimate the cost of expensive drugs and overestimate the cost of the cheaper alternatives. A further analysis of the studies showed that many doctors said they would appreciate more accurate information on costs to help them choose which drugs to prescribe but that such information was not readily available.
What Do These Findings Mean?
The researchers concluded that their systematic review demonstrates a lack of appreciation by prescribing doctors of the large difference in cost between inexpensive and expensive drugs, and that this finding has serious implications for overall spending on drugs. They call for more education and information to be provided to doctors on the cost of medicines together with better processes to help doctors in making such decisions.
Additional Information.
Please access these Web sites via the online version of this summary at
A brief guide to systematic reviews has been published by the BMJ (British Medical Journal)
The Web site of the Cochrane Collaboration is a more detailed source of information on systematic reviews; in particular there is a newcomers' guide and information for health-care consumers
The Kaiser Family Foundation, a nonprofit, private operating foundation focusing on the major health care issues in the US, has a section on prescription drugs and their costs
PMCID: PMC1989748  PMID: 17896856
4.  Patient Satisfaction with Hospital Inpatient Care: Effects of Trust, Medical Insurance and Perceived Quality of Care 
PLoS ONE  2016;11(10):e0164366.
Deteriorations in the patient-provider relationship in China have attracted increasing attention in the international community. This study aims to explore the role of trust in patient satisfaction with hospital inpatient care, and how patient-provider trust is shaped from the perspectives of both patients and providers.
We adopted a mixed methods approach comprising a multivariate logistic regression model using secondary data (1200 people with inpatient experiences over the past year) from the fifth National Health Service Survey (NHSS, 2013) in Heilongjiang Province to determine the associations between patient satisfaction and trust, financial burden and perceived quality of care, followed by in-depth interviews with 62 conveniently selected key informants (27 from health and 35 from non-health sectors). A thematic analysis established a conceptual framework to explain deteriorating patient-provider relationships.
About 24% of respondents reported being dissatisfied with hospital inpatient care. The logistic regression model indicated that patient satisfaction was positively associated with higher level of trust (OR = 14.995), lower levels of hospital medical expenditure (OR = 5.736–1.829 as compared with the highest quintile of hospital expenditure), good staff attitude (OR = 3.155) as well as good ward environment (OR = 2.361). But patient satisfaction was negatively associated with medical insurance for urban residents and other insurance status (OR = 0.215–0.357 as compared with medical insurance for urban employees). The qualitative analysis showed that patient trust—the most significant predictor of patient satisfaction—is shaped by perceived high quality of service delivery, empathic and caring interpersonal interactions, and a better designed medical insurance that provides stronger financial protection and enables more equitable access to health care.
At the core of high levels of patient dissatisfaction with hospital care is the lack of trust. The current health care system reform in China has yet to address the fundamental problems embedded in the system that caused distrust. A singular focus on doctor-patient inter-personal interactions will not offer a successful solution to the deteriorated patient-provider relationships unless a systems approach to accountability is put into place involving all stakeholders.
PMCID: PMC5068749  PMID: 27755558
5.  Instrumental and socioemotional communications in doctor-patient interactions in urban and rural clinics 
Location of practice, such as working in a rural or urban clinic, may influence how physicians communicate with their patients. This exploratory pilot study examines the communication styles used during doctor-patient interactions in urban and rural family practice settings in Western Canada.
We analyzed observation and interview data from four physicians practicing in these different locations. Using a grounded theory approach, communications were categorized as either instrumental or socioemotional. Instrumental communication refers to “cure-oriented interactions” and tends to be more task-oriented focusing on the patient’s health concerns and reason for the appointment. In contrast, socioemotional communication refers to more “care-oriented interactions” that may make the patient feel comfortable, relieve patient anxiety and build a trusting relationship.
The physicians in small, rural towns appear to know their patients and their families on a more personal level and outside of their office, and engage in more socioemotional communications compared to those practicing in suburban clinics in a large urban centre. Knowing patients outside the clinic seems to change the nature of the doctor-patient interaction, and, in turn, the doctor-patient relationship itself. Interactions between urban doctors and their patients had a mixture of instrumental and socioemotional communications, while interactions between rural doctors and their patients tended to be highly interpersonal, often involving considerable socioemotional communication and relationship-building.
Despite the different ways that doctors and patients communicate with each other in the two settings, rural and urban doctors spend approximately the same amount of time with their patients. Thus, greater use of socioemotional communication by rural doctors, which may ease patient anxiety and increase patient trust, did not appear to add extra time to the patient visit. Research suggests that socioemotional communication may ultimately lead to better patient outcomes, which implies that health differences between rural and urban settings could be linked to differences in doctor-patient communication styles.
PMCID: PMC3734115  PMID: 23835062
Physicians; Family practice; Rural clinics; Urban clinics; Patient-physician communication; Patient-physician interaction
6.  Adult cystic fibrosis patients' experiences of primary care consultations: a qualitative study 
‘Expert patient’ programmes have been introduced in the UK as a new approach to chronic disease management for the 21st century. The average survival age of those with cystic fibrosis (CF) has steadily increased such that the majority of those with the condition now live into adulthood. Currently, specialist CF centres deliver the core of medical care, with primary care providing access to prescribed medicines, referral to other services, and care of non-CF needs, however, it is necessary to provide a more comprehensive service for adult CF patients, involving both specialist centres and primary care. To date, little is known about these expert patients' experiences of primary care.
To investigate how young adults with CF perceive and experience primary healthcare services.
Design of study
Qualitative study.
One specialist CF centre in southeast England.
Interview study of 31 patients with CF, aged 18 years or over.
Adults with CF consult in primary care on two distinct levels: as lay and expert patients. When consulting as experts, patients tend to operate as consumers of health care and perceive a satisfactory doctor–patient relationship to be influenced by three factors: GPs' understanding of how people live with CF, GPs' ability to prescribe certain specialist medications, and sensitive management of the cost of health care for adults with CF. A doctor–patient relationship based on trust and understanding is seen as desirable, but requires that these factors are addressed both by the GP and the patient.
Expert patient policy has focused on the role of patients with common chronic conditions in secondary and tertiary care, with little consideration of how adults with rare chronic illness and their GPs manage health problems that can be addressed in primary care. Enabling easy access to holistic care, as well as establishing successful trusting relationships with people with long-term rare conditions, is a necessary foundation for expert patients to take an active role in their care.
PMCID: PMC1872062  PMID: 16834878
cystic fibrosis; doctor–patient relations; expert patient; primary health care; qualitative research
7.  Internet-Based Device-Assisted Remote Monitoring of Cardiovascular Implantable Electronic Devices 
Executive Summary
The objective of this Medical Advisory Secretariat (MAS) report was to conduct a systematic review of the available published evidence on the safety, effectiveness, and cost-effectiveness of Internet-based device-assisted remote monitoring systems (RMSs) for therapeutic cardiac implantable electronic devices (CIEDs) such as pacemakers (PMs), implantable cardioverter-defibrillators (ICDs), and cardiac resynchronization therapy (CRT) devices. The MAS evidence-based review was performed to support public financing decisions.
Clinical Need: Condition and Target Population
Sudden cardiac death (SCD) is a major cause of fatalities in developed countries. In the United States almost half a million people die of SCD annually, resulting in more deaths than stroke, lung cancer, breast cancer, and AIDS combined. In Canada each year more than 40,000 people die from a cardiovascular related cause; approximately half of these deaths are attributable to SCD.
Most cases of SCD occur in the general population typically in those without a known history of heart disease. Most SCDs are caused by cardiac arrhythmia, an abnormal heart rhythm caused by malfunctions of the heart’s electrical system. Up to half of patients with significant heart failure (HF) also have advanced conduction abnormalities.
Cardiac arrhythmias are managed by a variety of drugs, ablative procedures, and therapeutic CIEDs. The range of CIEDs includes pacemakers (PMs), implantable cardioverter-defibrillators (ICDs), and cardiac resynchronization therapy (CRT) devices. Bradycardia is the main indication for PMs and individuals at high risk for SCD are often treated by ICDs.
Heart failure (HF) is also a significant health problem and is the most frequent cause of hospitalization in those over 65 years of age. Patients with moderate to severe HF may also have cardiac arrhythmias, although the cause may be related more to heart pump or haemodynamic failure. The presence of HF, however, increases the risk of SCD five-fold, regardless of aetiology. Patients with HF who remain highly symptomatic despite optimal drug therapy are sometimes also treated with CRT devices.
With an increasing prevalence of age-related conditions such as chronic HF and the expanding indications for ICD therapy, the rate of ICD placement has been dramatically increasing. The appropriate indications for ICD placement, as well as the rate of ICD placement, are increasingly an issue. In the United States, after the introduction of expanded coverage of ICDs, a national ICD registry was created in 2005 to track these devices. A recent survey based on this national ICD registry reported that 22.5% (25,145) of patients had received a non-evidence based ICD and that these patients experienced significantly higher in-hospital mortality and post-procedural complications.
In addition to the increased ICD device placement and the upfront device costs, there is the need for lifelong follow-up or surveillance, placing a significant burden on patients and device clinics. In 2007, over 1.6 million CIEDs were implanted in Europe and the United States, which translates to over 5.5 million patient encounters per year if the recommended follow-up practices are considered. A safe and effective RMS could potentially improve the efficiency of long-term follow-up of patients and their CIEDs.
In addition to being therapeutic devices, CIEDs have extensive diagnostic abilities. All CIEDs can be interrogated and reprogrammed during an in-clinic visit using an inductive programming wand. Remote monitoring would allow patients to transmit information recorded in their devices from the comfort of their own homes. Currently most ICD devices also have the potential to be remotely monitored. Remote monitoring (RM) can be used to check system integrity, to alert on arrhythmic episodes, and to potentially replace in-clinic follow-ups and manage disease remotely. They do not currently have the capability of being reprogrammed remotely, although this feature is being tested in pilot settings.
Every RMS is specifically designed by a manufacturer for their cardiac implant devices. For Internet-based device-assisted RMSs, this customization includes details such as web application, multiplatform sensors, custom algorithms, programming information, and types and methods of alerting patients and/or physicians. The addition of peripherals for monitoring weight and pressure or communicating with patients through the onsite communicators also varies by manufacturer. Internet-based device-assisted RMSs for CIEDs are intended to function as a surveillance system rather than an emergency system.
Health care providers therefore need to learn each application, and as more than one application may be used at one site, multiple applications may need to be reviewed for alarms. All RMSs deliver system integrity alerting; however, some systems seem to be better geared to fast arrhythmic alerting, whereas other systems appear to be more intended for remote follow-up or supplemental remote disease management. The different RMSs may therefore have different impacts on workflow organization because of their varying frequency of interrogation and methods of alerts. The integration of these proprietary RM web-based registry systems with hospital-based electronic health record systems has so far not been commonly implemented.
Currently there are 2 general types of RMSs: those that transmit device diagnostic information automatically and without patient assistance to secure Internet-based registry systems, and those that require patient assistance to transmit information. Both systems employ the use of preprogrammed alerts that are either transmitted automatically or at regular scheduled intervals to patients and/or physicians.
The current web applications, programming, and registry systems differ greatly between the manufacturers of transmitting cardiac devices. In Canada there are currently 4 manufacturers—Medtronic Inc., Biotronik, Boston Scientific Corp., and St Jude Medical Inc.—which have regulatory approval for remote transmitting CIEDs. Remote monitoring systems are proprietary to the manufacturer of the implant device. An RMS for one device will not work with another device, and the RMS may not work with all versions of the manufacturer’s devices.
All Internet-based device-assisted RMSs have common components. The implanted device is equipped with a micro-antenna that communicates with a small external device (at bedside or wearable) commonly known as the transmitter. Transmitters are able to interrogate programmed parameters and diagnostic data stored in the patients’ implant device. The information transfer to the communicator can occur at preset time intervals with the participation of the patient (waving a wand over the device) or it can be sent automatically (wirelessly) without their participation. The encrypted data are then uploaded to an Internet-based database on a secure central server. The data processing facilities at the central database, depending on the clinical urgency, can trigger an alert for the physician(s) that can be sent via email, fax, text message, or phone. The details are also posted on the secure website for viewing by the physician (or their delegate) at their convenience.
Research Questions
The research directions and specific research questions for this evidence review were as follows:
To identify the Internet-based device-assisted RMSs available for follow-up of patients with therapeutic CIEDs such as PMs, ICDs, and CRT devices.
To identify the potential risks, operational issues, or organizational issues related to Internet-based device-assisted RM for CIEDs.
To evaluate the safety, acceptability, and effectiveness of Internet-based device-assisted RMSs for CIEDs such as PMs, ICDs, and CRT devices.
To evaluate the safety, effectiveness, and cost-effectiveness of Internet-based device-assisted RMSs for CIEDs compared to usual outpatient in-office monitoring strategies.
To evaluate the resource implications or budget impact of RMSs for CIEDs in Ontario, Canada.
Research Methods
Literature Search
The review included a systematic review of published scientific literature and consultations with experts and manufacturers of all 4 approved RMSs for CIEDs in Canada. Information on CIED cardiac implant clinics was also obtained from Provincial Programs, a division within the Ministry of Health and Long-Term Care with a mandate for cardiac implant specialty care. Various administrative databases and registries were used to outline the current clinical follow-up burden of CIEDs in Ontario. The provincial population-based ICD database developed and maintained by the Institute for Clinical Evaluative Sciences (ICES) was used to review the current follow-up practices with Ontario patients implanted with ICD devices.
Search Strategy
A literature search was performed on September 21, 2010 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published from 1950 to September 2010. Search alerts were generated and reviewed for additional relevant literature until December 31, 2010. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria full-text articles were obtained. Reference lists were also examined for any additional relevant studies not identified through the search.
Inclusion Criteria
published between 1950 and September 2010;
English language full-reports and human studies;
original reports including clinical evaluations of Internet-based device-assisted RMSs for CIEDs in clinical settings;
reports including standardized measurements on outcome events such as technical success, safety, effectiveness, cost, measures of health care utilization, morbidity, mortality, quality of life or patient satisfaction;
randomized controlled trials (RCTs), systematic reviews and meta-analyses, cohort and controlled clinical studies.
Exclusion Criteria
non-systematic reviews, letters, comments and editorials;
reports not involving standardized outcome events;
clinical reports not involving Internet-based device assisted RM systems for CIEDs in clinical settings;
reports involving studies testing or validating algorithms without RM;
studies with small samples (<10 subjects).
Outcomes of Interest
The outcomes of interest included: technical outcomes, emergency department visits, complications, major adverse events, symptoms, hospital admissions, clinic visits (scheduled and/or unscheduled), survival, morbidity (disease progression, stroke, etc.), patient satisfaction, and quality of life.
Summary of Findings
The MAS evidence review was performed to review available evidence on Internet-based device-assisted RMSs for CIEDs published until September 2010. The search identified 6 systematic reviews, 7 randomized controlled trials, and 19 reports for 16 cohort studies—3 of these being registry-based and 4 being multi-centered. The evidence is summarized in the 3 sections that follow.
1. Effectiveness of Remote Monitoring Systems of CIEDs for Cardiac Arrhythmia and Device Functioning
In total, 15 reports on 13 cohort studies involving investigations with 4 different RMSs for CIEDs in cardiology implant clinic groups were identified in the review. The 4 RMSs were: Care Link Network® (Medtronic Inc,, Minneapolis, MN, USA); Home Monitoring® (Biotronic, Berlin, Germany); House Call 11® (St Jude Medical Inc., St Pauls, MN, USA); and a manufacturer-independent RMS. Eight of these reports were with the Home Monitoring® RMS (12,949 patients), 3 were with the Care Link® RMS (167 patients), 1 was with the House Call 11® RMS (124 patients), and 1 was with a manufacturer-independent RMS (44 patients). All of the studies, except for 2 in the United States, (1 with Home Monitoring® and 1 with House Call 11®), were performed in European countries.
The RMSs in the studies were evaluated with different cardiac implant device populations: ICDs only (6 studies), ICD and CRT devices (3 studies), PM and ICD and CRT devices (4 studies), and PMs only (2 studies). The patient populations were predominately male (range, 52%–87%) in all studies, with mean ages ranging from 58 to 76 years. One study population was unique in that RMSs were evaluated for ICDs implanted solely for primary prevention in young patients (mean age, 44 years) with Brugada syndrome, which carries an inherited increased genetic risk for sudden heart attack in young adults.
Most of the cohort studies reported on the feasibility of RMSs in clinical settings with limited follow-up. In the short follow-up periods of the studies, the majority of the events were related to detection of medical events rather than system configuration or device abnormalities. The results of the studies are summarized below:
The interrogation of devices on the web platform, both for continuous and scheduled transmissions, was significantly quicker with remote follow-up, both for nurses and physicians.
In a case-control study focusing on a Brugada population–based registry with patients followed-up remotely, there were significantly fewer outpatient visits and greater detection of inappropriate shocks. One death occurred in the control group not followed remotely and post-mortem analysis indicated early signs of lead failure prior to the event.
Two studies examined the role of RMSs in following ICD leads under regulatory advisory in a European clinical setting and noted:
– Fewer inappropriate shocks were administered in the RM group.
– Urgent in-office interrogations and surgical revisions were performed within 12 days of remote alerts.
– No signs of lead fracture were detected at in-office follow-up; all were detected at remote follow-up.
Only 1 study reported evaluating quality of life in patients followed up remotely at 3 and 6 months; no values were reported.
Patient satisfaction was evaluated in 5 cohort studies, all in short term follow-up: 1 for the Home Monitoring® RMS, 3 for the Care Link® RMS, and 1 for the House Call 11® RMS.
– Patients reported receiving a sense of security from the transmitter, a good relationship with nurses and physicians, positive implications for their health, and satisfaction with RM and organization of services.
– Although patients reported that the system was easy to implement and required less than 10 minutes to transmit information, a variable proportion of patients (range, 9% 39%) reported that they needed the assistance of a caregiver for their transmission.
– The majority of patients would recommend RM to other ICD patients.
– Patients with hearing or other physical or mental conditions hindering the use of the system were excluded from studies, but the frequency of this was not reported.
Physician satisfaction was evaluated in 3 studies, all with the Care Link® RMS:
– Physicians reported an ease of use and high satisfaction with a generally short-term use of the RMS.
– Physicians reported being able to address the problems in unscheduled patient transmissions or physician initiated transmissions remotely, and were able to handle the majority of the troubleshooting calls remotely.
– Both nurses and physicians reported a high level of satisfaction with the web registry system.
2. Effectiveness of Remote Monitoring Systems in Heart Failure Patients for Cardiac Arrhythmia and Heart Failure Episodes
Remote follow-up of HF patients implanted with ICD or CRT devices, generally managed in specialized HF clinics, was evaluated in 3 cohort studies: 1 involved the Home Monitoring® RMS and 2 involved the Care Link® RMS. In these RMSs, in addition to the standard diagnostic features, the cardiac devices continuously assess other variables such as patient activity, mean heart rate, and heart rate variability. Intra-thoracic impedance, a proxy measure for lung fluid overload, was also measured in the Care Link® studies. The overall diagnostic performance of these measures cannot be evaluated, as the information was not reported for patients who did not experience intra-thoracic impedance threshold crossings or did not undergo interventions. The trial results involved descriptive information on transmissions and alerts in patients experiencing high morbidity and hospitalization in the short study periods.
3. Comparative Effectiveness of Remote Monitoring Systems for CIEDs
Seven RCTs were identified evaluating RMSs for CIEDs: 2 were for PMs (1276 patients) and 5 were for ICD/CRT devices (3733 patients). Studies performed in the clinical setting in the United States involved both the Care Link® RMS and the Home Monitoring® RMS, whereas all studies performed in European countries involved only the Home Monitoring® RMS.
3A. Randomized Controlled Trials of Remote Monitoring Systems for Pacemakers
Two trials, both multicenter RCTs, were conducted in different countries with different RMSs and study objectives. The PREFER trial was a large trial (897 patients) performed in the United States examining the ability of Care Link®, an Internet-based remote PM interrogation system, to detect clinically actionable events (CAEs) sooner than the current in-office follow-up supplemented with transtelephonic monitoring transmissions, a limited form of remote device interrogation. The trial results are summarized below:
In the 375-day mean follow-up, 382 patients were identified with at least 1 CAE—111 patients in the control arm and 271 in the remote arm.
The event rate detected per patient for every type of CAE, except for loss of atrial capture, was higher in the remote arm than the control arm.
The median time to first detection of CAEs (4.9 vs. 6.3 months) was significantly shorter in the RMS group compared to the control group (P < 0.0001).
Additionally, only 2% (3/190) of the CAEs in the control arm were detected during a transtelephonic monitoring transmission (the rest were detected at in-office follow-ups), whereas 66% (446/676) of the CAEs were detected during remote interrogation.
The second study, the OEDIPE trial, was a smaller trial (379 patients) performed in France evaluating the ability of the Home Monitoring® RMS to shorten PM post-operative hospitalization while preserving the safety of conventional management of longer hospital stays.
Implementation and operationalization of the RMS was reported to be successful in 91% (346/379) of the patients and represented 8144 transmissions.
In the RM group 6.5% of patients failed to send messages (10 due to improper use of the transmitter, 2 with unmanageable stress). Of the 172 patients transmitting, 108 patients sent a total of 167 warnings during the trial, with a greater proportion of warnings being attributed to medical rather than technical causes.
Forty percent had no warning message transmission and among these, 6 patients experienced a major adverse event and 1 patient experienced a non-major adverse event. Of the 6 patients having a major adverse event, 5 contacted their physician.
The mean medical reaction time was faster in the RM group (6.5 ± 7.6 days vs. 11.4 ± 11.6 days).
The mean duration of hospitalization was significantly shorter (P < 0.001) for the RM group than the control group (3.2 ± 3.2 days vs. 4.8 ± 3.7 days).
Quality of life estimates by the SF-36 questionnaire were similar for the 2 groups at 1-month follow-up.
3B. Randomized Controlled Trials Evaluating Remote Monitoring Systems for ICD or CRT Devices
The 5 studies evaluating the impact of RMSs with ICD/CRT devices were conducted in the United States and in European countries and involved 2 RMSs—Care Link® and Home Monitoring ®. The objectives of the trials varied and 3 of the trials were smaller pilot investigations.
The first of the smaller studies (151 patients) evaluated patient satisfaction, achievement of patient outcomes, and the cost-effectiveness of the Care Link® RMS compared to quarterly in-office device interrogations with 1-year follow-up.
Individual outcomes such as hospitalizations, emergency department visits, and unscheduled clinic visits were not significantly different between the study groups.
Except for a significantly higher detection of atrial fibrillation in the RM group, data on ICD detection and therapy were similar in the study groups.
Health-related quality of life evaluated by the EuroQoL at 6-month or 12-month follow-up was not different between study groups.
Patients were more satisfied with their ICD care in the clinic follow-up group than in the remote follow-up group at 6-month follow-up, but were equally satisfied at 12- month follow-up.
The second small pilot trial (20 patients) examined the impact of RM follow-up with the House Call 11® system on work schedules and cost savings in patients randomized to 2 study arms varying in the degree of remote follow-up.
The total time including device interrogation, transmission time, data analysis, and physician time required was significantly shorter for the RM follow-up group.
The in-clinic waiting time was eliminated for patients in the RM follow-up group.
The physician talk time was significantly reduced in the RM follow-up group (P < 0.05).
The time for the actual device interrogation did not differ in the study groups.
The third small trial (115 patients) examined the impact of RM with the Home Monitoring® system compared to scheduled trimonthly in-clinic visits on the number of unplanned visits, total costs, health-related quality of life (SF-36), and overall mortality.
There was a 63.2% reduction in in-office visits in the RM group.
Hospitalizations or overall mortality (values not stated) were not significantly different between the study groups.
Patient-induced visits were higher in the RM group than the in-clinic follow-up group.
The TRUST Trial
The TRUST trial was a large multicenter RCT conducted at 102 centers in the United States involving the Home Monitoring® RMS for ICD devices for 1450 patients. The primary objectives of the trial were to determine if remote follow-up could be safely substituted for in-office clinic follow-up (3 in-office visits replaced) and still enable earlier physician detection of clinically actionable events.
Adherence to the protocol follow-up schedule was significantly higher in the RM group than the in-office follow-up group (93.5% vs. 88.7%, P < 0.001).
Actionability of trimonthly scheduled checks was low (6.6%) in both study groups. Overall, actionable causes were reprogramming (76.2%), medication changes (24.8%), and lead/system revisions (4%), and these were not different between the 2 study groups.
The overall mean number of in-clinic and hospital visits was significantly lower in the RM group than the in-office follow-up group (2.1 per patient-year vs. 3.8 per patient-year, P < 0.001), representing a 45% visit reduction at 12 months.
The median time from onset of first arrhythmia to physician evaluation was significantly shorter (P < 0.001) in the RM group than in the in-office follow-up group for all arrhythmias (1 day vs. 35.5 days).
The median time to detect clinically asymptomatic arrhythmia events—atrial fibrillation (AF), ventricular fibrillation (VF), ventricular tachycardia (VT), and supra-ventricular tachycardia (SVT)—was also significantly shorter (P < 0.001) in the RM group compared to the in-office follow-up group (1 day vs. 41.5 days) and was significantly quicker for each of the clinical arrhythmia events—AF (5.5 days vs. 40 days), VT (1 day vs. 28 days), VF (1 day vs. 36 days), and SVT (2 days vs. 39 days).
System-related problems occurred infrequently in both groups—in 1.5% of patients (14/908) in the RM group and in 0.7% of patients (3/432) in the in-office follow-up group.
The overall adverse event rate over 12 months was not significantly different between the 2 groups and individual adverse events were also not significantly different between the RM group and the in-office follow-up group: death (3.4% vs. 4.9%), stroke (0.3% vs. 1.2%), and surgical intervention (6.6% vs. 4.9%), respectively.
The 12-month cumulative survival was 96.4% (95% confidence interval [CI], 95.5%–97.6%) in the RM group and 94.2% (95% confidence interval [CI], 91.8%–96.6%) in the in-office follow-up group, and was not significantly different between the 2 groups (P = 0.174).
The CONNECT trial, another major multicenter RCT, involved the Care Link® RMS for ICD/CRT devices in a15-month follow-up study of 1,997 patients at 133 sites in the United States. The primary objective of the trial was to determine whether automatically transmitted physician alerts decreased the time from the occurrence of clinically relevant events to medical decisions. The trial results are summarized below:
Of the 575 clinical alerts sent in the study, 246 did not trigger an automatic physician alert. Transmission failures were related to technical issues such as the alert not being programmed or not being reset, and/or a variety of patient factors such as not being at home and the monitor not being plugged in or set up.
The overall mean time from the clinically relevant event to the clinical decision was significantly shorter (P < 0.001) by 17.4 days in the remote follow-up group (4.6 days for 172 patients) than the in-office follow-up group (22 days for 145 patients).
– The median time to a clinical decision was shorter in the remote follow-up group than in the in-office follow-up group for an AT/AF burden greater than or equal to 12 hours (3 days vs. 24 days) and a fast VF rate greater than or equal to 120 beats per minute (4 days vs. 23 days).
Although infrequent, similar low numbers of events involving low battery and VF detection/therapy turned off were noted in both groups. More alerts, however, were noted for out-of-range lead impedance in the RM group (18 vs. 6 patients), and the time to detect these critical events was significantly shorter in the RM group (same day vs. 17 days).
Total in-office clinic visits were reduced by 38% from 6.27 visits per patient-year in the in-office follow-up group to 3.29 visits per patient-year in the remote follow-up group.
Health care utilization visits (N = 6,227) that included cardiovascular-related hospitalization, emergency department visits, and unscheduled clinic visits were not significantly higher in the remote follow-up group.
The overall mean length of hospitalization was significantly shorter (P = 0.002) for those in the remote follow-up group (3.3 days vs. 4.0 days) and was shorter both for patients with ICD (3.0 days vs. 3.6 days) and CRT (3.8 days vs. 4.7 days) implants.
The mortality rate between the study arms was not significantly different between the follow-up groups for the ICDs (P = 0.31) or the CRT devices with defribillator (P = 0.46).
There is limited clinical trial information on the effectiveness of RMSs for PMs. However, for RMSs for ICD devices, multiple cohort studies and 2 large multicenter RCTs demonstrated feasibility and significant reductions in in-office clinic follow-ups with RMSs in the first year post implantation. The detection rates of clinically significant events (and asymptomatic events) were higher, and the time to a clinical decision for these events was significantly shorter, in the remote follow-up groups than in the in-office follow-up groups. The earlier detection of clinical events in the remote follow-up groups, however, was not associated with lower morbidity or mortality rates in the 1-year follow-up. The substitution of almost all the first year in-office clinic follow-ups with RM was also not associated with an increased health care utilization such as emergency department visits or hospitalizations.
The follow-up in the trials was generally short-term, up to 1 year, and was a more limited assessment of potential longer term device/lead integrity complications or issues. None of the studies compared the different RMSs, particularly the different RMSs involving patient-scheduled transmissions or automatic transmissions. Patients’ acceptance of and satisfaction with RM were reported to be high, but the impact of RM on patients’ health-related quality of life, particularly the psychological aspects, was not evaluated thoroughly. Patients who are not technologically competent, having hearing or other physical/mental impairments, were identified as potentially disadvantaged with remote surveillance. Cohort studies consistently identified subgroups of patients who preferred in-office follow-up. The evaluation of costs and workflow impact to the health care system were evaluated in European or American clinical settings, and only in a limited way.
Internet-based device-assisted RMSs involve a new approach to monitoring patients, their disease progression, and their CIEDs. Remote monitoring also has the potential to improve the current postmarket surveillance systems of evolving CIEDs and their ongoing hardware and software modifications. At this point, however, there is insufficient information to evaluate the overall impact to the health care system, although the time saving and convenience to patients and physicians associated with a substitution of in-office follow-up by RM is more certain. The broader issues surrounding infrastructure, impacts on existing clinical care systems, and regulatory concerns need to be considered for the implementation of Internet-based RMSs in jurisdictions involving different clinical practices.
PMCID: PMC3377571  PMID: 23074419
8.  Primary Health Care Consumers’ Acceptance, Trust and Gender Preferences towards Omani Doctors 
Oman Medical Journal  2007;22(3):51-56.
The percentage of Omani physicians from total number of physicians working in the Sultanate tripled from 9% in 1999 to 27% in 2006 and is expected to increase to 50% by 2010. The study aimed to asses community attitudes towards Omani doctors and to investigate the different socio-demographic variables related to these attitudes.
It was done in two selected Primary Health Care (PHC) facilities by simple random technique in Batinah region. Face-to-face interview was made on 305 randomly selected samples of PHC customers by trained researchers from Sultan Qaboos University (SQU). Omani Doctors Acceptance Scale (ODAS) was adapted and used to assess participants acceptance of the communication skills of the Omani doctor, care to the patient, absence of language barrier, competence level, preference to be seen by doctor from the same sex, embarrassment from seeing an Omani doctor, qualification, experience, knowledge and skills of the Omani experience of the Omani doctor, and trust on the Omani doctor. Chi squared tests of significance was used in analysis.
Males reported more satisfaction about communication skills of the Omani doctors, whereas female respondents reported higher likelihood of being embarrassed from the latter. Elder age cohort, those reported ever treated by an Omani doctor, married respondents, and those of lower level of education were more likely to report higher level of acceptance than others. Those aged 26-40 and those above 40 years of age were 2.41 and 3.41 times higher than the youngest age cohort respectively. Similarly, older age cohort reported having more trust than the middle age respondents relatively to the youngest age group.
The current study showed an accepted level of acceptance to Omani doctors. Looking for crucial issues in patient-doctor relationships as acceptance, satisfaction, trust, gender preference especially for PHC doctors ensure the continuity of care.
PMCID: PMC3294151  PMID: 22400094
9.  A Preliminary Mixed-Method Investigation of Trust and Hidden Signals in Medical Consultations 
PLoS ONE  2014;9(3):e90941.
Several factors influence patients' trust, and trust influences the doctor-patient relationship. Recent literature has investigated the quality of the personal relationship and its dynamics by considering the role of communication and the elements that influence trust giving in the frame of general practitioner (GP) consultations.
We analysed certain aspects of the interaction between patients and GPs to understand trust formation and maintenance by focusing on communication channels. The impact of socio-demographic variables in trust relationships was also evaluated.
A cross-sectional design using concurrent mixed qualitative and quantitative research methods was employed. One hundred adults were involved in a semi-structured interview composed of both qualitative and quantitative items for descriptive and exploratory purposes. The study was conducted in six community-based departments adjacent to primary care clinics in Trento, Italy.
The findings revealed that patients trusted their GP to a high extent by relying on simple signals that were based on the quality of the one-to-one communication and on behavioural and relational patterns. Patients inferred the ability of their GP by adopting simple heuristics based mainly on the so-called social “honest signals” rather than on content-dependent features. Furthermore, socio-demographic variables affected trust: less literate and elderly people tended to trust more.
This study is unique in attempting to explore the role of simple signals in trust relationships within medical consultation: people shape trust and give meaning to their relationships through a powerful channel of communication that orbits not around words but around social relations. The findings have implications for both clinicians and researchers. For doctors, these results suggest a way of thinking about encounters with patients. For researchers, the findings underline the importance of analysing some new key factors around trust for future investigations in medical practice and education.
PMCID: PMC3949702  PMID: 24618683
10.  Dimensions and Determinants of Trust in Health Care in Resource Poor Settings – A Qualitative Exploration 
PLoS ONE  2013;8(7):e69170.
Trust in health care has been intensely researched in resource rich settings. Some studies in resource poor settings suggest that the dimensions and determinants of trust are likely to be different.
This study was done as a qualitative exploration of the dimensions and determinants of trust in health care in Tamil Nadu, a state in south India to assess the differences from dimensions and determinants in resource rich settings.
The participants included people belonging to marginalized communities with poor access to health care services and living in conditions of resource deprivation. A total of thirty five in depth interviews were conducted. The interviews were summarized and transcribed and data were analyzed following thematic analysis and grounded theory approach.
The key dimensions of trust in health care identified during the interviews were perceived competence, assurance of treatment irrespective of ability to pay or at any time of the day, patients’ willingness to accept drawbacks in health care, loyalty to the physician and respect for the physician. Comfort with the physician and health facility, personal involvement of the doctor with the patient, behavior and approach of doctor, economic factors, and health awareness were identified as factors determining the levels of trust in health care.
The dimensions and determinants of trust in health care in resource poor settings are different from that in resource rich settings. There is a need to develop scales to measure trust in health care in resource poor settings using these specific dimensions and determinants.
PMCID: PMC3712948  PMID: 23874904
11.  Does Family Medicine training in Thailand affect patient satisfaction with primary care doctors? 
BMC Family Practice  2007;8:14.
Recent national healthcare reforms in Thailand aim to transfer primary care to family physicians, away from more expensive specialists. As Family Medicine has yet to be established as a separate discipline in Thailand, newly trained family physicians work alongside untrained general doctors in primary care. While it has been shown that Family Medicine training programs in Thailand can increase the quality of referrals from primary care doctors to specialists, information is lacking about whether such training affects the quality of patient care. In the Department of Family Medicine at Ramathibodi Hospital, trained family physicians work with residents and general doctors. Although this situation is not typical within Thailand, it offers us the opportunity to look for variations in the levels of satisfaction reported by patients treated by different types of primary care doctor.
During a two-week period in December 2005, 2,600 questionnaires (GPAQ) were given to patients visiting the Department of Family Medicine at Ramathibodi Hospital. Patients were given the choice of whether or not they wanted to participate in the study. A cross-sectional analysis was performed on the completed questionnaires. Mean GPAQ scores were calculated for each dimension and scored out of 100. Student t-tests, ANOVA with F-test statistic and multiple comparisons by Scheffe were used to compare the perceived characteristics of the different groups of doctors. Five dimensions were measured ranging from access to care, continuity of care, communication skills, enablement (the patient's knowledge of a self-care plan after the consultation) and overall satisfaction.
The response rate was 70%. There were significant differences in mean GPAQ scores among faculty family physicians, residents and general doctors. For continuity of care, patients gave higher scores for faculty family physicians (67.87) compared to residents (64.57) and general doctors (62.51). For communication skills, patients gave the highest GPAQ scores to faculty family physicians (69.77) and family medicine residents (69.79). For enablement, faculty family physicians received the highest score (82.44) followed by family medicine residents (80.75) and general doctors (76.29).
Faculty family physicians scored higher for continuity of care when compared with general doctors and residents. General doctors had lower GPAQ scores for communication skills and enablement when compared to faculty family physicians and residents. Faculty family physicians had the highest GPAQ scores in many dimensions of family practice skills, followed by residents and general doctors.
PMCID: PMC1852109  PMID: 17394639
12.  A qualitative study to understand guideline-discordant use of imaging to stage incident prostate cancer 
Implementation Science : IS  2016;11(1):118.
Approximately half of veterans with low-risk prostate cancer receive guideline-discordant imaging. Our objective was to identify and describe (1) physician knowledge, attitudes, and practices related to the use of imaging to stage prostate cancer, (2) patient attitudes and behaviors related to use of imaging, and (3) to compare responses across three VA medical centers (VAMCs).
A qualitative approach was used to explore patient and provider knowledge and behaviors relating to the use of imaging. We conducted 39 semi-structured interviews total—including 22 interviews with patients with newly diagnosed with prostate cancer and 17 interviews with physicians caring for them—between September 2014 and July 2015 at three VAMCs representing a spectrum of inappropriate imaging rates. After core theoretical concepts were identified, the Theoretical Domains Framework (TDF) was selected to explore linkages between themes within the dataset and existing domains within the framework. Interviews were audio-recorded, transcribed verbatim, and then coded and analyzed using Nvivo software.
Themes from patient interviews were categorized within four TDF domains. Patients reported little interest in staging as compared to disease treatment (goals), and many could not remember if they had imaging at all (knowledge). Patients tended to trust their doctor to make decisions about appropriate tests (beliefs about capabilities). Some patients expressed a minor concern for radiation exposure, but anxiety about cancer outcomes outweighed these fears (emotion). Themes from physician interviews were categorized within five TDF domains. Most physicians self-reported that they know and trust imaging guidelines (knowledge) yet some were still likely to follow their own intuition, whether due to clinical suspicion or years of experience (beliefs about capabilities). Additionally, physicians reported that medico-legal concerns, fear of missing associated diagnoses (beliefs about consequences), influence from colleagues who image frequently (social influences), and the facility where they practice influences rates of imaging (environmental context).
Interviews with patients and physicians suggest that physicians are the primary (and in some cases only) decision-makers regarding staging imaging for prostate cancer. This finding suggests a physician-targeted intervention may be the most effective strategy to improve guideline-concordant prostate cancer imaging.
PMCID: PMC5010696  PMID: 27590603
Theoretical domains framework; Prostate cancer; Imaging; Guideline; Qualitative; Semi-structured interviews
13.  African Migrant Patients’ Trust in Chinese Physicians: A Social Ecological Approach to Understanding Patient-Physician Trust 
PLoS ONE  2015;10(5):e0123255.
Patient trust in physicians is a critical determinant of health seeking behaviors, medication adherence, and health outcomes. A crisis of interpersonal trust exists in China, extending throughout multiple social spheres, including the healthcare system. At the same time, with increased migration from Africa to China in the last two decades, Chinese physicians must establish mutual trust with an increasingly diverse patient population. We undertook a qualitative study to identify factors affecting African migrants’ trust in Chinese physicians and to identify potential mechanisms for promoting trust.
Methods / Principal Findings
We conducted semi-structured, in-depth interviews with 40 African migrants in Guangzhou, China. A modified version of the social ecological model was used as a theoretical framework. At the patient-physician level, interpersonal treatment, technical competence, perceived commitment and motive, and language concordance were associated with enhanced trust. At the health system level, two primary factors influenced African migrants’ trust in their physicians: the fee-for-service payment system and lack of continuity with any one physician. Patients’ social networks and the broader socio-cultural context of interactions between African migrants and Chinese locals also influenced patients’ trust of their physicians.
These findings demonstrate the importance of factors beyond the immediate patient-physician interaction and suggest opportunities to promote trust through health system interventions.
PMCID: PMC4428824  PMID: 25965064
14.  A qualitative study of patient (dis)trust in public and private hospitals: the importance of choice and pragmatic acceptance for trust considerations in South Australia 
This paper explores the nature and reasoning for (dis)trust in Australian public and private hospitals. Patient trust increases uptake of, engagement with and optimal outcomes from healthcare services and is therefore central to health practice, policy and planning.
A qualitative study in South Australia, including 36 in-depth interviews (18 from public and 18 from private hospitals).
‘Private patients’ made active choices about both their hospital and doctor, playing the role of the ‘consumer’, where trust and choice went hand in hand. The reputation of the doctor and hospital were key drivers of trust, under the assumption that a better reputation equates with higher quality care. However, making a choice to trust a doctor led to personal responsibility and the additional requirement for self-trust. ‘Public patients’ described having no choice in their hospital or doctor. They recognised ‘problems’ in the public healthcare system but accepted and even excused these as ‘part of the system’. In order to justify their trust, they argued that doctors in public hospitals tried to do their best in difficult circumstances, thereby deserving of trust. This ‘resigned trust’ may stem from a lack of alternatives for free health care and thus a dependence on the system.
These two contrasting models of trust within the same locality point to the way different configurations of healthcare systems, hospital experiences, insurance coverage and related forms of ‘choice’ combine to shape different formats of trust, as patients act to manage their vulnerability within these contexts.
PMCID: PMC4518638  PMID: 26223973
Trust; Choice; Public hospitals; Private hospitals; Qualitative; Australia
15.  “Doctor, please tell me it’s nothing serious”: an exploration of patients’ worrying and reassuring cognitions using stimulated recall interviews 
BMC Family Practice  2014;15:73.
Many patients who consult their GP are worried about their health, but there is little empirical data on strategies for effective reassurance. To gain a better understanding of mechanisms for effective patient reassurance, we explored cognitions underlying patients’ worries, cognitions underlying reassurance and factors supporting patients’ reassuring cognitions.
In a qualitative study, we conducted stimulated recall interviews with 21 patients of 12 different GPs shortly after their consultation. We selected consultations in which the GPs aimed to reassure worried patients and used their videotaped consultation as a stimulus for the interview. The interviews were analysed with thematic coding and by writing interpretive summaries.
Patients expressed four different core cognitions underlying their concerns: ‘I have a serious illness’, ‘my health problem will have adverse physical effects’, ‘my treatment will have adverse effects’ and ‘my health problem will negatively impact my life’. Patients mentioned a range of person-specific and context-specific cognitions as reasons for these core cognitions. Patients described five core reassuring cognitions: ‘I trust my doctor’s expertise’, ‘I have a trusting and supporting relationship with my doctor’, ‘I do not have a serious disease’, ‘my health problem is harmless’ and ‘my health problem will disappear.’ Factors expressed as reasons for these reassuring cognitions were GPs’ actions during the consultation as well as patients’ pre-existing cognitions about their GP, the doctor-patient relationship and previous events. Patients’ worrying cognitions were counterbalanced by specific reassuring cognitions, i.e. worrying and reassuring cognitions seemed to be interrelated.
Patients described a wide range of worrying cognitions, some of which were not expressed during the consultation. Gaining a thorough understanding of the specific cognitions and tailoring reassuring strategies to them should be an effective way of achieving reassurance. The identified reassuring cognitions can guide doctors in applying these strategies in their daily practice.
PMCID: PMC4008437  PMID: 24762333
16.  Patient-Physician Racial/Ethnic Concordance and Blood Pressure Control: The Role of Trust and Medication Adherence 
Ethnicity & health  2013;19(5):565-578.
To examine associations between racial/ethnic concordance and BP control, and determine if patient trust and medication adherence mediate these associations.
Cross-sectional study of 723 hypertensive African American and White patients receiving care from 205 White and African American providers at 119 primary care clinics, from 2001–2005. Racial/ethnic concordance was characterized as dyads where both the patient and physician were of the same race/ethnicity; discordance occurred in dyads where the patient was African American and the physician was White. Patient perceptions of trust and medication adherence were assessed with self-report measures. Blood pressure readings were abstracted from patients’ medical charts using standardized procedures.
Six hundred thirty seven patients were in race/ethnic-concordant relationships; 86 were in race/ethnic-discordant relationships. Concordance had no association with blood pressure control. White patients in race/ethnic-concordant relationships were more likely to report better adherence than African American patients in race/ethnic-discordant relationships (OR: 1.27 95% CI: 1.01, 1.61 p = 0.04). Little difference in adherence was found for African American patients in race/ethnic-concordant vs. discordant relationships. Increasing trust was associated with significantly better adherence (OR: 1.17 95% CI: 1.04, 1.31, p < 0.01) and a trend toward better BP control among all patients (OR: 1.26, 95% CI: 0.97, 1.63, p=0.07).
Patient trust may influence medication adherence and BP control regardless of patient-physician racial/ethnic composition.
PMCID: PMC4031314  PMID: 24266617
blood pressure control; trust; medication adherence; racial/ethnic concordance
17.  The patient–doctor relationship: a synthesis of the qualitative literature on patients' perspectives 
The British Journal of General Practice  2009;59(561):e116-e133.
The patient–doctor relationship is an important but poorly defined topic. In order to comprehensively assess its significance for patient care, a clearer understanding of the concept is required.
To derive a conceptual framework of the factors that define patient–doctor relationships from the perspective of patients.
Design of study
Systematic review and thematic synthesis of qualitative studies.
Medline, EMBASE, PsychINFO and Web of Science databases were searched. Studies were screened for relevance and appraised for quality. The findings were synthesised using a thematic approach.
From 1985 abstracts, 11 studies from four countries were included in the final synthesis. They examined the patient–doctor relationship generally (n = 3), or in terms of loyalty (n = 3), personal care (n = 2), trust (n = 2), and continuity (n = 1). Longitudinal care (seeing the same doctor) and consultation experiences (patients' encounters with the doctor) were found to be the main processes by which patient–doctor relationships are promoted. The resulting depth of patient–doctor relationship comprises four main elements: knowledge, trust, loyalty, and regard. These elements have doctor and patient aspects to them, which may be reciprocally related.
A framework is proposed that distinguishes between dynamic factors that develop or maintain the relationship, and characteristics that constitute an ongoing depth of relationship. Having identified the different elements involved, future research should examine for associations between longitudinal care, consultation experiences, and depth of patient–doctor relationship, and, in turn, their significance for patient care.
PMCID: PMC2662123  PMID: 19341547
communication; continuity of patient care; physician–patient relations; qualitative research
18.  'It gives you an understanding you can't get from any book.' The relationship between medical students' and doctors' personal illness experiences and their performance: a qualitative and quantitative study 
Anecdotes abound about doctors' personal illness experiences and the effect they have on their empathy and care of patients. We formally investigated the relationship between doctors' and medical students' personal illness experiences, their examination results, preparedness for clinical practice, learning and professional attitudes and behaviour towards patients.
Newly-qualified UK doctors in 2005 (n = 2062/4784), and two cohorts of students at one London medical school (n = 640/749) participated in the quantitative arm of the study. 37 Consultants, 1 Specialist Registrar, 2 Clinical Skills Tutors and 25 newly-qualified doctors participated in the qualitative arm. Newly-qualified doctors and medical students reported their personal illness experiences in a questionnaire. Doctors' experiences were correlated with self-reported preparedness for their new clinical jobs. Students' experiences were correlated with their examination results, and self-reported anxiety and depression. Interviews with clinical teachers, newly-qualified doctors and senior doctors qualitatively investigated how personal illness experiences affect learning, professional attitudes, and behaviour.
85.5% of newly-qualified doctors and 54.4% of medical students reported personal illness experiences. Newly-qualified doctors who had been ill felt less prepared for starting work (p < 0.001), but those who had only experienced illness in a relative or friend felt more prepared (p = 0.02). Clinical medical students who had been ill were more anxious (p = 0.01) and had lower examination scores (p = 0.006). Doctors felt their personal illness experiences helped them empathise and communicate with patients. Medical students with more life experience were perceived as more mature, empathetic, and better learners; but illness at medical school was recognised to impede learning.
The majority of the medical students and newly qualified doctors we studied reported personal illness experiences, and these experiences were associated with lower undergraduate examination results, higher anxiety, and lower preparedness. However reflection on such experiences may have improved professional attitudes such as empathy and compassion for patients. Future research is warranted in this area.
PMCID: PMC2211477  PMID: 18053231
19.  The compatibility of prescribing guidelines and the doctor-patient partnership: a primary care mixed-methods study 
The British Journal of General Practice  2012;62(597):e275-e281.
UK policy expects health professionals to involve patients in decisions about their care (including medicines use) and, at the same time, to follow prescribing guidelines. The compatibility of these approaches is unclear.
To explore the relationship between prescribing guidelines and patient-partnership by exploring the attitudes of patients, GPs and primary care trust (PCT) prescribing advisors.
Design and setting
A mixed-methods study using qualitative, semi-structured interviews followed by a quantitative, questionnaire survey in primary care in Northern England.
Interviews were conducted with 14 patients taking a statin or a proton pump inhibitor, eight GPs and two prescribing advisors. A multi-variate sampling strategy was used. Qualitative findings were analysed using framework analysis. Questionnaires based on themes derived from the interviews were distributed to 533 patients and 305 GPs of whom 286 (54%) and 142 (43%) responded.
Areas of tension between guidelines and patient partnership were identified, including potential damage to trust in the doctor and reduced patient choice, through the introduction of the policy maker as a third stakeholder in prescribing decisions. Other areas of tension related to applying single condition guidelines to patients with multiple illnesses, competition for doctors' time and the perception of cost containment. Many GPs coped with these tensions by adopting a flexible approach or prioritising the doctor–patient relationship over guidelines.
Rigidly applied guidelines can limit patient choice and may damage the doctor–patient relationship. GPs need flexibility in order to optimise the implementation of prescribing guidelines, while responding to individuals' needs and preferences.
PMCID: PMC3310034  PMID: 22520915
decision making; evidence-based practice; patient compliance; physician-patient relations; professional practice
20.  The Development of Online Doctor Reviews in China: An Analysis of the Largest Online Doctor Review Website in China 
Since the time of Web 2.0, more and more consumers have used online doctor reviews to rate their doctors or to look for a doctor. This phenomenon has received health care researchers’ attention worldwide, and many studies have been conducted on online doctor reviews in the United States and Europe. But no study has yet been done in China. Also, in China, without a mature primary care physician recommendation system, more and more Chinese consumers seek online doctor reviews to look for a good doctor for their health care concerns.
This study sought to examine the online doctor review practice in China, including addressing the following questions: (1) How many doctors and specialty areas are available for online review? (2) How many online reviews are there on those doctors? (3) What specialty area doctors are more likely to be reviewed or receive more reviews? (4) Are those reviews positive or negative?
This study explores an empirical dataset from Good Doctor website,—the earliest and largest online doctor review and online health care community website in China—from 2006 to 2014, to examine the stated research questions by using descriptive statistics, binary logistic regression, and multivariate linear regression.
The dataset from the Good Doctor website contained 314,624 doctors across China and among them, 112,873 doctors received 731,543 quantitative reviews and 772,979 qualitative reviews as of April 11, 2014. On average, 37% of the doctors had been reviewed on the Good Doctor website. Gynecology-obstetrics-pediatrics doctors were most likely to be reviewed, with an odds ratio (OR) of 1.497 (95% CI 1.461-1.535), and internal medicine doctors were less likely to be reviewed, with an OR of 0.94 (95% CI 0.921-0.960), relative to the combined small specialty areas. Both traditional Chinese medicine doctors and surgeons were more likely to be reviewed than the combined small specialty areas, with an OR of 1.483 (95% CI 1.442-1.525) and an OR of 1.366 (95% CI 1.337-1.395), respectively. Quantitatively, traditional Chinese medicine doctors (P<.001) and gynecology-obstetrics-pediatrics doctors (P<.001) received more reviews than the combined small specialty areas. But internal medicine doctors received fewer reviews than the combined small specialty areas (P<.001). Also, the majority of quantitative reviews were positive—about 88% were positive for the doctors' treatment effect measure and 91% were positive for the bedside manner measure. This was the case for the four major specialty areas, which had the most number of doctors—internal medicine, gynecology-obstetrics-pediatrics, surgery, and traditional Chinese medicine.
Like consumers in the United States and Europe, Chinese consumers have started to use online doctor reviews. Similar to previous research on other countries’ online doctor reviews, the online reviews in China covered almost every medical specialty, and most of the reviews were positive even though all of the reviewing procedures and the final available information were anonymous. The average number of reviews per rated doctor received in this dataset was 6, which was higher than that for doctors in the United States or Germany, probably because this dataset covered a longer time period than did the US or German dataset. But this number is still very small compared to any doctor’s real patient population, and it cannot represent the reality of that population. Also, since all the data used for analysis were from one single website, the data might be biased and might not be a representative national sample of China.
PMCID: PMC4526894  PMID: 26032933
online doctor reviews; China health system; quantitative review; qualitative review; patient empowerment; physician quality
21.  Trust in Physicians Among Rural Medicaid-Enrolled Smokers 
Associations have been found between trusting patient-physician relationships and use of preventive care and a greater adherence to prescribed care. The objectives of this study were to assess the level of trust rural Medicaid smokers have in their physicians and whether trust was related to patient characteristics or physician behavior.
This was a cross-sectional study of smokers who were enrolled in a tobacco dependence treatment program. Participants were rural Medicaid-enrolled adults, age 18 and older, who were current smokers. Participants were enrolled from 8 primary care clinics as they came in for an appointment with their physician. The Trust in Physician Scale was completed at the baseline visit. One week later, an interview was conducted with the smoker to determine whether the physician provided tobacco dependence treatment counseling at the visit. Mixed models were used to model the relationship between trust and participant characteristics and physician behaviors.
Medicaid smokers in this study exhibited a high level of trust in their health care provider, as levels were similar to those reported in the general population of patients. Trust was significantly higher among individuals with better self-reported health.
Rural Medicaid smokers appeared to have similar levels of trust in their physician as other patients. Future research should explore the role trust plays in shaping interactions between underserved populations and physicians within the context of smoking cessation counseling.
PMCID: PMC3994402  PMID: 24689546
epidemiology; Medicaid; observational data; rural; tobacco dependence treatment
22.  Patient Trust in Physicians: Empirical Evidence from Shanghai, China 
Chinese Medical Journal  2016;129(7):814-818.
Patient trust in physicians, which can be considered a collective good, is necessary for an effective health care system. However, there is a widespread concern that patient trust in physicians is declining under various threats to the physician–patient relationship worldwide. This article aimed to assess patient trust in physicians through a quantitative study in Shanghai, China, and to provide appropriate suggestions for improving the trust in China.
The data from a survey conducted in Zhongshan Hospital and Shanghai Tenth People's Hospital, which are two tertiary public hospitals in Shanghai, were used in this study. Patient trust in physicians was the dependent variable. Furthermore, a 10-item scale was used to precisely describe the dependent variable. The demographic characteristics were independent variables of trust in physicians. Binomial logistic regression was employed to analyze the factors associated with the dependent variable, which was divided into two categories on the basis of the responses (1: Strongly agree or agree and 0: Strongly disagree, disagree, or neutral).
This study found that 67% of patients trusted or strongly trusted physicians. The mean score of patient trust in physicians was 35.4 from a total score of 50. Furthermore, patient trust in physicians was significantly correlated with the age, education level, annual income, and health insurance coverage of the patients.
Patient trust in physicians in Shanghai, China is higher than previously reported. Furthermore, the most crucial reason for patient distrust in physicians is the information asymmetry between patients and physicians, which is a natural property of the physician–patient relationship, rather than the so-called for-profit characteristic of physicians or patients’ excessive expectations.
PMCID: PMC4819302  PMID: 26996477
Patient Trust; Physician–Patient Relationship; Public Hospital
23.  Developing an efficient scheduling template of a chemotherapy treatment unit 
The Australasian Medical Journal  2011;4(10):575-588.
This study was undertaken to improve the performance of a Chemotherapy Treatment Unit by increasing the throughput and reducing the average patient’s waiting time. In order to achieve this objective, a scheduling template has been built. The scheduling template is a simple tool that can be used to schedule patients' arrival to the clinic. A simulation model of this system was built and several scenarios, that target match the arrival pattern of the patients and resources availability, were designed and evaluated. After performing detailed analysis, one scenario provide the best system’s performance. A scheduling template has been developed based on this scenario. After implementing the new scheduling template, 22.5% more patients can be served.
CancerCare Manitoba is a provincially mandated cancer care agency. It is dedicated to provide quality care to those who have been diagnosed and are living with cancer. MacCharles Chemotherapy unit is specially built to provide chemotherapy treatment to the cancer patients of Winnipeg. In order to maintain an excellent service, it tries to ensure that patients get their treatment in a timely manner. It is challenging to maintain that goal because of the lack of a proper roster, the workload distribution and inefficient resource allotment. In order to maintain the satisfaction of the patients and the healthcare providers, by serving the maximum number of patients in a timely manner, it is necessary to develop an efficient scheduling template that matches the required demand with the availability of resources. This goal can be reached using simulation modelling. Simulation has proven to be an excellent modelling tool. It can be defined as building computer models that represent real world or hypothetical systems, and hence experimenting with these models to study system behaviour under different scenarios.1, 2
A study was undertaken at the Children's Hospital of Eastern Ontario to identify the issues behind the long waiting time of a emergency room.3 A 20-­‐day field observation revealed that the availability of the staff physician and interaction affects the patient wait time. Jyväskylä et al.4 used simulation to test different process scenarios, allocate resources and perform activity-­‐based cost analysis in the Emergency Department (ED) at the Central Hospital. The simulation also supported the study of a new operational method, named "triage-team" method without interrupting the main system. The proposed triage team method categorises the entire patient according to the urgency to see the doctor and allows the patient to complete the necessary test before being seen by the doctor for the first time. The simulation study showed that it will decrease the throughput time of the patient and reduce the utilisation of the specialist and enable the ordering all the tests the patient needs right after arrival, thus quickening the referral to treatment.
Santibáñez et al.5 developed a discrete event simulation model of British Columbia Cancer Agency"s ambulatory care unit which was used to study the impact of scenarios considering different operational factors (delay in starting clinic), appointment schedule (appointment order, appointment adjustment, add-­‐ons to the schedule) and resource allocation. It was found that the best outcomes were obtained when not one but multiple changes were implemented simultaneously. Sepúlveda et al.6 studied the M. D. Anderson Cancer Centre Orlando, which is a cancer treatment facility and built a simulation model to analyse and improve flow process and increase capacity in the main facility. Different scenarios were considered like, transferring laboratory and pharmacy areas, adding an extra blood draw room and applying different scheduling techniques of patients. The study shows that by increasing the number of short-­‐term (four hours or less) patients in the morning could increase chair utilisation.
Discrete event simulation also helps improve a service where staff are ignorant about the behaviour of the system as a whole; which can also be described as a real professional system. Niranjon et al.7 used simulation successfully where they had to face such constraints and lack of accessible data. Carlos et al. 8 used Total quality management and simulation – animation to improve the quality of the emergency room. Simulation was used to cover the key point of the emergency room and animation was used to indicate the areas of opportunity required. This study revealed that a long waiting time, overload personnel and increasing withdrawal rate of patients are caused by the lack of capacity in the emergency room.
Baesler et al.9 developed a methodology for a cancer treatment facility to find stochastically a global optimum point for the control variables. A simulation model generated the output using a goal programming framework for all the objectives involved in the analysis. Later a genetic algorithm was responsible for performing the search for an improved solution. The control variables that were considered in this research are number of treatment chairs, number of drawing blood nurses, laboratory personnel, and pharmacy personnel. Guo et al. 10 presented a simulation framework considering demand for appointment, patient flow logic, distribution of resources, scheduling rules followed by the scheduler. The objective of the study was to develop a scheduling rule which will ensure that 95% of all the appointment requests should be seen within one week after the request is made to increase the level of patient satisfaction and balance the schedule of each doctor to maintain a fine harmony between "busy clinic" and "quiet clinic".
Huschka et al.11 studied a healthcare system which was about to change their facility layout. In this case a simulation model study helped them to design a new healthcare practice by evaluating the change in layout before implementation. Historical data like the arrival rate of the patients, number of patients visited each day, patient flow logic, was used to build the current system model. Later, different scenarios were designed which measured the changes in the current layout and performance.
Wijewickrama et al.12 developed a simulation model to evaluate appointment schedule (AS) for second time consultations and patient appointment sequence (PSEQ) in a multi-­‐facility system. Five different appointment rule (ARULE) were considered: i) Baily; ii) 3Baily; iii) Individual (Ind); iv) two patients at a time (2AtaTime); v) Variable Interval and (V-­‐I) rule. PSEQ is based on type of patients: Appointment patients (APs) and new patients (NPs). The different PSEQ that were studied in this study were: i) first-­‐ come first-­‐serve; ii) appointment patient at the beginning of the clinic (APBEG); iii) new patient at the beginning of the clinic (NPBEG); iv) assigning appointed and new patients in an alternating manner (ALTER); v) assigning a new patient after every five-­‐appointment patients. Also patient no show (0% and 5%) and patient punctuality (PUNCT) (on-­‐time and 10 minutes early) were also considered. The study found that ALTER-­‐Ind. and ALTER5-­‐Ind. performed best on 0% NOSHOW, on-­‐time PUNCT and 5% NOSHOW, on-­‐time PUNCT situation to reduce WT and IT per patient. As NOSHOW created slack time for waiting patients, their WT tends to reduce while IT increases due to unexpected cancellation. Earliness increases congestion whichin turn increases waiting time.
Ramis et al.13 conducted a study of a Medical Imaging Center (MIC) to build a simulation model which was used to improve the patient journey through an imaging centre by reducing the wait time and making better use of the resources. The simulation model also used a Graphic User Interface (GUI) to provide the parameters of the centre, such as arrival rates, distances, processing times, resources and schedule. The simulation was used to measure the waiting time of the patients in different case scenarios. The study found that assigning a common function to the resource personnel could improve the waiting time of the patients.
The objective of this study is to develop an efficient scheduling template that maximises the number of served patients and minimises the average patient's waiting time at the given resources availability. To accomplish this objective, we will build a simulation model which mimics the working conditions of the clinic. Then we will suggest different scenarios of matching the arrival pattern of the patients with the availability of the resources. Full experiments will be performed to evaluate these scenarios. Hence, a simple and practical scheduling template will be built based on the indentified best scenario. The developed simulation model is described in section 2, which consists of a description of the treatment room, and a description of the types of patients and treatment durations. In section 3, different improvement scenarios are described and their analysis is presented in section 4. Section 5 illustrates a scheduling template based on one of the improvement scenarios. Finally, the conclusion and future direction of our work is exhibited in section 6.
Simulation Model
A simulation model represents the actual system and assists in visualising and evaluating the performance of the system under different scenarios without interrupting the actual system. Building a proper simulation model of a system consists of the following steps.
Observing the system to understand the flow of the entities, key players, availability of resources and overall generic framework.
Collecting the data on the number and type of entities, time consumed by the entities at each step of their journey, and availability of resources.
After building the simulation model it is necessary to confirm that the model is valid. This can be done by confirming that each entity flows as it is supposed to and the statistical data generated by the simulation model is similar to the collected data.
Figure 1 shows the patient flow process in the treatment room. On the patient's first appointment, the oncologist comes up with the treatment plan. The treatment time varies according to the patient’s condition, which may be 1 hour to 10 hours. Based on the type of the treatment, the physician or the clinical clerk books an available treatment chair for that time period.
On the day of the appointment, the patient will wait until the booked chair is free. When the chair is free a nurse from that station comes to the patient, verifies the name and date of birth and takes the patient to a treatment chair. Afterwards, the nurse flushes the chemotherapy drug line to the patient's body which takes about five minutes and sets up the treatment. Then the nurse leaves to serve another patient. Chemotherapy treatment lengths vary from less than an hour to 10 hour infusions. At the end of the treatment, the nurse returns, removes the line and notifies the patient about the next appointment date and time which also takes about five minutes. Most of the patients visit the clinic to take care of their PICC line (a peripherally inserted central catheter). A PICC is a line that is used to inject the patient with the chemical. This PICC line should be regularly cleaned, flushed to maintain patency and the insertion site checked for signs of infection. It takes approximately 10–15 minutes to take care of a PICC line by a nurse.
Cancer Care Manitoba provided access to the electronic scheduling system, also known as "ARIA" which is comprehensive information and image management system that aggregates patient data into a fully-­‐electronic medical chart, provided by VARIAN Medical System. This system was used to find out how many patients are booked in every clinic day. It also reveals which chair is used for how many hours. It was necessary to search a patient's history to find out how long the patient spends on which chair. Collecting the snapshot of each patient gives the complete picture of a one day clinic schedule.
The treatment room consists of the following two main limited resources:
Treatment Chairs: Chairs that are used to seat the patients during the treatment.
Nurses: Nurses are required to inject the treatment line into the patient and remove it at the end of the treatment. They also take care of the patients when they feel uncomfortable.
Mc Charles Chemotherapy unit consists of 11 nurses, and 5 stations with the following description:
Station 1: Station 1 has six chairs (numbered 1 to 6) and two nurses. The two nurses work from 8:00 to 16:00.
Station 2: Station 2 has six chairs (7 to 12) and three nurses. Two nurses work from 8:00 to 16:00 and one nurse works from 12:00 to 20:00.
Station 3: Station 4 has six chairs (13 to 18) and two nurses. The two nurses work from 8:00 to 16:00.
Station 4: Station 4 has six chairs (19 to 24) and three nurses. One nurse works from 8:00 to 16:00. Another nurse works from 10:00 to 18:00.
Solarium Station: Solarium Station has six chairs (Solarium Stretcher 1, Solarium Stretcher 2, Isolation, Isolation emergency, Fire Place 1, Fire Place 2). There is only one nurse assigned to this station that works from 12:00 to 20:00. The nurses from other stations can help when need arises.
There is one more nurse known as the "float nurse" who works from 11:00 to 19:00. This nurse can work at any station. Table 1 summarises the working hours of chairs and nurses. All treatment stations start at 8:00 and continue until the assigned nurse for that station completes her shift.
Currently, the clinic uses a scheduling template to assign the patients' appointments. But due to high demand of patient appointment it is not followed any more. We believe that this template can be improved based on the availability of nurses and chairs. Clinic workload was collected from 21 days of field observation. The current scheduling template has 10 types of appointment time slot: 15-­‐minute, 1-­‐hour, 1.5-­‐hour, 2-­‐hour, 3-­‐hour, 4-­‐hour, 5-­‐hour, 6-­‐hour, 8-­‐hour and 10-­‐hour and it is designed to serve 95 patients. But when the scheduling template was compared with the 21 days observations, it was found that the clinic is serving more patients than it is designed for. Therefore, the providers do not usually follow the scheduling template. Indeed they very often break the time slots to accommodate slots that do not exist in the template. Hence, we find that some of the stations are very busy (mostly station 2) and others are underused. If the scheduling template can be improved, it will be possible to bring more patients to the clinic and reduce their waiting time without adding more resources.
In order to build or develop a simulation model of the existing system, it is necessary to collect the following data:
Types of treatment durations.
Numbers of patients in each treatment type.
Arrival pattern of the patients.
Steps that the patients have to go through in their treatment journey and required time of each step.
Using the observations of 2,155 patients over 21 days of historical data, the types of treatment durations and the number of patients in each type were estimated. This data also assisted in determining the arrival rate and the frequency distribution of the patients. The patients were categorised into six types. The percentage of these types and their associated service times distributions are determined too.
ARENA Rockwell Simulation Software (v13) was used to build the simulation model. Entities of the model were tracked to verify that the patients move as intended. The model was run for 30 replications and statistical data was collected to validate the model. The total number of patients that go though the model was compared with the actual number of served patients during the 21 days of observations.
Improvement Scenarios
After verifying and validating the simulation model, different scenarios were designed and analysed to identify the best scenario that can handle more patients and reduces the average patient's waiting time. Based on the clinic observation and discussion with the healthcare providers, the following constraints have been stated:
The stations are filled up with treatment chairs. Therefore, it is literally impossible to fit any more chairs in the clinic. Moreover, the stakeholders are not interested in adding extra chairs.
The stakeholders and the caregivers are not interested in changing the layout of the treatment room.
Given these constraints the options that can be considered to design alternative scenarios are:
Changing the arrival pattern of the patients: that will fit over the nurses' availability.
Changing the nurses' schedule.
Adding one full time nurse at different starting times of the day.
Figure 2 compares the available number of nurses and the number of patients' arrival during different hours of a day. It can be noticed that there is a rapid growth in the arrival of patients (from 13 to 17) between 8:00 to 10:00 even though the clinic has the equal number of nurses during this time period. At 12:00 there is a sudden drop of patient arrival even though there are more available nurses. It is clear that there is an imbalance in the number of available nurses and the number of patient arrivals over different hours of the day. Consequently, balancing the demand (arrival rate of patients) and resources (available number of nurses) will reduce the patients' waiting time and increases the number of served patients. The alternative scenarios that satisfy the above three constraints are listed in Table 2. These scenarios respect the following rules:
Long treatments (between 4hr to 11hr) have to be scheduled early in the morning to avoid working overtime.
Patients of type 1 (15 minutes to 1hr treatment) are the most common. They can be fitted in at any time of the day because they take short treatment time. Hence, it is recommended to bring these patients in at the middle of the day when there are more nurses.
Nurses get tired at the end of the clinic day. Therefore, fewer patients should be scheduled at the late hours of the day.
In Scenario 1, the arrival pattern of the patient was changed so that it can fit with the nurse schedule. This arrival pattern is shown Table 3. Figure 3 shows the new patients' arrival pattern compared with the current arrival pattern. Similar patterns can be developed for the remaining scenarios too.
Analysis of Results
ARENA Rockwell Simulation software (v13) was used to develop the simulation model. There is no warm-­‐up period because the model simulates day-­‐to-­‐day scenarios. The patients of any day are supposed to be served in the same day. The model was run for 30 days (replications) and statistical data was collected to evaluate each scenario. Tables 4 and 5 show the detailed comparison of the system performance between the current scenario and Scenario 1. The results are quite interesting. The average throughput rate of the system has increased from 103 to 125 patients per day. The maximum throughput rate can reach 135 patients. Although the average waiting time has increased, the utilisation of the treatment station has increased by 15.6%. Similar analysis has been performed for the rest of the other scenarios. Due to the space limitation the detailed results are not given. However, Table 6 exhibits a summary of the results and comparison between the different scenarios. Scenario 1 was able to significantly increase the throughput of the system (by 21%) while it still results in an acceptable low average waiting time (13.4 minutes). In addition, it is worth noting that adding a nurse (Scenarios 3, 4, and 5) does not significantly reduce the average wait time or increase the system's throughput. The reason behind this is that when all the chairs are busy, the nurses have to wait until some patients finish the treatment. As a consequence, the other patients have to wait for the commencement of their treatment too. Therefore, hiring a nurse, without adding more chairs, will not reduce the waiting time or increase the throughput of the system. In this case, the only way to increase the throughput of the system is by adjusting the arrival pattern of patients over the nurses' schedule.
Developing a Scheduling Template based on Scenario 1
Scenario 1 provides the best performance. However a scheduling template is necessary for the care provider to book the patients. Therefore, a brief description is provided below on how scheduling the template is developed based on this scenario.
Table 3 gives the number of patients that arrive hourly, following Scenario 1. The distribution of each type of patient is shown in Table 7. This distribution is based on the percentage of each type of patient from the collected data. For example, in between 8:00-­‐9:00, 12 patients will come where 54.85% are of Type 1, 34.55% are of Type 2, 15.163% are of Type 3, 4.32% are of Type 4, 2.58% are of Type 5 and the rest are of Type 6. It is worth noting that, we assume that the patients of each type arrive as a group at the beginning of the hourly time slot. For example, all of the six patients of Type 1 from 8:00 to 9:00 time slot arrive at 8:00.
The numbers of patients from each type is distributed in such a way that it respects all the constraints described in Section 1.3. Most of the patients of the clinic are from type 1, 2 and 3 and they take less amount of treatment time compared with the patients of other types. Therefore, they are distributed all over the day. Patients of type 4, 5 and 6 take a longer treatment time. Hence, they are scheduled at the beginning of the day to avoid overtime. Because patients of type 4, 5 and 6 come at the beginning of the day, most of type 1 and 2 patients come at mid-­‐day (12:00 to 16:00). Another reason to make the treatment room more crowded in between 12:00 to 16:00 is because the clinic has the maximum number of nurses during this time period. Nurses become tired at the end of the clinic which is a reason not to schedule any patient after 19:00.
Based on the patient arrival schedule and nurse availability a scheduling template is built and shown in Figure 4. In order to build the template, if a nurse is available and there are patients waiting for service, a priority list of these patients will be developed. They are prioritised in a descending order based on their estimated slack time and secondarily based on the shortest service time. The secondary rule is used to break the tie if two patients have the same slack. The slack time is calculated using the following equation:
Slack time = Due time - (Arrival time + Treatment time)
Due time is the clinic closing time. To explain how the process works, assume at hour 8:00 (in between 8:00 to 8:15) two patients in station 1 (one 8-­‐hour and one 15-­‐ minute patient), two patients in station 2 (two 12-­‐hour patients), two patients in station 3 (one 2-­‐hour and one 15-­‐ minute patient) and one patient in station 4 (one 3-­‐hour patient) in total seven patients are scheduled. According to Figure 2, there are seven nurses who are available at 8:00 and it takes 15 minutes to set-­‐up a patient. Therefore, it is not possible to schedule more than seven patients in between 8:00 to 8:15 and the current scheduling is also serving seven patients by this time. The rest of the template can be justified similarly.
PMCID: PMC3562880  PMID: 23386870
24.  Patient–physician mistrust and violence against physicians in Guangdong Province, China: a qualitative study 
BMJ Open  2015;5(10):e008221.
To better understand the origins, manifestations and current policy responses to patient–physician mistrust in China.
Qualitative study using in-depth interviews focused on personal experiences of patient–physician mistrust and trust.
Guangdong Province, China.
One hundred and sixty patients, patient family members, physicians, nurses and hospital administrators at seven hospitals varying in type, geography and stages of achieving goals of health reform. These interviews included purposive selection of individuals who had experienced both trustful and mistrustful patient–physician relationships.
One of the most prominent forces driving patient–physician mistrust was a patient perception of injustice within the medical sphere, related to profit mongering, knowledge imbalances and physician conflicts of interest. Individual physicians, departments and hospitals were explicitly incentivised to generate revenue without evaluation of caregiving. Physicians did not receive training in negotiating medical disputes or humanistic principles that underpin caregiving. Patient–physician mistrust precipitated medical disputes leading to the following outcomes: non-resolution with patient resentment towards physicians; violent resolution such as physical and verbal attacks against physicians; and non-violent resolution such as hospital-mediated dispute resolution. Policy responses to violence included increased hospital security forces, which inadvertently fuelled mistrust. Instead of encouraging communication that facilitated resolution, medical disputes sometimes ignited a vicious cycle leading to mob violence. However, patient–physician interactions at one hospital that has implemented a primary care model embodying health reform goals showed improved patient–physician trust.
The blind pursuit of financial profits at a systems level has eroded patient–physician trust in China. Restructuring incentives, reforming medical education and promoting caregiving are pathways towards restoring trust. Assessing and valuing the quality of caregiving is essential for transitioning away from entrenched profit-focused models. Moral, in addition to regulatory and legal, responses are urgently needed to restore trust.
PMCID: PMC4606416  PMID: 26443652
25.  Interactions between Non-Physician Clinicians and Industry: A Systematic Review 
PLoS Medicine  2013;10(11):e1001561.
In a systematic review of studies of interactions between non-physician clinicians and industry, Quinn Grundy and colleagues found that many of the issues identified for physicians' industry interactions exist for non-physician clinicians.
Please see later in the article for the Editors' Summary
With increasing restrictions placed on physician–industry interactions, industry marketing may target other health professionals. Recent health policy developments confer even greater importance on the decision making of non-physician clinicians. The purpose of this systematic review is to examine the types and implications of non-physician clinician–industry interactions in clinical practice.
Methods and Findings
We searched MEDLINE and Web of Science from January 1, 1946, through June 24, 2013, according to PRISMA guidelines. Non-physician clinicians eligible for inclusion were: Registered Nurses, nurse prescribers, Physician Assistants, pharmacists, dieticians, and physical or occupational therapists; trainee samples were excluded. Fifteen studies met inclusion criteria. Data were synthesized qualitatively into eight outcome domains: nature and frequency of industry interactions; attitudes toward industry; perceived ethical acceptability of interactions; perceived marketing influence; perceived reliability of industry information; preparation for industry interactions; reactions to industry relations policy; and management of industry interactions. Non-physician clinicians reported interacting with the pharmaceutical and infant formula industries. Clinicians across disciplines met with pharmaceutical representatives regularly and relied on them for practice information. Clinicians frequently received industry “information,” attended sponsored “education,” and acted as distributors for similar materials targeted at patients. Clinicians generally regarded this as an ethical use of industry resources, and felt they could detect “promotion” while benefiting from industry “information.” Free samples were among the most approved and common ways that clinicians interacted with industry. Included studies were observational and of varying methodological rigor; thus, these findings may not be generalizable. This review is, however, the first to our knowledge to provide a descriptive analysis of this literature.
Non-physician clinicians' generally positive attitudes toward industry interactions, despite their recognition of issues related to bias, suggest that industry interactions are normalized in clinical practice across non-physician disciplines. Industry relations policy should address all disciplines and be implemented consistently in order to mitigate conflicts of interest and address such interactions' potential to affect patient care.
Please see later in the article for the Editors' Summary
Editors' Summary
Making and selling health care goods (including drugs and devices) and services is big business. To maximize the profits they make for their shareholders, companies involved in health care build relationships with physicians by providing information on new drugs, organizing educational meetings, providing samples of their products, giving gifts, and holding sponsored events. These relationships help to keep physicians informed about new developments in health care but also create the potential for causing harm to patients and health care systems. These relationships may, for example, result in increased prescription rates of new, heavily marketed medications, which are often more expensive than their generic counterparts (similar unbranded drugs) and that are more likely to be recalled for safety reasons than long-established drugs. They may also affect the provision of health care services. Industry is providing an increasingly large proportion of routine health care services in many countries, so relationships built up with physicians have the potential to influence the commissioning of the services that are central to the treatment and well-being of patients.
Why Was This Study Done?
As a result of concerns about the tension between industry's need to make profits and the ethics underlying professional practice, restrictions are increasingly being placed on physician–industry interactions. In the US, for example, the Physician Payments Sunshine Act now requires US manufacturers of drugs, devices, and medical supplies that participate in federal health care programs to disclose all payments and gifts made to physicians and teaching hospitals. However, other health professionals, including those with authority to prescribe drugs such as pharmacists, Physician Assistants, and nurse practitioners are not covered by this legislation or by similar legislation in other settings, even though the restructuring of health care to prioritize primary care and multidisciplinary care models means that “non-physician clinicians” are becoming more numerous and more involved in decision-making and medication management. In this systematic review (a study that uses predefined criteria to identify all the research on a given topic), the researchers examine the nature and implications of the interactions between non-physician clinicians and industry.
What Did the Researchers Do and Find?
The researchers identified 15 published studies that examined interactions between non-physician clinicians (Registered Nurses, nurse prescribers, midwives, pharmacists, Physician Assistants, and dieticians) and industry (corporations that produce health care goods and services). They extracted the data from 16 publications (representing 15 different studies) and synthesized them qualitatively (combined the data and reached word-based, rather than numerical, conclusions) into eight outcome domains, including the nature and frequency of interactions, non-physician clinicians' attitudes toward industry, and the perceived ethical acceptability of interactions. In the research the authors identified, non-physician clinicians reported frequent interactions with the pharmaceutical and infant formula industries. Most non-physician clinicians met industry representatives regularly, received gifts and samples, and attended educational events or received educational materials (some of which they distributed to patients). In these studies, non-physician clinicians generally regarded these interactions positively and felt they were an ethical and appropriate use of industry resources. Only a minority of non-physician clinicians felt that marketing influenced their own practice, although a larger percentage felt that their colleagues would be influenced. A sizeable proportion of non-physician clinicians questioned the reliability of industry information, but most were confident that they could detect biased information and therefore rated this information as reliable, valuable, or useful.
What Do These Findings Mean?
These and other findings suggest that non-physician clinicians generally have positive attitudes toward industry interactions but recognize issues related to bias and conflict of interest. Because these findings are based on a small number of studies, most of which were undertaken in the US, they may not be generalizable to other countries. Moreover, they provide no quantitative assessment of the interaction between non-physician clinicians and industry and no information about whether industry interactions affect patient care outcomes. Nevertheless, these findings suggest that industry interactions are normalized (seen as standard) in clinical practice across non-physician disciplines. This normalization creates the potential for serious risks to patients and health care systems. The researchers suggest that it may be unrealistic to expect that non-physician clinicians can be taught individually how to interact with industry ethically or how to detect and avert bias, particularly given the ubiquitous nature of marketing and promotional materials. Instead, they suggest, the environment in which non-physician clinicians practice should be structured to mitigate the potentially harmful effects of interactions with industry.
Additional Information
Please access these websites via the online version of this summary at
This study is further discussed in a PLOS Medicine Perspective by James S. Yeh and Aaron S. Kesselheim
The American Medical Association provides guidance for physicians on interactions with pharmaceutical industry representatives, information about the Physician Payments Sunshine Act, and a toolkit for preparing Physician Payments Sunshine Act reports
The International Council of Nurses provides some guidance on industry interactions in its position statement on nurse-industry relations
The UK General Medical Council provides guidance on financial and commercial arrangements and conflicts of interest as part of its good medical practice website, which describes what is required of all registered doctors in the UK
Understanding and Responding to Pharmaceutical Promotion: A Practical Guide is a manual prepared by Health Action International and the World Health Organization that schools of medicine and pharmacy can use to train students how to recognize and respond to pharmaceutical promotion.
The Institute of Medicine's Report on Conflict of Interest in Medical Research, Education, and Practice recommends steps to identify, limit, and manage conflicts of interest
The University of California, San Francisco, Office of Continuing Medical Education offers a course called Marketing of Medicines
PMCID: PMC3841103  PMID: 24302892

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