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1.  Changes resulting from increasing appointment length: practical and theoretical issues. 
The experience of one urban teaching practice in changing its appointment length from 7.5 to 10.0 minutes is described. Observed benefits to patients attending routine surgeries included an increased consultation time (mean 8.6 minutes before, 9.1 minutes after) and reduced waiting time (mean 19.1 minutes compared with 14.6 minutes). Overall, workload was unchanged but improving the 'fit' between supply and demand was associated with loss of flexibility--a greater number of extra patients required to be seen, apparently because fewer appointments were available at the start of each day. Waiting and consultation times in teaching surgeries and trainee surgeries (booked throughout at 10.0 minute intervals) were unchanged in response to the new arrangements. The changes introduced were well received by medical and reception staff although their response was not formally measured. Planning the organization of an appointment system requires several distinct decisions to be made. The preferred or actual average length of consultations has to be decided and booking arrangements designed to enable this to take place without the doctors persistently running over time. The number of appointments per week required to meet anticipated demand has to be calculated on the basis of list size and expected annual consultation rate. However, an exact fit between supply and demand will lead to congestion of the system and it appears that flexibility in the form of an overprovision of appointments to projected demand of about 120% should be built in. Sufficient vacant slots must be provided at the start of each day to allow sufficient flexibility to avoid excessive numbers of patients having to be accommodated.(ABSTRACT TRUNCATED AT 250 WORDS)
PMCID: PMC1372085  PMID: 1419259
2.  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
3.  Changing remuneration systems: effects on activity in general practice. 
BMJ : British Medical Journal  1990;300(6741):1698-1701.
OBJECTIVE--To investigate the effects on general practitioners' activities of a change in their remuneration from a capitation based system to a mixed fee per item and capitation based system. DESIGN--Follow up study with data collected from contact sheets completed by general practitioners in one period before (March 1987) a change in their remuneration system and two periods after (March 1988, November 1988), with a control group of general practitioners with a mixed fee per item and capitation based system throughout. SETTING--General practices in Copenhagen city (index group) and Copenhagen county (control group). SUBJECTS--265 General practitioners in Copenhagen city, of whom 100 were selected randomly from the 130 who agreed to participate (10 exclusions) and 326 general practitioners in Copenhagen county. MAIN OUTCOME MEASURES--Number of consultations (face to face and by telephone) and renewals of prescriptions, diagnostic and curative services, and specialist and hospital referrals per 1000 enlisted patients in one week. RESULTS--Of the 75 general practitioners who completed all three sheets, four were excluded for incomplete data. Total contact rates per 1000 patients listed rose significantly compared with the rates before the change index in the city (100.0 before the change v 111.7 (95% confidence interval 106.4 to 117.4 after the change) and over the same time in the control group (100.0 v 106.0), but within a year these rates fell (to 104.2(99.1 to 109.6) and 104.0 respectively). There was an increase in consultations by telephone initially but not thereafter. Rates of examinations and treatments that attracted specific additional remuneration after the change rose significantly compared with those before (diagnostic services, 138.1 (118.7 to 160.5) and 159.5 (137.8 to 184.7) and curative services 194.6 (152.2 to 248.9) and 194.8(152.3 to 249.2) for second and third data collections respectively) and with the control group (diagnostic services 105.3, 107.6 and curative services 106.0, 115.0) whereas referral rates to secondary care fell (specialist referrals 90.1 (80.7 to 100.6) and 77.0 (68.6 to 86.4) and hospital referrals 87.4 (71.1 to 107.5) and 68.4 (54.7 to 85.4] in doctors in the city. CONCLUSIONS--Introducing a partial fee for service system seemed to stimulate the provision of services by general practitioners, resulting in reduced referral rates. The concept of a "target income" which doctors aim at, rather than maximising their income seemed to play a part in adjustment to changing the system of remuneration.
PMCID: PMC1663335  PMID: 2390552
4.  Equity in waiting times for major joint arthroplasty 
Canadian Journal of Surgery  2002;45(4):269-276.
To ascertain whether waiting lists are managed in an equitable fashion in a universal health system by examining demographic, socioeconomic and clinical factors, along with 2 health systems variables.
A prospective survey by questionnaire.
The Capital Health Region of Edmonton, Alta.
Patients and methods
A cohort of 553 patients, who were waiting for either total hip or total knee replacement surgery, seen between Dec. 18, 1995, and Jan. 24, 1997.
A home visit was made when the patient was first placed on the waiting list and again just before surgery to complete the questionnaires. The Western Ontario and McMaster Universities (WOMAC) instrument and the Medication Quantification Score were administered at the time the patient was placed on the waiting list.
Main outcome measure
The length of waiting time, defined as the date the patient was put on the waiting list to the date the patient was operated on.
There were no biases in waiting time with respect to age, gender, education or work status. Although pain and function were not related to waiting time, multivariate analyses found that marital status, primary language, body mass index, pain medication use and the size of the surgeons’ major joint replacement practice determined waiting time for surgery. However, this model explained only 10% of the variance in waiting time.
Waiting lists were managed unfairly in terms of clinical equity (clinical severity) but managed fairly in terms of social equity.
PMCID: PMC3684679  PMID: 12174981
5.  Left Ventricular Assist Devices 
Executive Summary
The objective of this health technology policy assessment was to determine the effectiveness and cost-effectiveness of using implantable ventricular assist devices in the treatment of end-stage heart failure.
Heart Failure
Heart failure is a complex syndrome that impairs the ability of the heart to maintain adequate blood circulation, resulting in multiorgan abnormalities and, eventually, death. In the period of 1994 to 1997, 38,702 individuals in Ontario had a first hospital admission for heart failure. Despite reported improvement in survival, the five-year mortality rate for heart failure is about 50%.
For patients with end-stage heart failure that does not respond to medical therapy, surgical treatment or traditional circulatory assist devices, heart transplantation (in appropriate patients) is the only treatment that provides significant patient benefit.
Heart Transplant in Ontario
With a shortage in the supply of donor hearts, patients are waiting longer for a heart transplant and may die before a donor heart is available. From 1999 to 2003, 55 to 74 people received a heart transplant in Ontario each year. Another 12 to 21 people died while waiting for a suitable donor heart. Of these, 1 to 5 deaths occurred in people under 18 years old. The rate-limiting factor in heart transplant is the supply of donor hearts. Without an increase in available donor hearts, attempts at prolonging the life of some patients on the transplant wait list could have a harmful effect on other patients that are being pushed down the waiting list (knock on effect).
LVAD Technology
Ventricular assist devices [VADs] have been developed to provide circulatory assistance to patients with end-stage heart failure. These are small pumps that usually assist the damaged left ventricle [LVADs] and may be situated within the body (intracorporeal] or outside the body [extracorporeal). Some of these devices were designed for use in the right ventricle [RVAD] or both ventricles (bi-ventricular).
LVADs have been mainly used as a “bridge-to-transplant” for patients on a transplant waiting list. As well, they have been used as a “bridge-to-recovery” in acute heart failure, but this experience is limited. There has been an increasing interest in using LVAD as a permanent (destination) therapy.
Review of LVAD by the Medical Advisory Secretariat
The Medical Advisory Secretariat’s review included a descriptive synthesis of findings from five systematic reviews and 60 reports published between January 2000 and December 2003. Additional information was obtained through consultation and by searching the websites of Health Canada, the United Network of Organ Sharing, Organ Donation Ontario, and LVAD manufacturers.
Summary of Findings
Safety and Effectiveness
Previous HTAs and current Level 3 evidence from prospective non-randomized controlled studies showed that when compared to optimal medical therapy, LVAD support significantly improved the pre-transplant survival rates of heart transplant candidates waiting for a suitable donor heart (71% for LVAD and 36% for medical therapy). Pre-transplant survival rates reported ranged from 58% to 90% (median 74%). Improved transplant rates were also reported for people who received pre-transplant LVAD support (e.g. 67% for LVAD vs 33% for medical therapy). Reported transplant rates for LVAD patients ranged from 39% to 90% (median 71%).
Patient’s age greater than 60 years and pre-existing conditions of respiratory failure associated with septicemia, ventilation, and right heart failure were independent risk factors for mortality after the LVAD implantation.
LVAD support was shown to improve the New York Heart Association [NYHA)] functional classification and quality of life of patients waiting for heart transplant. LVAD also enabled approximately 41% - 49% of patients to be discharged from hospitals and wait for a heart transplant at home. However, over 50% of the discharged patients required re-hospitalization due to adverse events.
Post-transplant survival rates for LVAD-bridged patients were similar to or better than the survival rates of patients bridged by medical therapy.
LVAD support has been associated with serious adverse events, including infection (median 53%, range 6%–72%), bleeding (8.6%–48%, median 35%), thromboembolism (5%–37%), neurologic disorders (7%–28%), right ventricular failure (11%–26%), organ dysfunction (5%–50%) and hemolysis (6%–20%). Bleeding tends to occur in the first few post-implant days and is rare thereafter. It is fatal in 2%–7% of patients. Infection and thromboembolism occurred throughout the duration of the implant, though their frequency tended to diminish with time. Device malfunction has been identified as one of the major complications. Fatalities directly attributable to the devices were about 1% in short-term LVAD use. However, mechanical failure was the second most frequent cause of death in patients on prolonged LVAD support. Malfunctions were mainly associated with the external components, and often could be replaced by backed up components.
LVAD has been used as a bridge-to-recovery in patients suffering from acute cardiogenic shock due to cardiomyopathy, myocarditis or cardiotomy. The survival rates were reported to be lower than in bridge-to-transplant (median 26%). Some of the bridge-to-recovery patients (14%–75%) required a heart transplant or remained on prolonged LVAD support. According to an expert in the field, experience with LVAD as a bridge-to-recovery technology has been more favourable in Germany than in North America, where it is not regarded as a major indication since evidence for its effectiveness in this setting is limited.
LVAD has also been explored as a destination therapy. A small, randomized, controlled trial (level 2 evidence) showed that LVAD significantly increased the 1-year survival rate of patients with end-stage heart failure but were not eligible for a heart transplant (51% LVAD vs 25% for medical therapy). However, improved survival was associated with adverse events 2.35 times higher than medically treated patients and a higher hospital re-admission rate. The 2-year survival rate on LVAD decreased to 23%, although it was still significantly better compared to patients on medical therapy (8%). The leading causes of deaths were sepsis (41%) and device failure (17%).
The FDA has given conditional approval for the permanent use of HeartMate SNAP VE LVAS in patients with end-stage heart failure who are not eligible for heart transplantation, although the long-term effect of this application is not known.
In Canada, four LVAD systems have been licensed for bridge-to-transplant only. The use of LVAD support raises ethical issues because of the implications of potential explantation that could be perceived as a withdrawal of life support.
Potential Impact on the Transplant Waiting List
With the shortage of donor hearts for adults, LVAD support probably would not increase the number of patients who receive a heart transplant. If LVAD supported candidates are prioritized for urgent heart transplant, there will be a knock on effect as other transplant candidates without LVAD support would be pushed down, resulting in longer wait, deterioration in health status and die before a suitable donor heart becomes available.
Under the current policy for allocating donor hearts in Ontario, patients on LVAD support would be downgraded to Status 3 with a lower priority to receive a transplant. This would likely result in an expansion of the transplant waiting list with an increasing number of patients on prolonged LVAD support, which is not consistent with the indication of LVAD use approved by Health Canada.
There is indication in the United Kingdom that LVAD support in conjunction with an urgent transplant listing in the pediatric population may decrease the number of deaths on the waiting list without a harmful knock-on effect on other transplant candidates.
LVAD support as a bridge-to-transplant has been shown to improve the survival rate, functional status and quality of life of patients on the heart transplant waiting list. However, due to the shortage of donor hearts and the current heart transplant algorithm, LVAD support for transplant candidates of all age groups would likely result in an expansion of the waiting list and prolonged use of LVAD with significant budget implications but without increasing the number of heart transplants. Limited level 4 evidence showed that LVAD support in children yielded survival rates comparable to those in the adult population. The introduction of LVAD in the pediatric population would be more cost-effective and might not have a negative effect on the transplant waiting list.
PMCID: PMC3387736  PMID: 23074453
6.  What is the impact of primary care model type on specialist referral rates? A cross-sectional study 
BMC Family Practice  2014;15:22.
Several new primary care models have been implemented in Ontario, Canada over the past two decades. These practice models differ in team structure, physician remuneration, and group size. Few studies have examined the impact of these models on specialist referrals. We compared specialist referral rates amongst three primary care models: 1) Enhanced Fee-for-service, 2) Capitation- Non-Interdisciplinary (CAP-NI), 3) Capitation – Interdisciplinary (CAP-I).
We conducted a cross-sectional study using health administrative data from primary care practices in Ontario from April 1st, 2008 to March 31st, 2010. The analysis included all family physicians providing comprehensive care in one of the three models, had at least 100 patients, and did not have a prolonged absence (eight consecutive weeks). The primary outcome was referral rate (# of referrals to all medical specialties/1000 patients/year). A multivariable clustered Poisson regression analysis was used to compare referral rates between models while adjusting for provider (sex, years since graduation, foreign trained, time in current model) and patient (age, sex, income, rurality, health status) characteristics.
Fee-for-service had a significantly lower adjusted referral rate (676, 95% CI: 666-687) than the CAP-NI (719, 95% confidence interval (CI): 705-734) and CAP-I (694, 95% CI: 681-707) models and the interdisciplinary CAP-I group had a 3.5% lower referral rate than the CAP-NI group (RR = 0.965, 95% CI: 0.943-0.987, p = 0.002). Female and Canadian-trained physicians referred more often, while female, older, sicker and urban patients were more likely to be referred.
Primary care model is significantly associated with referral rate. On a study population level, these differences equate to 111,059 and 37,391 fewer referrals by fee-for-service versus CAP-NI and CAP-I, respectively – a difference of $22.3 million in initial referral appointment costs. Whether a lower rate of referral is more appropriate or not is not known and requires further investigation. Physician remuneration and team structure likely account for the differences; however, further investigation is also required to better understand whether other organizational factors associated with primary care model also impact referral.
PMCID: PMC3933232  PMID: 24490703
Primary care; Specialist referral; Capitation; Primary care model
7.  Characteristics of practices, general practitioners and patients related to levels of patients' satisfaction with consultations. 
BACKGROUND: Despite interest in the relationship between patient satisfaction and consultation performance, there is little information about how other characteristics of general practitioners, practices and patients influence satisfaction with consultations. AIM: To identify characteristics of patients, practices and general practitioners that influence satisfaction with consultations. METHOD: In 1991-92, a consultation satisfaction questionnaire (CSQ) was administered to 75 patients attending each of the 126 general practitioners in 39 practices. Further questionnaires were used to collect information about the practice (such as total list size, training status, fundholding status and presence of a personal list system) and about the general practitioners (age, sex, whether vocationally trained, a trainer or a trainee, and the number of patients booked in the appointment system per hour). Stepwise multiple regression was undertaken to identify characteristics of patients, practices or general practitioners that influenced satisfaction. RESULTS: The mean of the response rates to the patient questionnaire for each general practitioner was 76.6%, with a standard deviation (SD) of 17.8. Practice characteristics associated with falls in satisfaction were an increasing total list size, the absence of a personal list system and its being a training practice. If more patients were booked in the appointment system per hour, satisfaction with the perceived length of consultations fell. Patient characteristics associated with falls in satisfaction were increased age and an increased proportion of male patients. The only characteristic of general practitioners associated with lower levels of satisfaction was increasing age. The sex of general practitioners did not influence satisfaction. CONCLUSIONS: The findings of this study give further support to the importance of a personal service in determining patient satisfaction in general practice. General Practitioners need to review the organization of practices to ensure an acceptable balance between the requirements of modern clinical care and the wishes of patients. Future studies should take account of the many variables that can influence patient satisfaction.
PMCID: PMC1239785  PMID: 8945798
8.  Effect of the remuneration system on the general practitioner's choice between surgery consultations and home visits. 
OBJECTIVE--To assess the influence of the remuneration system, municipality, doctor, and patient characteristics on general practitioners' choices between surgery and home visits. DESIGN--Prospective registration of patient contacts during one week for 116 general practitioners (GPs). SETTING--General practice in rural areas of northern Norway. MAIN OUTCOME MEASURE--Type of GP visit (surgery v home visit). RESULTS--The estimated home visit rate was 0.14 per person per year. About 7% (range 0-39%) of consultations were home visits. Using multilevel analysis it was found that doctors paid on a "fee for service" basis tended to choose home visits more often than salaried doctors (adjusted odds ratio 1.90, 99% confidence interval 0.98, 3.69), but this was statistically significant for "scheduled" visits only (adjusted OR 4.50, 99% CI 1.67, 12.08). Patients who were older, male, and who were living in areas well served by doctors were more likely to receive home visits. CONCLUSION--In the choice between home visits and surgery consultations, doctors seem to be influenced by the nature of the remuneration when the patient's problem is not acute. Although home visiting is a function of tradition, culture, and organisational characteristics, the study indicates that financial incentives may be used to change behaviour and encourage home visiting.
PMCID: PMC1059863  PMID: 8120504
9.  Comparative Efficacy of Seven Psychotherapeutic Interventions for Patients with Depression: A Network Meta-Analysis 
PLoS Medicine  2013;10(5):e1001454.
Jürgen Barth and colleagues use network meta-analysis - a novel methodological approach - to reexamine the comparative efficacy of seven psychotherapeutic interventions for adults with depression.
Please see later in the article for the Editors' Summary
Previous meta-analyses comparing the efficacy of psychotherapeutic interventions for depression were clouded by a limited number of within-study treatment comparisons. This study used network meta-analysis, a novel methodological approach that integrates direct and indirect evidence from randomised controlled studies, to re-examine the comparative efficacy of seven psychotherapeutic interventions for adult depression.
Methods and Findings
We conducted systematic literature searches in PubMed, PsycINFO, and Embase up to November 2012, and identified additional studies through earlier meta-analyses and the references of included studies. We identified 198 studies, including 15,118 adult patients with depression, and coded moderator variables. Each of the seven psychotherapeutic interventions was superior to a waitlist control condition with moderate to large effects (range d = −0.62 to d = −0.92). Relative effects of different psychotherapeutic interventions on depressive symptoms were absent to small (range d = 0.01 to d = −0.30). Interpersonal therapy was significantly more effective than supportive therapy (d = −0.30, 95% credibility interval [CrI] [−0.54 to −0.05]). Moderator analysis showed that patient characteristics had no influence on treatment effects, but identified aspects of study quality and sample size as effect modifiers. Smaller effects were found in studies of at least moderate (Δd = 0.29 [−0.01 to 0.58]; p = 0.063) and large size (Δd = 0.33 [0.08 to 0.61]; p = 0.012) and those that had adequate outcome assessment (Δd = 0.38 [−0.06 to 0.87]; p = 0.100). Stepwise restriction of analyses by sample size showed robust effects for cognitive-behavioural therapy, interpersonal therapy, and problem-solving therapy (all d>0.46) compared to waitlist. Empirical evidence from large studies was unavailable or limited for other psychotherapeutic interventions.
Overall our results are consistent with the notion that different psychotherapeutic interventions for depression have comparable benefits. However, the robustness of the evidence varies considerably between different psychotherapeutic treatments.
Please see later in the article for the Editors' Summary
Editors' Summary
Depression is a very common condition. One in six people will experience depression at some time during their life. People who are depressed have recurrent feelings of sadness and hopelessness and might feel that life is no longer worth living. The condition can last for months and often includes physical symptoms such as headaches, sleeping problems, and weight gain or loss. Treatment of depression can include non-drug treatments (psychotherapy), antidepressant drugs, or a combination of the two. Especially for people with mild or intermediate depression, psychotherapy is often considered the preferred first option. Psychotherapy describes a range of different psychotherapies, and a number of established types of psychotherapies have all shown to work for at least some patients.
Why Was This Study Done?
While it is broadly accepted that psychotherapy can help people with depression, the question of which type of psychotherapy works best for most patients remains controversial. While many scientific studies have compared one psychotherapy with control conditions, there have been few studies that directly compared multiple treatments. Without such direct comparisons, it has been difficult to establish the respective merits of the different types of psychotherapy. Taking advantage of a recently developed method called “network meta-analysis,” the authors re-examine the evidence on seven different types of psychotherapy to see how well they have been shown to work and whether some work better than others.
What Did the Researchers Do and Find?
The researchers looked at seven different types of psychotherapy, which they defined as follows. “Interpersonal psychotherapy” is short and highly structured, using a manual to focus on interpersonal issues in depression. “Behavioral activation” raises the awareness of pleasant activities and seeks to increase positive interactions between the patient and his or her environment. “Cognitive behavioral therapy” focuses on a patient's current negative beliefs, evaluates how they affect current and future behavior, and attempts to restructure the beliefs and change the outlook. “Problem solving therapy” aims to define a patient's problems, propose multiple solutions for each problem, and then select, implement, and evaluate the best solution. “Psychodynamic therapy” focuses on past unresolved conflicts and relationships and the impact they have on a patient's current situation. In “social skills therapy,” patients are taught skills that help to build and maintain healthy relationships based on honesty and respect. “Supportive counseling” is a more general therapy that aims to get patients to talk about their experiences and emotions and to offer empathy without suggesting solutions or teaching new skills.
The researchers started with a systematic search of the medical literature for relevant studies. The search identified 198 articles that reported on such clinical trials. The trials included a total of 15,118 patients and compared one of the seven psychotherapies either with another one or with a common “control intervention”. In most cases, the control (no psychotherapy) was deferral of treatment by “wait-listing” patients or continuing “usual care.” With network meta-analysis they were able to summarize the results of all these trials in a meaningful way. They did this by integrating direct comparisons of several psychotherapies within the same trial (where those were available) with indirect comparisons across all trials (using no psychotherapy as a control intervention).
Based on the combined trial results, all seven psychotherapies tested were better than wait-listing or usual care, and the differences were moderate to large, meaning that the average person in the group that received therapy was better off than about half of the patients in the control group. When comparing the therapies with each other, the researchers saw small or no differences, meaning that none of them really stood out as much better or much worse than the others. They also found that the treatments worked equally well for different patient groups with depression (younger or older patients, or mothers who had depression after having given birth). Similarly, they saw no big differences when comparing individual with group therapy, or person-to-person with internet-based interactions between therapist and patient.
However, they did find that smaller and less rigorous studies generally found larger benefits of psychotherapies, and most of the studies included in the analysis were small. Only 36 of the studies had at least 50 patients who received the same treatment. When they restricted their analysis to those studies, the researchers still saw clear benefits of cognitive-behavioral therapy, interpersonal therapy, and problem-solving therapy, but not for the other four therapies.
What Do these Findings Mean?
Similar to earlier attempts to summarize and make sense of the many study results, this one finds benefits for all of the seven psychotherapies examined, and none of them stood as being much better than some or all others. The scientific support for being beneficial was stronger for some therapies, mostly because they had been tested more often and in larger studies.
Treatments with proven benefits still do not necessarily work for all patients, and which type of psychotherapy might work best for a particular patient likely depends on that individual. So overall this analysis suggests that patients with depression and their doctors should consider psychotherapies and explore which of the different types might be best suited for a particular patient.
The study also points to the need for further research. Whereas depression affects large numbers of people around the world, all of the trials identified were conducted in rich countries and Western societies. Trials in different settings are essential to inform treatment of patients worldwide. In addition, large high-quality studies should further explore the potential benefits of some of therapies for which less support currently exists. Where possible, future studies should compare psychotherapies with one another, because all of them have benefits, and it would not be ethical to withhold such beneficial treatment from patients.
Additional Information
Please access these Web sites via the online version of this summary at
The US National Institute of Mental Health provides information on all aspects of depression (in English and Spanish); information on psychotherapy includes information on its most common forms
The UK National Health Service Choices website also provides detailed information about depression and includes personal stories about depression
The UK nonprofit Mind provides information on depression, including an explanation of the most common psychotherapies in the UK
MedlinePlus provides links to other resources about depression (in English and Spanish)
The UK nonprofit has a unique database of personal and patient experiences on depression
PMCID: PMC3665892  PMID: 23723742
10.  A qualitative study in rural and urban areas on whether – and how – to consult during routine and out of hours 
BMC Family Practice  2006;7:26.
Patients vary widely when making decisions to consult primary care. Some present frequently with trivial illness: others delay with serious disease. Differences in health service provision may play a part in this. We aimed to explore whether and how patients' consulting intentions take account of their perceptions of health service provision.
Four focus groups and 51 semi-structured interviews with 78 participants (45 to 64 years) in eight urban and rural general practices in Northeast and Southwest Scotland. We used vignettes to stimulate discussion about what to do and why. Inductive analysis identified themes and explored the influence of their perceptions of health service provision on decision-making processes.
Anticipated waiting times for appointments affected consulting intentions, especially when the severity of symptoms was uncertain. Strategies were used to deal with this, however: in cities, these included booking early just in case, being assertive, demanding visits, or calling out-of-hours; in rural areas, participants used relationships with primary care staff, and believed that being perceived as undemanding was advantageous. Out-of-hours, decisions to consult were influenced by opinions regarding out-of-hours services. Some preferred to attend nearby emergency departments or call 999. In rural areas, participants tended to delay until their own doctor was available, or might contact them even when not on call.
Perceived barriers to health service access affect decisions to consult, but some patients develop strategies to get round them. Current changes in UK primary care are unlikely to reduce differences in consulting behaviour and may increase delays by some patients, especially in rural areas.
PMCID: PMC1523347  PMID: 16640780
11.  Waiting list dynamics and the impact of earmarked funding. 
BMJ : British Medical Journal  1995;311(7008):783-785.
OBJECTIVE--To determine how changes in the number of admissions from waiting lists and changes in the number of additions to the lists are related to list size and waiting times, in the context of local waiting list initiatives. DESIGN--Review of national and Körner statistics. SETTING--England (1987-94) and districts of the former Oxford region (1987-91). MAIN OUTCOME MEASURES--Correlation of quarterly changes in the number of admissions from waiting lists in England with changes in total list size, numbers of patients waiting one to two, or over two years, and number of additions to the lists; examination of changes in waiting list statistics for individual district specialties in one region in relation to funding for waiting list initiatives. RESULTS--Nationally, changes in the number of admissions to hospital from lists closely correlated with changes in the number of additions to lists (r = 0.84; P < 0.01). After adjusting for changes in the number of additions to lists, changes in the number of admissions correlated inversely with changes in list size (r = -0.62; P < 0.001). Decreases in the number of patients waiting from one to two years were significantly associated with increases in the number of admissions (r = -0.52; P < 0.01); locally, only six of 44 waiting list initiatives were followed by an increase in admissions and a fall in list size, although a further 11 were followed by a fall in list size without a corresponding increase in admissions. CONCLUSIONS--An increase in admissions improved waiting times but did not reduce list size because additions to the list tended to increase at the same time. The appropriateness of waiting list initiatives as a method of funding elective surgery should be reviewed.
PMCID: PMC2550789  PMID: 7580440
12.  Social Isolation in Community-Dwelling Seniors 
Executive Summary
In early August 2007, the Medical Advisory Secretariat began work on the Aging in the Community project, an evidence-based review of the literature surrounding healthy aging in the community. The Health System Strategy Division at the Ministry of Health and Long-Term Care subsequently asked the secretariat to provide an evidentiary platform for the ministry’s newly released Aging at Home Strategy.
After a broad literature review and consultation with experts, the secretariat identified 4 key areas that strongly predict an elderly person’s transition from independent community living to a long-term care home. Evidence-based analyses have been prepared for each of these 4 areas: falls and fall-related injuries, urinary incontinence, dementia, and social isolation. For the first area, falls and fall-related injuries, an economic model is described in a separate report.
Please visit the Medical Advisory Secretariat Web site,, to review these titles within the Aging in the Community series.
Aging in the Community: Summary of Evidence-Based Analyses
Prevention of Falls and Fall-Related Injuries in Community-Dwelling Seniors: An Evidence-Based Analysis
Behavioural Interventions for Urinary Incontinence in Community-Dwelling Seniors: An Evidence-Based Analysis
Caregiver- and Patient-Directed Interventions for Dementia: An Evidence-Based Analysis
Social Isolation in Community-Dwelling Seniors: An Evidence-Based Analysis
The Falls/Fractures Economic Model in Ontario Residents Aged 65 Years and Over (FEMOR)
Objective of the Evidence-Based Analysis
The objective was to systematically review interventions aimed at preventing or reducing social isolation and loneliness in community-dwelling seniors, that is, persons ≥ 65 years of age who are not living in long-term care institutions. The analyses focused on the following questions:
Are interventions to reduce social isolation and/or loneliness effective?
Do these interventions improve health, well-being, and/or quality of life?
Do these interventions impact on independent community living by delaying or preventing functional decline or disability?
Do the interventions impact on health care utilization, such as physician visits, emergency visits, hospitalization, or admission to long-term care?
Background: Target Population and Condition
Social and family relationships are a core element of quality of life for seniors, and these relationships have been ranked second, next to health, as the most important area of life. Several related concepts—reduced social contact, being alone, isolation, and feelings of loneliness—have all been associated with a reduced quality of life in older people. Social isolation and loneliness have also been associated with a number of negative outcomes such as poor health, maladaptive behaviour, and depressed mood. Higher levels of loneliness have also been associated with increased likelihood of institutionalization.
Note: It is recognized that the terms “senior” and “elderly” carry a range of meanings for different audiences; this report generally uses the former, but the terms are treated here as essentially interchangeable.
Methods of the Evidence-Based Analysis
The scientific evidence base was evaluated through a systematic literature review. The literature searches were conducted with several computerized bibliographic databases for literature published between January 1980 and February 2008. The search was restricted to English-language reports on human studies and excluded letters, comments and editorials, and case reports. Journal articles eligible for inclusion in the review included those that reported on single, focused interventions directed towards or evaluating social isolation or loneliness; included, in whole or in part, community-dwelling seniors (≥ 65 years); included some quantitative outcome measure on social isolation or loneliness; and included a comparative group. Assessments of current practices were obtained through consultations with various individuals and agencies including the Ontario Community Care Access Centres and the Ontario Assistive Devices Program. An Ontario-based budget impact was also assessed for the identified effective interventions for social isolation.
A systematic review of the published literature focusing on interventions for social isolation and loneliness in community-dwelling seniors identified 11 quantitative studies. The studies involved European or American populations with diverse recruitment strategies, intervention objectives, and limited follow-up, with cohorts from 10 to 15 years ago involving mainly elderly women less than 75 years of age. The studies involved 2 classes of interventions: in-person group support activities and technology-assisted interventions. These were delivered to diverse targeted groups of seniors such as those with mental distress, physically inactive seniors, low-income groups, and informal caregivers. The interventions were primarily focused on behaviour-based change. Modifying factors (client attitude or preference) and process issues (targeting methods of at-risk subjects, delivery methods, and settings) influenced intervention participation and outcomes.
Both classes of interventions were found to reduce social isolation and loneliness in seniors. Social support groups were found to effectively decrease social isolation for seniors on wait lists for senior apartments and those living in senior citizen apartments. Community-based exercise programs featuring health and wellness for physically inactive community-dwelling seniors also effectively reduced loneliness. Rehabilitation for mild/moderate hearing loss was effective in improving communication disabilities and reducing loneliness in seniors. Interventions evaluated for informal caregivers of seniors with dementia, however, had limited effectiveness for social isolation or loneliness.
Research into interventions for social isolation in seniors has not been broadly based, relative to the diverse personal, social, health, economic, and environmentally interrelated factors potentially affecting isolation. Although rehabilitation for hearing-related disability was evaluated, the systematic review did not locate research on interventions for other common causes of aging-related disability and loneliness, such as vision loss or mobility declines. Despite recent technological advances in e-health or telehealth, controlled studies evaluating technology-assisted interventions for social isolation have examined only basic technologies such as phone- or computer-mediated support groups.
Although effective interventions were identified for social isolation and loneliness in community-dwelling seniors, they were directed at specifically targeted groups and involved only a few of the many potential causes of social isolation. Little research has been directed at identifying effective interventions that influence the social isolation and other burdens imposed upon caregivers, in spite of the key role that caregivers assume in caring for seniors. The evidence on technology-assisted interventions and their effects on the social health and well-being of seniors and their caregivers is limited, but increasing demand for home health care and the need for efficiencies warrant further exploration. Interventions for social isolation in community-dwelling seniors need to be researched more broadly in order to develop effective, appropriate, and comprehensive strategies for at-risk populations.
PMCID: PMC3377559  PMID: 23074510
13.  The challenge of long waiting lists: how we implemented a GP referral system for non-urgent specialist' appointments at an Australian public hospital 
Our Problem
The length of wait lists to access specialist clinics in the public system is problematic for Queensland Health, general practitioners and patients. To address this issue at The Townsville Hospital, the GP Liaison Officer, GPs and hospital staff including specialists, collaborated to develop a process to review patients waiting longer than two years. GPs frequently send referrals to public hospital specialist clinics. Once received, referrals are triaged to Category A, B or C depending on clinical criteria resulting in appointment timeframes of 30, 90 or 365 days for each category, respectively. However, hospitals often fail to meet these targets, creating a long wait list. These wait listed patients are only likely to be seen if their condition deteriorates and an updated referral upgrades them to Category A.
Process to Address the Problem
A letter sent to long wait patients offered two options 1) take no action if the appointment was no longer required or 2) visit their GP to update their referral on a clinic specific template if they felt the referral was still required. Local GPs were advised of the trial and provided education on the new template and minimum data required for specialist referrals.
What Happened
In 2008, 872 letters were sent to long wait orthopaedic patients and 101 responded. All respondents were seen at specially arranged clinics. Of these, 16 patients required procedures and the others were discharged. In 2009 the process was conducted in the specialties of orthopaedics, ENT, neurosurgery, urology, and general surgery. Via this new process 6885 patients have been contacted, 633 patients have been seen by public hospital specialists at specially arranged clinics and 197 have required a procedure.
Since the start of this process in 2008, the wait time to access a specialist appointment has reduced from eight to two years. The process described here is achievable across a range of specialties, deliverable within the routine of the referral centre and identifies the small number of people on the long wait list in need of a procedure.
PMCID: PMC2991304  PMID: 21050488
14.  The effects of New York state's ban on multiple listing for cadaveric kidney transplantation. 
Health Services Research  1998;33(2 Pt 1):205-222.
OBJECTIVE: To study the effectiveness of a 1990 ban by New York state on entry to more than one waiting list for a cadaver kidney transplant, and the impact of the ban on equity in access to transplantation. DATA SOURCES: (1) Waiting list files from the Organ Procurement and Transplantation Network, (2) the Health Care Financing Administration's Medicare Program Management and Medical Information System, and (3) U.S. Census Public Use Files. STUDY DESIGN: Multivariate hazard models were used to estimate the impact of the ban of the overall odds of multiple listing and on the odds of multiple listing at in-state and out-of-state transplant centers. After estimating the relationship between multiple listing and subsequent transplantation, we used simulation techniques to estimate the effects of a complete multiple listing ban on group waiting time differentials. Independent variables included demographic/socioeconomic characteristics, measures of ESRD severity, general transplantation suitability, measures that affect the likelihood of finding a good donor organ, and measures of the productivity of the transplant/dialysis center. PRINCIPAL FINDINGS: The ban was associated with a 66 percent reduction in the rate of multiple listing for New York patients, and multiple listing at in-state transplant centers declined by 87 percent. Simulation results suggested that even a completely effective ban would produce only small, mixed equity effects. CONCLUSIONS: While the ban was effective in reducing the proportion of patients who registered at multiple transplant centers, taken together the results suggest that banning multiple listing is not likely to result in large improvements in equity in access to transplantation.
PMCID: PMC1070261  PMID: 9618668
15.  Impact of remuneration on guideline adherence: Empirical evidence in general practice 
Background and objective. Changes in the Dutch GP remuneration system provided the opportunity to study the effects of changes in financial incentives on the quality of care. Separate remuneration systems for publicly insured patients (capitation) and privately insured patients (fee-for-service) were replaced by a combined system of capitation and fee-for-service for all in 2006. The effects of these changes on the quality of care in terms of guideline adherence were investigated. Design and setting. A longitudinal study from 2002 to 2009 using data from patient electronic medical records in general practice. A multilevel (patient and practice) approach was applied to study the effect of changes in the remuneration system on guideline adherence. Subjects. 21 421 to 39 828 patients from 32 to 52 general practices (dynamic panel of GPs). Main outcome measures. Sixteen guideline adherence indicators on prescriptions and referrals for acute and chronic conditions. Results. Guideline adherence increased between 2002 and 2008 by 7% for (formerly) publicly insured patients and 10% for (formerly) privately insured patients. In general, no significant differences in the trends for guideline adherence were found between privately and publicly insured patients, indicating the absence of an effect of the remuneration system on guideline adherence. Adherence to guidelines involving more time investment in terms of follow-up contacts was affected by changes in the remuneration system. For publicly insured patients, GPs showed a higher trend for guideline adherence for guidelines involving more time investment in terms of follow-up contacts compared with privately insured patients. Conclusion. The change in the remuneration system had a limited impact on guideline adherence.
PMCID: PMC3587301  PMID: 23330604
General practice; guideline adherence; quality of care; remuneration system; The Netherlands
16.  List sizes and use of time in general practice. 
The claim that list sizes in general practice should continue to fall towards a national average of 1700 patients rests heavily on the assumption that the extra time available to doctors would be used mainly for longer consultations, resulting in better standards of care. Evidence suggests, however, that the time is more likely to be used to increase rates of consultation in surgeries and home visits and to reduce the length of the working week. A national, random sample of 2104 principals in general practice in England and Wales were questioned about their allocation and use of time. The response rate was 67%, and no large biases in response were detected. The smaller their personal list size the less time general practitioners spent on all aspects of their work and the higher their rates of consultation and home visiting. The effects of further reductions in list sizes would be haphazard, being differentially distributed across the range of list sizes. Longer consultations would probably result, but most of the extra time would probably be used in higher rates of consultation in surgeries and home visits and some would be taken as free time.
PMCID: PMC1248545  PMID: 3121026
17.  Delivery of primary health care to persons who are socio-economically disadvantaged: does the organizational delivery model matter? 
As health systems evolve, it is essential to evaluate their impact on the delivery of health services to socially disadvantaged populations. We evaluated the delivery of primary health services for different socio-economic groups and assessed the performance of different organizational models in terms of equality of health care delivery in Ontario, Canada.
Cross sectional study of 5,361 patients receiving care from primary care practices using Capitation, Salaried or Fee-For-Service remuneration models. We assessed self-reported health status of patients, visit duration, number of visits per year, quality of health service delivery, and quality of health promotion. We used multi-level regressions to study service delivery across socio-economic groups and within each delivery model. Identified disparities were further analysed using a t-test to determine the impact of service delivery model on equity.
Low income individuals were more likely to be women, unemployed, recent immigrants, and in poorer health. These individuals were overrepresented in the Salaried model, reported more visits/year across all models, and tended to report longer visits in the Salaried model. Measures of primary care services generally did not differ significantly between low and higher income/education individuals; when they did, the difference favoured better service delivery for at-risk groups. At-risk patients in the Salaried model were somewhat more likely to report health promotion activities than patients from Capitation and Fee-For-Service models. At-risk patients from Capitation models reported a smaller increase in the number of additional clinic visits/year than Fee-For-Service and Salaried models. At-risk patients reported better first contact accessibility than their non-at-risk counterparts in the Fee-For-Service model only.
Primary care service measures did not differ significantly across socio-economic status or primary care delivery models. In Ontario, capitation-based remuneration is age and sex adjusted only. Patients of low socio-economic status had fewer additional visits compared to those with high socio-economic status under the Capitation model. This raises the concern that Capitation may not support the provision of additional care for more vulnerable groups. Regions undertaking primary care model reforms need to consider the potential impact of the changes on the more vulnerable populations.
PMCID: PMC3927777  PMID: 24341530
Primary care; Health equity; Organizational models; Physician remuneration
18.  Organising unrestricted open access gastroscopy in South Tees. 
Gut  1993;34(3):422-427.
Increasing demand for upper gastrointestinal endoscopy has forced many clinicians to reconsider the policy of seeing all patients in a specialist clinic before gastroscopy. The following are considered essential in setting up an open access gastroscopy service. (1) Assessment of the need by examination of waiting times for the outpatient clinic and the proportion of patients requiring upper gastrointestinal endoscopy, and consultation with colleagues in general practice. During the first 2 years of the service the average waiting time for a medical gastrointestinal outpatient appointment has fallen from over 120 days to 37 days in this area. (2) An adequately staffed and equipped gastrointestinal unit with well motivated nurses (the workload will increase) and sufficient clinical support to allocate patients to the next available gastroscopy list is vital. A safe mechanism for relaying information back to the GP (including histology reports) is essential otherwise medicolegal problems could arise. Open access gastroscopy now accounts for 29% of the total endoscopy workload in South Tees. (3) Close cooperation between medical and surgical gastroenterologists must be achieved to ensure a uniform approach to the provision of this service and equal distribution of the endoscopy workload. This will require close examination of the potential numbers and may necessitate appointment of a clinical assistant or additional consultant. Clinical assistants perform just over 50% of the open access gastroscopies in South Tees and the waiting time has been kept short (average 17 days). (4) A comprehensive request form with guidelines for GPs and a specific box identifying whether the GP requires a report and brief advice only or follow up at the discretion of the endoscopist (often a clinical assistant) is required. (5) Management must be involved in identifying adequate resources. (6) Methods of monitoring requests and outcome measures to ensure effective audit must be established.
PMCID: PMC1374153  PMID: 8472994
19.  Practice size: impact on consultation length, workload, and patient assessment of care. 
BACKGROUND: Variations in practice list size are known to be associated with changes in a number of markers of primary care. Few studies have addressed the issue of how single-handed and smaller practices compare with larger group practices and what might be the optimal size of a general practice. AIM: To examine variations in markers of the nature of the care being provided by practices of various size. DESIGN OF STUDY: Practice profile questionnaire survey. SETTING: A randomised sample of general practitioners (GPs) and practices from two inner-London areas, stratified according to practice size and patients attending the practice over a two-week period. METHOD: Average consultation length was calculated over 200 consecutive consultations. A patient survey using the General Practice Assessment Survey instrument was undertaken in each practice. A practice workload survey was carried out over a two-week period. These outcome measures were examined in relation to five measures of practice size based on total list size and the number of doctors providing care. RESULTS: Out of 202 pratices approached, 54 provided analysable datasets. The patient survey response rate was 7247/11,000 (66%). Smaller practices had shorter average consultation lengths and reduced practice performance scores compared with larger practices. The number of patients corrected for the number of doctors providing care was an important predictor of consultation length in group practices. Responders from smaller practices reported improved accessibility of care and receptionist performance, better continuity of care compared with larger practices, and no disadvantage in relation to 10 other dimensions of care. Practices with smaller numbers of patients per doctor had longer average consultation lengths than those with larger numbers of patients per doctor. CONCLUSION: Defining the optimal size of practice is a complex decision in which the views of doctors, patients, and health service managers may be at variance. Some markers of practice performance are related to the total number of patients cared for, but the practice size corrected for the number of available doctors gives a different perspective on the issue. An oversimplistic approach that fails to account for the views of patients as well as health professionals is likely to be disadvantageous to service planning.
PMCID: PMC1314075  PMID: 11510394
20.  Income development of General Practitioners in eight European countries from 1975 to 2005 
This study aims to gain insight into the international development of GP incomes over time through a comparative approach. The study is an extension of an earlier work (1975–1990, conducted in five yearly intervals). The research questions to be addressed in this paper are: 1) How can the remuneration system of GPs in a country be characterized? 2) How has the annual GP income developed over time in selected European countries? 3) What are the differences in GP incomes when differences in workload are taken into account? And 4) to what extent do remuneration systems, supply of GPs and gate-keeping contribute to the income position of GPs?
Data were collected for Belgium, Denmark, Germany, Finland, France, the Netherlands, Sweden and the United Kingdom. Written sources, websites and country experts were consulted. The data for the years 1995 and 2000 were collected in 2004–2005. The data for 2005 were collected in 2006–2007.
During the period 1975–1990, the income of GPs, corrected for inflation, declined in all the countries under review. During the period 1995–2005, the situation changed significantly: The income of UK GPs rose to the very top position. Besides this, the gap between the top end (UK) and bottom end (Belgium) widened considerably. Practice costs form about 50% of total revenues, regardless of the absolute level of revenues. Analysis based on income per patient leads to a different ranking of countries compared to the ranking based on annual income. In countries with a relatively large supply of GPs, income per hour is lower. The type of remuneration appeared to have no effect on the financial position of the GPs in the countries in this study. In countries with a gate-keeping system the average GP income was systematically higher compared to countries with a direct-access system.
There are substantial differences in the income of GPs among the countries included in this study. The discrepancy between countries has increased over time. The income of British GPs showed a marked increase from 2000 to 2005, due to the introduction of a new contract between the NHS and GPs.
PMCID: PMC2670288  PMID: 19203360
21.  Waiting list management in general practice: a review of orthopaedic patients. 
BMJ : British Medical Journal  1996;312(7035):887-888.
OBJECTIVE: To review all patients on a current general practice orthopaedic waiting list for outpatient appointments with regard to accuracy of the list, clinical priority, and need for further radiological investigation before hospital attendance. DESIGN: Record review by one general practitioner and a radiologist, and discussion with patients of management alternatives. SETTING: Six partner city centre urban fund-holding general practice, list size 8651 (29% low deprivation payment status). SUBJECTS: 116 adults on an orthopaedic waiting list. MAIN OUTCOME MEASURES: List accuracy (patient details and status on waiting list); clinical priority (severity of condition); further investigations (results of tests after radiological review). RESULTS: 32 patients (28%) were removed from the waiting list because of inaccuracies. 14 patients were considered to be high priority and referred to other hospitals by utilising waiting list initiative funds. Of these patients, five agreed to referral to another hospital (treatment completed on average within three months of rereferral), six did not wish to be rereferred, and two did not attend to discuss the offer and remained on the original waiting list. One prioritised patient had further radiological investigations, was reassured, and was taken off the waiting list. 10 patients had further investigations. These resulted in six patients being referred to other hospitals, three being taken off the waiting list, and one seeking private care. CONCLUSIONS: Systematic review of patients on an orthopaedic waiting list of one general practice, though time consuming, led to the identification of inaccuracies in the list and changes in management. Costs need further evaluation, but if the findings occur widely substantial benefits could be achieved for patients.
PMCID: PMC2350605  PMID: 8611882
22.  Nurse telephone triage for same day appointments in general practice: multiple interrupted time series trial of effect on workload and costs 
BMJ : British Medical Journal  2002;325(7374):1214.
To compare the workloads of general practitioners and nurses and costs of patient care for nurse telephone triage and standard management of requests for same day appointments in routine primary care.
Multiple interrupted time series using sequential introduction of experimental triage system in different sites with repeated measures taken one week in every month for 12 months.
Three primary care sites in York.
4685 patients: 1233 in standard management, 3452 in the triage system. All patients requesting same day appointments during study weeks were included in the trial.
Main outcome measures
Type of consultation (telephone, appointment, or visit), time taken for consultation, presenting complaints, use of services during the month after same day contact, and costs of drugs and same day, follow up, and emergency care.
The triage system reduced appointments with general practitioner by 29-44%. Compared with standard management, the triage system had a relative risk (95% confidence interval) of 0.85 (0.72 to 1.00) for home visits, 2.41 (2.08 to 2.80) for telephone care, and 3.79 (3.21 to 4.48) for nurse care. Mean overall time in the triage system was 1.70 minutes longer, but mean general practitioner time was reduced by 2.45 minutes. Routine appointments and nursing time increased, as did out of hours and accident and emergency attendance. Costs did not differ significantly between standard management and triage: mean difference £1.48 more per patient for triage (95% confidence interval –0.19 to 3.15).
Triage reduced the number of same day appointments with general practitioners but resulted in busier routine surgeries, increased nursing time, and a small but significant increase in out of hours and accident and emergency attendance. Consequently, triage does not reduce overall costs per patient for managing same day appointments.
What is already known on this topicNurse telephone triage is used to manage the increasing demand for same day appointments in general practiceEvidence that nurse telephone triage is effective is limitedWhat this study addsTriage resulted in 29-44% fewer same day appointments with general practitioners than standard managementNursing and overall time increased in the triage group as 40% of patients were managed by nursesTriage was not less costly than standard management because of increased costs for nursing, follow up, out of hours, and accident and emergency care
PMCID: PMC135495  PMID: 12446539
23.  Effectiveness of a physiotherapy-initiated telephone triage of orthopedic waitlist patients 
There is generally a lengthy wait on outpatient orthopedic waiting lists in Australian public hospitals to consult a specialist. Patients then wait again for surgery, if required. Patients with higher need are rarely prioritized, and there is the potential for increased morbidity for those who wait. There is generally no option of alternative care whilst waiting. This paper compares historical orthopedic outpatient clinic data with the outcomes of a physiotherapy-led initiative in one large Australian tertiary hospital.
Two physiotherapists working within-scope conducted a telephone triage (October to December 2010) using a standard instrument for all new patients on the orthopedic waiting list. They were offered primary treatment options of retaining their appointment, being discharged, referral to a new model of assessment (multidisciplinary specialist clinic), or referral to physiotherapy. The outcomes were costs of the service, waiting time, and percentage of patients taking up management options. This was compared with a historical sample of new patients on the orthopedic waiting list (January to March 2009), whose treatment consumption was tracked longitudinally.
The telephone triage resulted in 16.4% patients being discharged directly (compared with 0.1% comparison sample). For approximately AU$17.00 per patient, the telephone triage process released 21 booked appointments on the outpatient clinic waiting list. Moreover, approximately 26% patients were referred directly to physiotherapy, which was not a primary management option in the comparison sample. The waiting time for an appointment, for those patients who remained on the waiting list, was significantly shorter for the telephone triage sample than the comparison sample. There were significantly higher rates of failure to attend appointments, and significantly lower rates of discharge, in the comparison sample, than the telephone triage sample.
A physiotherapist-led intervention offering alternative management options whilst patients waited for an orthopedic outpatient clinic consultation appears to be cost-effective, and patient-centered.
PMCID: PMC3417931  PMID: 22915976
extended scope practice; orthopedics; evaluation
24.  Do nursing home residents make greater demands on GPs? A prospective comparative study. 
BACKGROUND: The number of people residing in nursing homes has increased. General practitioners (GPs) receive an increased capitation fee for elderly patients in recognition of their higher consultation rate. However, there is no distinction between elderly patients residing in nursing homes and those in the community. AIM: To determine whether nursing home residents receive greater general practice input than people residing in the community. METHOD: Prospective comparative study of all 345 residents of eight nursing homes in Glasgow and a 2:1 age, sex, and GP matched comparison group residing in the community. A comparison of contacts with primary care over three months in terms of frequency, nature, length, and outcome was carried out. RESULTS: Nursing home residents received more total contacts with primary care staff (P < 0.0001) and more face-to-face consultations with GPs (P < 0.0001). They were more likely to be seen as an emergency (P < 0.01) but were no more likely to be referred to hospital, and were less likely to be followed-up by their GP (P < 0.0001). Although individual consultations with nursing home residents were shorter than those with the community group (P < 0.0001), the overall time spent consulting with them was longer (P < 0.001). This equated to an additional 28 minutes of time per patient per annum. Some of this time would have been offset by less time spent travelling, since 61% of nursing home consultations were done during the same visit as other consultations, compared with only 3% of community consultations (P < 0.0001). CONCLUSION: Our study suggests that nursing home residents do require a greater input from general practice than people of the same age and sex who are residing in the community. While consideration may be given to greater financial reimbursement of GPs who provide medical care to nursing home residents, consideration should also be given to restructuring the medical cover for nursing home residents. This would result in a greater scope for proactive and preventive interventions and for consulting with several patients during one visit.
PMCID: PMC1313470  PMID: 10621985
25.  Evaluation of specialists' outreach clinics in general practice in England: process and acceptability to patients, specialists, and general practitioners. 
OBJECTIVES: The wider study aimed to evaluate specialists' outreach clinics in relation to their costs, processes, and effectiveness, including patients' and professionals' attitudes. The data on processes and attitudes are presented here. DESIGN: Self administered questionnaires were drawn up for patients, their general practitioners (GPs) and specialists, and managers in the practice. Information was sought from hospital trusts. The study formed a pilot phase prior to a wider evaluation. SETTING: Nine outreach clinics in general practices in England, each with a hospital outpatient department as a control clinic were studied. SUBJECTS: The specialties included were ear, nose, and throat surgery; rheumatology; and gynaecology. The subjects were the patients who attended either the outreach clinics or hospital outpatients clinics during the study period, the outreach patients' GPs, the outreach patients' and outpatients' specialists, the managers in the practices, and the NHS trusts which employed the specialists. MAIN OUTCOME MEASURES: Process items included waiting lists, waiting times in clinics, number of follow up visits, investigations and procedures performed, treatment, health status, patients' and specialists' travelling times, and patients' and doctors' attitudes to, and satisfaction with, the clinic. RESULTS: There was no difference in the health status of patients in relation to the clinic site (ie, outreach and hospital outpatients' clinics) at baseline, and all but one of the specialists said there were no differences in casemix between their outreach and outpatients' clinics. Patients preferred, and were more satisfied with, care in specialists' outreach clinics in general practice, in comparison with outpatients' clinics. The outreach clinics were rated as more convenient than outpatients' clinics in relation to journey times; those outreach patients in work lost less time away from work than outpatients' clinic patients due to the clinic attendance. Length of time on the waiting list was significantly reduced for gynaecology patients; waiting times in clinics were lower for outreach patients than outpatients across all specialties. In addition, outreach patients were more likely to be first rather than follow up attenders; rheumatology outreach patients were more likely than hospital outpatients to receive therapy. GPs' referrals to hospital outpatients' clinics were greatly reduced by the availability of outreach clinics. Both specialists and GPs saw the main advantages of outreach clinics in relation to the greater convenience and better access to care for patients. Few of the specialists and GPs in the outreach practices held formal training and education sessions in the outreach clinic, although over half of the GPs felt that their skills/expertise had broadened as a result of the outreach clinic. CONCLUSIONS: The processes of care (waiting times, patient satisfaction, convenience to patients, follow up attendances) were better in outreach than in outpatients' clinics. However, waiting lists were only significantly reduced for gynaecology patients, despite both GPs and consultants reporting reduced waiting lists for patients as one of the main advantages of outreach. Whether these improvements merit the increased cost to the specialists (in terms of their increased travelling times and time spent away from their hospital base) and whether the development of what is, in effect, two standards of care between practices with and without outreach can be stemmed and the standard of care raised in all practices (eg, by sharing outreach clinics between GPs in an area) remain the subject of debate. As the data were based on the pilot study, the results should be viewed with some caution, although statistical power was adequate for comparisons of sites if not specialties.
PMCID: PMC1060410  PMID: 9135789

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