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1.  Can Broader Diffusion of Value-Based Insurance Design Increase Benefits from US Health Care without Increasing Costs? Evidence from a Computer Simulation Model 
PLoS Medicine  2010;7(2):e1000234.
Using a computer simulation based on US data, R. Scott Braithwaite and colleagues calculate the benefits of value-based insurance design, in which patients pay less for highly cost-effective services.
Background
Evidence suggests that cost sharing (i.e.,copayments and deductibles) decreases health expenditures but also reduces essential care. Value-based insurance design (VBID) has been proposed to encourage essential care while controlling health expenditures. Our objective was to estimate the impact of broader diffusion of VBID on US health care benefits and costs.
Methods and Findings
We used a published computer simulation of costs and life expectancy gains from US health care to estimate the impact of broader diffusion of VBID. Two scenarios were analyzed: (1) applying VBID solely to pharmacy benefits and (2) applying VBID to both pharmacy benefits and other health care services (e.g., devices). We assumed that cost sharing would be eliminated for high-value services (<$100,000 per life-year), would remain unchanged for intermediate- or unknown-value services ($100,000–$300,000 per life-year or unknown), and would be increased for low-value services (>$300,000 per life-year). All costs are provided in 2003 US dollars. Our simulation estimated that approximately 60% of health expenditures in the US are spent on low-value services, 20% are spent on intermediate-value services, and 20% are spent on high-value services. Correspondingly, the vast majority (80%) of health expenditures would have cost sharing that is impacted by VBID. With prevailing patterns of cost sharing, health care conferred 4.70 life-years at a per-capita annual expenditure of US$5,688. Broader diffusion of VBID to pharmaceuticals increased the benefit conferred by health care by 0.03 to 0.05 additional life-years, without increasing costs and without increasing out-of-pocket payments. Broader diffusion of VBID to other health care services could increase the benefit conferred by health care by 0.24 to 0.44 additional life-years, also without increasing costs and without increasing overall out-of-pocket payments. Among those without health insurance, using cost saving from VBID to subsidize insurance coverage would increase the benefit conferred by health care by 1.21 life-years, a 31% increase.
Conclusion
Broader diffusion of VBID may amplify benefits from US health care without increasing health expenditures.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
More money is spent per person on health care in the US than in any other country. US health care expenditure accounts for 16.2% of the gross domestic product and this figure is rising. Indeed, the increase in health care costs is outstripping the economy's growth rate. Consequently, US policy makers and providers of health insurance—health care in the US is largely provided by the private sector and is paid for through private health insurance or through government programs such as Medicare and Medicaid—are looking for better ways to control health expenditures. Although some health care cost reductions can be achieved by increasing efficiency, controlling the quantity of health care consumed is an essential component of strategies designed to reduce health expenditures. These strategies can target health care providers (for example, by requiring primary care physicians to provide referrals before their patients' insurance provides cover for specialist care) or can target consumers, often through cost sharing. Nowadays, most insurance plans include several tiers of cost sharing in which patients pay a larger proportion of the costs of expensive interventions than of cheap interventions.
Why Was This Study Done?
Cost sharing decreases health expenditure but it can also reduce demand for essential care and thus reduce the quality of care. Consequently, some experts have proposed value-based insurance design (VBID), an approach in which the amount of cost sharing is set according to the “value” of an intervention rather than its cost. The value of an intervention is defined as the ratio of the additional benefits to the additional costs of the intervention when compared to the next best alternative intervention. Under VBID, cost sharing could be waived for office visits necessary to control blood pressure in people with diabetes, which deliver high-value care, but could be increased for high-tech scans for dementia, which deliver low-value care. VBID has been adopted by several private health insurance schemes and its core principal is endorsed by US policy makers. However, it is unclear whether wider use of VBID is warranted. In this study, the researchers use a computer simulation of the US health care system to estimate the impact of broader diffusion of VBID on US health care benefits and costs.
What Did the Researchers Do and Find?
The researchers used their computer simulation to estimate the impact of applying VBID to cost sharing for drugs alone and to cost sharing for drugs, procedures, and other health care services for one million hypothetical US patients. In their simulation, the researchers eliminated cost sharing for services that cost less than US$100,000 per life-year gained (high-value services) and increased cost-sharing for services that cost more than US$300,000 per life-year gained (low-value services); cost-sharing remained unchanged for intermediate- or unknown-value services. With the current pattern of cost sharing, 60% of health expenditure is spent on low-value services and health care increases life expectancy by 4.70 years for an annual per person expenditure of US$5,688, the researchers report. With widespread application of VBID to cost sharing for drugs alone, health care increased life expectancy by an additional 0.03 to 0.05 years without increasing costs. With widespread application of VBID to cost sharing for other health care services, health care increased life expectancy by a further 0.24 to 0.44 years without additional costs. Finally, if the costs saved by applying VBID were used to subsidize insurance for the 15% of the US population currently without health insurance, the benefit conferred by health care among these people would increase by 1.21 life-years.
What Do These Findings Mean?
The findings of this study depend on the many assumptions included in the computer simulation, which, although complex, is a greatly simplified representation of the US health care system. Nevertheless, these findings suggest that if VBID were used more widely within the US health care system to encourage the use of high-value services, it might be possible to amplify the benefits from US health care without increasing health expenditures. Importantly, the money saved by VBID could be used to help fund universal insurance, a central aim of US health care reform. More research is needed, however, to determine the value of various health care interventions and to investigate whether other ways of linking value to cost sharing might yield even better gains in life expectancy at little or no additional cost.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000234.
Wikipedia has a page on health care in the United States (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
Families USA works to promote high-quality affordable health care for all Americans and provides information about all aspects of US health care and about US health care reforms
The US Centers for Medicare and Medicaid provides information on the major government health insurance programs and on US national health expenditure statistics
doi:10.1371/journal.pmed.1000234
PMCID: PMC2821897  PMID: 20169114
2.  What Are the Consequences of Waiting for Health Care in the Veteran Population? 
Journal of General Internal Medicine  2011;26(Suppl 2):676-682.
ABSTRACT
National health reform is expected to increase how long individuals have to wait between requests for appointments and when their appointment is scheduled. The increase in demand for care due to more widespread insurance will result in longer waits if there is not also a concomitant increase in supply of healthcare services. Long waits for healthcare are hypothesized to compromise health because less frequent outpatient visits result in delays in diagnosis and treatment. Research testing this hypothesis is scarce due to a paucity of data on how long individuals wait for healthcare in the United States. The main exception is the Veterans Health Administration (VA) that has been routinely collecting data on how long veterans wait for outpatient care for over a decade. This narrative review summarizes the results of studies using VA wait time data to answer two main questions: 1) How much do longer wait times decrease healthcare utilization and 2) Do longer wait times cause poorer health outcomes? Longer VA wait times lead to small, yet statistically significant decreases in utilization and are related to poorer health in elderly and vulnerable veteran populations. Both long-term outcomes (e.g. mortality, preventable hospitalizations) and intermediate outcomes such as hemoglobin A1C levels are worse for veterans who seek care at facilities with longer waits compared to veterans who visit facilities with shorter waits. Further research is needed on the mechanisms connecting longer wait times and poorer outcomes including identifying patient sub-populations whose risks are most sensitive to delayed access to care. If wait times increase for the general patient population with the implementation of national reform as expected, U.S. healthcare policymakers and clinicians will need to consider policies and interventions that minimize potential harms for all patients.
doi:10.1007/s11606-011-1819-1
PMCID: PMC3191224  PMID: 21989621
wait times; health outcomes; chronic conditions; health care utilization; VA
3.  Does Wait-List Size at Registration Influence Time to Surgery? Analysis of a Population-Based Cardiac Surgery Registry 
Health Services Research  2006;41(1):23-39.
Objective
To determine whether the probability of undergoing coronary bypass surgery within a certain time was related to the number of patients on the wait list at registration for the operation in a publicly funded health system.
Methods
A prospective cohort study comparing waiting times among patients registered on wait lists at the hospitals delivering adult cardiac surgery. For each calendar week, the list size, the number of new registrations, and the number of direct admissions immediately after angiography characterized the demand for surgery.
Results
The length of delay in undergoing treatment was associated with list size at registration, with shorter times for shorter lists (log-rank test 1,198.3, p<.0001). When the list size at registration required clearance time over 1 week patients had 42 percent lower odds of undergoing surgery compared with lists with clearance time less than 1 week (odds ratio [OR] 0.58 percent, 95 percent, confidence interval [CI] 0.53–0.63), after adjustment for age, sex, comorbidity, period, and hospital. The weekly number of new registrations exceeding weekly service capacity had an independent effect toward longer service delays when the list size at registration required clearance time less than 1 week (OR 0.56 percent, 95 percent CI 0.45–0.71), but not for longer lists. Every time the operation was performed for a patient requiring surgery without registration on wait lists, the odds of surgery for listed patients were reduced by 6 percent (OR 0.94, CI 0.93–0.95).
Conclusion
For wait-listed patients, time to surgery depends on the list size at registration, the number of new registrations, as well as on the weekly number of patients who move immediately from angiography to coronary bypass surgery without being registered on a wait list. Hospital managers may use these findings to improve resource planning and to reduce uncertainty when providing advice on expected treatment delays.
doi:10.1111/j.1475-6773.2005.00459.x
PMCID: PMC1681524  PMID: 16430599
Access to health care; surgical procedures; elective surgery; waiting lists; delay in treatment; patient admission; registries; cohort studies
4.  Evaluating Drug Prices, Availability, Affordability, and Price Components: Implications for Access to Drugs in Malaysia 
PLoS Medicine  2007;4(3):e82.
Background
Malaysia's stable health care system is facing challenges with increasing medicine costs. To investigate these issues a survey was carried out to evaluate medicine prices, availability, affordability, and the structure of price components.
Methods and Findings
The methodology developed by the World Health Organization (WHO) and Health Action International (HAI) was used. Price and availability data for 48 medicines was collected from 20 public sector facilities, 32 private sector retail pharmacies and 20 dispensing doctors in four geographical regions of West Malaysia. Medicine prices were compared with international reference prices (IRPs) to obtain a median price ratio. The daily wage of the lowest paid unskilled government worker was used to gauge the affordability of medicines. Price component data were collected throughout the supply chain, and markups, taxes, and other distribution costs were identified. In private pharmacies, innovator brand (IB) prices were 16 times higher than the IRPs, while generics were 6.6 times higher. In dispensing doctor clinics, the figures were 15 times higher for innovator brands and 7.5 for generics. Dispensing doctors applied high markups of 50%–76% for IBs, and up to 316% for generics. Retail pharmacy markups were also high—25%–38% and 100%–140% for IBs and generics, respectively. In the public sector, where medicines are free, availability was low even for medicines on the National Essential Drugs List. For a month's treatment for peptic ulcer disease and hypertension people have to pay about a week's wages in the private sector.
Conclusions
The free market by definition does not control medicine prices, necessitating price monitoring and control mechanisms. Markups for generic products are greater than for IBs. Reducing the base price without controlling markups may increase profits for retailers and dispensing doctors without reducing the price paid by end users. To increase access and affordability, promotion of generic medicines and improved availability of medicines in the public sector are required.
Drug price and availability data were collected from West Malaysian public sector facilities, private sector retail pharmacies, and dispensing doctors. Mark-ups were higher on generic drugs than on innovator brands.
Editors' Summary
Background.
The World Health Organization has said that one-third of the people of the world cannot access the medicines they need. An important reason for this problem is that prices are often too high for people or government-funded health systems to afford. In developing countries, most people who need medicines have to pay for them out of their own pockets. Where the cost of drugs is covered by health systems, spending on medicines is a major part of the total healthcare budget. Governments use a variety of approaches to try to control the cost of drugs and make sure that essential medicines are affordable and not overpriced. According to the theory of “free market economics,” the costs of goods and services are determined by interactions between buyers and sellers and not by government intervention. However, free market economics does not work well at containing the costs of medicines, particularly new medicines, because new medicines are protected by patent law, which legally prevents others from making, using, or selling the medicine for a particular period of time. Therefore, without government intervention, there is nothing to help to push down prices.
Why Was This Study Done?
Malaysia is a middle-income country with a relatively effective public health system, but it is facing a rapid rise in drug costs. In Malaysia, medicine prices are determined by free-market economics, without any control by government. Government hospitals are expected to provide drugs free, but a substantial proportion of medicines are paid for by patients who buy them directly from private pharmacies or prescribing doctors. There is evidence that Malaysian patients have difficulties accessing the drugs they need and that cost is an important factor. Therefore, the researchers who wrote this paper wanted to examine the cost of different medicines in Malaysia, and their availability and affordability from different sources.
What Did the Researchers Do and Find?
In this research project, 48 drugs were studied, of which 28 were part of a “core list” identified by the World Health Organization as “essential drugs” on the basis of the global burden of disease. The remaining 20 reflected health care needs in Malaysia itself. The costs of each medicine were collected from government hospitals, private pharmacies, and dispensing doctors in four different regions of Malaysia. Data were collected for the “innovator brand” (made by the original patent holder) and for “generic” brands (an equivalent drug to the innovator brand, produced by a different company once the innovator brand no longer has an exclusive patent). The medicine prices were compared against international reference prices (IRP), which are the average prices offered by not-for-profit drug companies to developing countries. Finally, the researchers also compared the cost of the drugs with daily wages, in order to work out their “affordability.”
The researchers found that, irrespective of the source of medicines, prices were on average very much higher than the international reference price, ranging from 2.4 times the IRP for innovator brands accessed through public hospitals, to 16 times the IRP for innovator brands accessed through private pharmacies. The availability of medicines was also very poor, with only 25% of generic medicines available on average through the public sector. The affordability of many of the medicines studied was again very poor. For example, one month's supply of ranitidine (a drug for stomach ulcers) was equivalent to around three days' wages for a low-paid government worker, and one month's supply of fluoxetine (an antidepressant) would cost around 26 days' wages.
What Do These Findings Mean?
These results show that essential drugs are very expensive in Malaysia and are not universally available. Many people would not be able to pay for essential medicines. The cost of medicines in Malaysia seems to be much higher than in areas of India and Sri Lanka, although the researchers did not attempt to collect data in order to carry out an international comparison. It is possible that the high cost and low availability in Malaysia are the result of a lack of government regulation. Overall, the findings suggest that the government should set up mechanisms to prevent drug manufacturers from increasing prices too much and thus ensure greater access to essential medicines.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040082.
Read a related PLoS Medicine Perspective article by Suzanne Hill
Information is available from the World Health Organization on Improving Access to Medicines
Information on medicine prices is available from Health Action International
Wikipedia has an entry on Patent (a type of intellectual property that is normally used to prevent other companies from selling a newly invented medicine). (Wikipedia is an internet encyclopedia anyone can edit.)
The Drugs for Neglected Diseases Initiative is an international collaboration between public organizations that aims to develop drugs for people suffering from neglected diseases
doi:10.1371/journal.pmed.0040082
PMCID: PMC1831730  PMID: 17388660
5.  Oral Health Care Reform in Finland – aiming to reduce inequity in care provision 
BMC Oral Health  2008;8:3.
Background
In Finland, dental services are provided by a public (PDS) and a private sector. In the past, children, young adults and special needs groups were entitled to care and treatment from the public dental services (PDS). A major reform in 2001 – 2002 opened the PDS and extended subsidies for private dental services to all adults. It aimed to increase equity by improving adults' access to oral health care and reducing cost barriers. The aim of this study was to assess the impacts of the reform on the utilization of publicly funded and private dental services, numbers and distribution of personnel and costs in 2000 and in 2004, before and after the oral health care reform. An evaluation was made of how the health political goals of the reform: integrating oral health care into general health care, improving adults' access to care and lowering cost barriers had been fulfilled during the study period.
Methods
National registers were used as data sources for the study. Use of dental services, personnel resources and costs in 2000 (before the reform) and in 2004 (after the reform) were compared.
Results
In 2000, when access to publicly subsidised dental services was restricted to those born in 1956 or later, every third adult used the PDS or subsidised private services. By 2004, when subsidies had been extended to the whole adult population, this increased to almost every second adult. The PDS reported having seen 118 076 more adult patients in 2004 than in 2000. The private sector had the same number of patients but 542 656 of them had not previously been entitled to partial reimbursement of fees.
The use of both public and subsidised private services increased most in big cities and urban municipalities where access to the PDS had been poor and the number of private practitioners was high. The PDS employed more dentists (6.5%) and the number of private practitioners fell by 6.9%. The total dental care expenditure (PDS plus private) increased by 21% during the study period. Private patients who had previously not been entitled to reimbursements seemed to gain most from the reform.
Conclusion
The results of this study indicate that implementation of a substantial reform, that changes the traditionally defined tasks of the public and private sectors in an established oral health care provision system, proceeds slowly, is expensive and probably requires more stringent steering than was the case in Finland 2001 – 2004. However, the equity and fairness of the oral health care provision system improved and access to services and cost-sharing improved slightly.
doi:10.1186/1472-6831-8-3
PMCID: PMC2268684  PMID: 18226197
6.  Evaluation of supply-side initiatives to improve access to coronary bypass surgery 
Background
Guided by the evidence that delaying coronary revascularization may lead to symptom worsening and poorer clinical outcomes, expansion in cardiac surgery capacity has been recommended in Canada. Provincial governments started providing one-time and recurring increases in budgets for additional open heart surgeries to reduce waiting times. We sought to determine whether the year of decision to proceed with non-emergency coronary bypass surgery had an effect on time to surgery.
Methods
Using records from a population-based registry, we studied times between decision to operate and the procedure itself. We estimated changes in the length of time that patients had to wait for non-emergency operation over 14 calendar periods that included several years when supplementary funding was available. We studied waiting times separately for patients who access surgery through a wait list and through direct admission.
Results
During two periods when supplementary funding was available, 1998–1999 and 2004–2005, the weekly rate of undergoing surgery from a wait list was, respectively, 50% and 90% higher than in 1996–1997, the period with the longest waiting times. We also observed a reduction in the difference between 90th and 50th percentiles of the waiting-time distributions. Forty percent of patients in the 1998, 1999, 2004 and 2005 cohorts (years when supplementary funding was provided) underwent surgery within 16 to 20 weeks following the median waiting time, while it took between 27 and 37 weeks for the cohorts registered in the years when supplementary funding was not available. Times between decision and surgery were shorter for direct admissions than for wait-listed patients. Among patients who were directly admitted to hospital, time between decision and surgery was longest in 1992–1993 and then has been steadily decreasing through the late nineties. The rate of surgery among these patients was the highest in 1998–1999, and has not changed afterwards, even for years when supplementary funding was provided.
Conclusions
Waiting times for non-emergency coronary bypass surgery shortened after supplementary funding was granted to increase volume of cardiac surgical care in a health system with publicly-funded universal coverage for the procedure. The effect of the supplementary funding was not uniform for patients that access the services through wait lists and through direct admission.
doi:10.1186/1472-6963-12-311
PMCID: PMC3515401  PMID: 22963283
Access to care; CABG; Surgical wait lists; Provincial registry; Health policy
7.  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.
Introduction
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.
doi:10.4066/AMJ.2011.837
PMCID: PMC3562880  PMID: 23386870
8.  NHS waiting lists and evidence of national or local failure: analysis of health service data 
BMJ : British Medical Journal  2003;326(7382):188.
Objectives
To investigate the national distribution of prolonged waiting for elective day case and inpatient surgery, and to examine associations of prolonged waiting with markers of NHS capacity, activity in the independent sector, and need.
Setting
NHS hospital trusts in England.
Population
People waiting for elective treatment in the specialties of general surgery; ear, nose and throat surgery; ophthalmic surgery; and trauma and orthopaedic surgery.
Main outcome measure
Numbers of people waiting six months or longer (prolonged waiting). Characteristics of trusts with large numbers waiting six months or longer were examined by using logistic regression.
Results
The distribution of numbers of people waiting for day case or elective surgery in all the specialties examined was highly positively skewed. Between 52% and 83% of patients waiting longer than six months in the specialties studied were found in one quarter of trusts, which in turn contributed 23-45% of the national throughput specific to the specialty. In general, there was little evidence to show that capacity (measured by numbers of operating theatres, dedicated day case theatres, available beds, and bed occupancy rate) or independent sector activity were associated with prolonged waiting, although exceptions were noted for individual specialties. There was consistent evidence showing an increase in prolonged waiting, with increased numbers of anaesthetists across all specialties and with increased bed occupancy rates for ear, nose and throat surgery. Markers of greater need for health care, such as deprivation score and rate of limiting long term illness, were inversely associated with prolonged waiting.
Conclusion
In most instances, substantial numbers of patients waiting unacceptably long periods for elective surgery were limited to a small number of hospitals. Little and inconsistent support was found for associations of prolonged waiting with markers of capacity, independent sector activity, or need in the surgical specialties examined.
What is already known about this topicMany patients wait unacceptably long times for NHS surgeryThe size of waiting lists is of little relevance to understanding access to treatmentEvidence is scant for the common assumption that the waiting problem arises from a global mismatch between supply and demand and can be solved either by greater rationing or by increasing NHS capacityWhat this study addsLong waiting lists are not an indication of a general failure of the NHSOne quarter of hospital trusts contribute between half and four fifths of the patients waiting six months or longerMeasures of capacity (such as beds, operating theatres, doctors) and independent sector activity are not generally associated with prolonged waiting
PMCID: PMC140273  PMID: 12543833
9.  A model to prioritize access to elective surgery on the basis of clinical urgency and waiting time 
Background
Prioritization of waiting lists for elective surgery represents a major issue in public systems in view of the fact that patients often suffer from consequences of long waiting times. In addition, administrative and standardized data on waiting lists are generally lacking in Italy, where no detailed national reports are available. This is true although since 2002 the National Government has defined implicit Urgency-Related Groups (URGs) associated with Maximum Time Before Treatment (MTBT), similar to the Australian classification. The aim of this paper is to propose a model to manage waiting lists and prioritize admissions to elective surgery.
Methods
In 2001, the Italian Ministry of Health funded the Surgical Waiting List Info System (SWALIS) project, with the aim of experimenting solutions for managing elective surgery waiting lists. The project was split into two phases. In the first project phase, ten surgical units in the largest hospital of the Liguria Region were involved in the design of a pre-admission process model. The model was embedded in a Web based software, adopting Italian URGs with minor modifications. The SWALIS pre-admission process was based on the following steps: 1) urgency assessment into URGs; 2) correspondent assignment of a pre-set MTBT; 3) real time prioritization of every referral on the list, according to urgency and waiting time. In the second project phase a prospective descriptive study was performed, when a single general surgery unit was selected as the deployment and test bed, managing all registrations from March 2004 to March 2007 (1809 ordinary and 597 day cases). From August 2005, once the SWALIS model had been modified, waiting lists were monitored and analyzed, measuring the impact of the model by a set of performance indexes (average waiting time, length of the waiting list) and Appropriate Performance Index (API).
Results
The SWALIS pre-admission model was used for all registrations in the test period, fully covering the case mix of the patients referred to surgery. The software produced real time data and advanced parameters, providing patients and users useful tools to manage waiting lists and to schedule hospital admissions with ease and efficiency. The model protected patients from horizontal and vertical inequities, while positive changes in API were observed in the latest period, meaning that more patients were treated within their MTBT.
Conclusion
The SWALIS model achieves the purpose of providing useful data to monitor waiting lists appropriately. It allows homogeneous and standardized prioritization, enhancing transparency, efficiency and equity. Due to its applicability, it might represent a pragmatic approach towards surgical waiting lists, useful in both clinical practice and strategic resource management.
doi:10.1186/1472-6963-9-1
PMCID: PMC2651867  PMID: 19118494
10.  Comparative Performance of Private and Public Healthcare Systems in Low- and Middle-Income Countries: A Systematic Review 
PLoS Medicine  2012;9(6):e1001244.
A systematic review conducted by Sanjay Basu and colleagues reevaluates the evidence relating to comparative performance of public versus private sector healthcare delivery in low- and middle-income countries.
Introduction
Private sector healthcare delivery in low- and middle-income countries is sometimes argued to be more efficient, accountable, and sustainable than public sector delivery. Conversely, the public sector is often regarded as providing more equitable and evidence-based care. We performed a systematic review of research studies investigating the performance of private and public sector delivery in low- and middle-income countries.
Methods and Findings
Peer-reviewed studies including case studies, meta-analyses, reviews, and case-control analyses, as well as reports published by non-governmental organizations and international agencies, were systematically collected through large database searches, filtered through methodological inclusion criteria, and organized into six World Health Organization health system themes: accessibility and responsiveness; quality; outcomes; accountability, transparency, and regulation; fairness and equity; and efficiency. Of 1,178 potentially relevant unique citations, data were obtained from 102 articles describing studies conducted in low- and middle-income countries. Comparative cohort and cross-sectional studies suggested that providers in the private sector more frequently violated medical standards of practice and had poorer patient outcomes, but had greater reported timeliness and hospitality to patients. Reported efficiency tended to be lower in the private than in the public sector, resulting in part from perverse incentives for unnecessary testing and treatment. Public sector services experienced more limited availability of equipment, medications, and trained healthcare workers. When the definition of “private sector” included unlicensed and uncertified providers such as drug shop owners, most patients appeared to access care in the private sector; however, when unlicensed healthcare providers were excluded from the analysis, the majority of people accessed public sector care. “Competitive dynamics” for funding appeared between the two sectors, such that public funds and personnel were redirected to private sector development, followed by reductions in public sector service budgets and staff.
Conclusions
Studies evaluated in this systematic review do not support the claim that the private sector is usually more efficient, accountable, or medically effective than the public sector; however, the public sector appears frequently to lack timeliness and hospitality towards patients.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Health care can be provided through public and private providers. Public health care is usually provided by the government through national healthcare systems. Private health care can be provided through “for profit” hospitals and self-employed practitioners, and “not for profit” non-government providers, including faith-based organizations.
There is considerable ideological debate around whether low- and middle-income countries should strengthen public versus private healthcare services, but in reality, most low- and middle-income countries use both types of healthcare provision. Recently, as the global economic recession has put major constraints on government budgets—the major funding source for healthcare expenditures in most countries—disputes between the proponents of private and public systems have escalated, further fuelled by the recommendation of International Monetary Fund (an international finance institution) that countries increase the scope of private sector provision in health care as part of loan conditions to reduce government debt. However, critics of the private health sector believe that public healthcare provision is of most benefit to poor people and is the only way to achieve universal and equitable access to health care.
Why Was This Study Done?
Both sides of the public versus private healthcare debate draw on selected case reports to defend their viewpoints, but there is a widely held view that the private health system is more efficient than the public health system. Therefore, in order to inform policy, there is an urgent need for robust evidence to evaluate the quality and effectiveness of the health care provided through both systems. In this study, the authors reviewed all of the evidence in a systematic way to evaluate available data on public and private sector performance.
What Did the Researchers Do and Find?
The researchers used eight databases and a comprehensive key word search to identify and review appropriate published data and studies of private and public sector performance in low- and middle-income countries. They assessed selected studies against the World Health Organization's six essential themes of health systems—accessibility and responsiveness; quality; outcomes; accountability, transparency, and regulation; fairness and equity; and efficiency—and conducted a narrative review of each theme.
Out of the 102 relevant studies included in their comparative analysis, 59 studies were research studies and 13 involved meta-analysis, with the rest involving case reports or reviews. The researchers found that study findings varied considerably across countries studied (one-third of studies were conducted in Africa and a third in Southeast Asia) and by the methods used.
Financial barriers to care (such as user fees) were reported for both public and private systems. Although studies report that patients in the private sector experience better timeliness and hospitality, studies suggest that providers in the private sector more frequently violate accepted medical standards and have lower reported efficiency.
What Do These Findings Mean?
This systematic review did not support previous views that private sector delivery of health care in low- and middle-income settings is more efficient, accountable, or effective than public sector delivery. Each system has its strengths and weaknesses, but importantly, in both sectors, there were financial barriers to care, and each had poor accountability and transparency. This systematic review highlights a limited and poor-quality evidence base regarding the comparative performance of the two systems.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001244.
A previous PLoS Medicine study examined the outpatient care provided by the public and private sector in low-income countries
The WHO website provides more information on healthcare systems
The World Bank website provides information on health system financing
Oxfam provides an argument against increased private health care in poor countries
doi:10.1371/journal.pmed.1001244
PMCID: PMC3378609  PMID: 22723748
11.  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
12.  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.
Learnings
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.
doi:10.1186/1472-6963-10-303
PMCID: PMC2991304  PMID: 21050488
13.  Determinants of Unacceptable Waiting Times for Specialized Services in Canada 
Healthcare Policy  2007;2(3):e140-e154.
Background:
Much of the current evidence regarding timely access to healthcare services focuses on the duration of the waiting time as the principal determinant of wait time acceptability. We conducted the first national-level analysis of wait time acceptability in Canada to identify the determinants of unacceptable waits for specialized healthcare services, including selected demographic and socio-economic variables.
Methods:
We analyzed data reported by respondents to a national survey on access to healthcare services who accessed specialized services (i.e., specialist visits, non-emergency surgery and selected diagnostic tests) during a 12-month period. We used univariate analyses and weighted logistic regression to examine the relation between wait time acceptability and selected demographic, socio-economic and health status factors for each specialized service.
Results:
Between 17% and 29% of patients who waited for a specialized service declared that their waiting time was unacceptable. Most individuals reported waiting less than 3 months for their services. Between 10% and 19% of those who waited indicated that waiting for care affected their lives. Results of the logistic regression analyses showed that longer waits and adverse experiences during the waiting period were significantly associated with higher odds of reporting an unacceptable waiting time for all three types of specialized services. The role of socio-economic and demographic factors on wait time acceptability was varied. Individuals with lower education were consistently less likely to consider their waiting times unacceptable. Patients less than 65 years of age were more likely to consider their waiting times unacceptable for specialist visits and diagnostic tests.
Interpretation:
Our study shows that the primary determinants of waiting time acceptability are the length of the waiting time and the effects of waiting on the patient’s life. In addition, some patient characteristics, such as age and education, may play a role, pointing to the potential role of patient expectations in determining the acceptability of waits for specialized services.
PMCID: PMC2585450  PMID: 19305710
14.  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
15.  Left Ventricular Assist Devices 
Executive Summary
Objective
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.
Conclusion
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
16.  Equity, waiting times, and NHS reforms: retrospective study 
Objective To determine whether observable changes in waiting times occurred for certain key elective procedures between 1997 and 2007 in the English National Health Service and to analyse the distribution of those changes between socioeconomic groups as an indicator of equity.
Design Retrospective study of population-wide, patient level data using ordinary least squares regression to investigate the statistical relation between waiting times and patients’ socioeconomic status.
Setting English NHS from 1997 to 2007.
Participants 427 277 patients who had elective knee replacement, 406 253 who had elective hip replacement, and 2 568 318 who had elective cataract repair.
Main outcome measures Days waited from referral for surgery to surgery itself; socioeconomic status based on Carstairs index of deprivation.
Results Mean and median waiting times rose initially and then fell steadily over time. By 2007 variation in waiting times across the population tended to be lower. In 1997 waiting times and deprivation tended to be positively related. By 2007 the relation between deprivation and waiting time was less pronounced, and, in some cases, patients from the most deprived fifth were waiting less time than patients from the most advantaged fifth.
Conclusions Between 1997 and 2007 waiting times for patients having elective hip replacement, knee replacement, and cataract repair in England went down and the variation in waiting times for those procedures across socioeconomic groups was reduced. Many people feared that the government’s NHS reforms would lead to inequity, but inequity with respect to waiting times did not increase; if anything, it decreased. Although proving that the later stages of those reforms, which included patient choice, provider competition, and expanded capacity, was a catalyst for improvements in equity is impossible, the data show that these reforms, at a minimum, did not harm equity.
doi:10.1136/bmj.b3264
PMCID: PMC2737605  PMID: 19729415
17.  Time on wait lists for coronary bypass surgery in British Columbia, Canada, 1991 – 2000 
Background
In British Columbia, Canada, all necessary medical services are funded publicly. Concerned with growing wait lists in the mid-1990s, the provincial government started providing extra funding for coronary artery bypass grafting (CABG) operations annually. Although aimed at improving access, it is not known whether supplementary funding changed the time that patients spent on wait lists for CABG. We sought to determine whether the period of registration on wait lists had an effect on time to isolated CABG and whether the period effect was similar across priority groups.
Methods
Using records from a population-based registry, we studied the wait-list time before and after supplementary funding became available. We compared the number of weeks from registration to surgery for equal proportions of patients in synthetic cohorts defined by five registration periods in the 1990s.
Results
Overall, 9,231 patients spent a total of 137,126 person-weeks on the wait lists. The time to surgery increased by the middle of the decade, and decreased toward the end of the decade. Relative to the 1991–92 registration period, the conditional weekly probabilities of undergoing surgery were 30% lower among patients registered on the wait lists in 1995–96, hazard ratio (HR) = 0.70 (0.65–0.76), and 23% lower in 1997–98 patients, HR = 0.77 (0.71–0.83), while there were no differences with 1999–2000 patients, HR = 0.94 (0.88–1.02), after adjusting for priority group at registration, comorbidity, age and sex. We found that the effect of registration period was different across priority groups.
Conclusion
Our results provide evidence that time to CABG shortened after supplementary funding was provided on an annual basis to tertiary care hospitals within a single publicly funded health system. One plausible explanation is that these hospitals had capacity to increase the number of operations. At the same time, the effect was not uniform across priority groups indicating that changes in clinical practice should be considered when adding extra funding to reduce wait lists.
doi:10.1186/1472-6963-5-22
PMCID: PMC1079832  PMID: 15766381
18.  What did the public think of health services reform in Bangladesh? Three national community-based surveys 1999–2003 
Background
Supported by development partners, the Government of Bangladesh carried out a comprehensive reform of health services in Bangladesh between 1998 and 2003, intended to make services more responsive to public needs: the Health and Population Sector Programme (HPSP). They commissioned a series of surveys of the public, as part of evaluation of the HPSP. This article uses the survey findings to examine the changes in public opinions, use and experience of health services in the period of the HPSP.
Methods
We carried out three household surveys (1999, 2000 and 2003) of a stratified random sample of 217 rural sites and 30 urban sites. Each site comprised 100–120 contiguous households. Each survey included interviews with 25,000 household respondents and managers of health facilities serving the sites, and gender-stratified focus groups in each site. We measured: household ratings of government health services; reported use of services in the preceding month; unmet need for health care; user reports of waiting times, payments, explanations of condition, availability of prescribed medicines, and satisfaction with service providers.
Results
Public rating of government health services as "good" fell from 37% to 10% and the proportion using government treatment services fell from 13% to 10%. Unmet need increased from 3% to 9% of households. The proportion of visits to government facilities fell from 17% to 13%, while the proportion to unqualified practitioners rose from 52% to 60%. Satisfaction with service providers' behaviour dropped from 66% to 56%. Users were more satisfied when waiting time was shorter, prescribed medicines were available, and they received explanations of their condition.
Conclusion
Services have retracted despite increased investment and the public now prefer unqualified practitioners over government services. Public opinion of government health services has deteriorated and the reforms have not specifically helped the poorest people. User satisfaction could be increased if government doctors improved their interaction with patients and if waiting times were reduced by better management of facilities.
doi:10.1186/1478-4505-5-1
PMCID: PMC1810295  PMID: 17324263
19.  Waiting times in the ambulatory sector - the case of chronically Ill patients 
Aims
First, the influence of determinants on the waiting times of chronically ill patients in the ambulatory sector is investigated. The determinants are subdivided into four groups: (1) need, (2) socio-economic factors, (3) health system and (4) patient time pressures. Next, the influence of waiting times on the annual number of consultations is examined to assess whether the existing variation in waiting times influences the frequency of medical examinations. The waiting times of chronically ill patients are analysed since regular ambulatory care for this patient group could both improve treatment outcomes and lower costs.
Data sources
Individual data from the 2010 Representative Survey conducted by the National Association of Statutory Health Insurance Physicians (KBV) together with regional data from the Federal Office of Construction and Regional Planning.
Study design
This is a retrospective observational study. The dependent variables are waiting times in the ambulatory sector and the number of consultations of General Practitioners (GPs) and specialist physicians in the year 2010. The explanatory variables of interest are ‘need’ and ‘health system’ in the first model and ‘length of waiting times’ in the second. Negative binomial models with random effects are used to estimate the incidence rate ratios of increased waiting times and number of consultations. Subsequently, the models are stratified by urban and rural areas.
Results
In the pooled regression the factor ‘privately insured’ shortens the waiting time for treatment by a specialist by approximately 28% (about 3 days) in comparison with members of the statutory health insurance system. The category of insurance has no influence on the number of consultations of GPs. In addition, the regression results stratified by urban and rural areas show that in urban areas the factor ‘privately insured’ reduces the waiting time for specialists by approximately 35% (about 3.3 days) while in rural areas there is no evidence of statistical influence. In neither of the models, however, does the waiting time have a documentable effect on the number of consultations in the ambulatory sector.
Conclusions
In our random sample, characteristics of the health care system have an influence on the waiting time for specialists, but the waiting time has no documentable effect on the number of consultations in the ambulatory sector. In the present analysis this applies to consultations of both GPs and specialists. Nevertheless, it does not rule out the possibility that the length of waiting times might influence the treatment outcomes of certain patient populations.
doi:10.1186/1475-9276-12-77
PMCID: PMC3850794  PMID: 24020453
Waiting times; Ambulatory sector; Consultations
20.  Do list size and remuneration affect GPs' decisions about how they provide consultations? 
Background
Doctors' professional behaviour is influenced by the way they are paid. When GPs are paid per item, i.e., on a fee-for-service basis (FFS), there is a clear relationship between workload and income: more work means more money. In the case of capitation based payment, workload is not directly linked to income since the fees per patient are fixed. In this study list size was considered as an indicator for workload and we investigated how list size and remuneration affect GP decisions about how they provide consultations. The main objectives of this study were to investigate a) how list size is related to consultation length, waiting time to get an appointment, and the likelihood that GPs conduct home visits and b) to what extent the relationships between list size and these three variables are affected by remuneration.
Methods
List size was used because this is an important determinant of objective workload. List size was corrected for number of older patients and patients who lived in deprived areas. We focussed on three dependent variables that we expected to be related to remuneration and list size: consultation length; waiting time to get an appointment; and home visits. Data were derived from the second Dutch National Survey of General Practice (DNSGP-2), carried out between 2000 and 2002. The data were collected using electronic medical records, videotaped consultations and postal surveys. Multilevel regression analyses were performed to assess the hypothesized relationships.
Results
Our results indicate that list size is negatively related to consultation length, especially among GPs with relatively large lists. A correlation between list size and waiting time to get an appointment, and a correlation between list size and the likelihood of a home visit were only found for GPs with small practices. These correlations are modified by the proportion of patients for whom GPs receive capitation fees. Waiting times to get an appointment tend to become shorter with increasing patient lists when there is a larger capitation percentage. The likelihood that GPs will conduct home visit rises with increasing patient lists when the capitation percentage is small.
Conclusion
Remuneration appears to affect GPs' decisions about how they provide consultations, especially among GPs with relatively small patient lists. This role is, however, small compared to other factors such as patient characteristics.
doi:10.1186/1472-6963-9-39
PMCID: PMC2654894  PMID: 19245685
21.  Generic waiting lists for routine spinal surgery 
National Health Service Hospitals are under pressure to reduce waiting lists within the constraints of a limited infrastructure. We implemented two systems to reduce waiting times for elective non-complex spinal surgery. The first of these was the introduction of managed generic waiting lists for both initial outpatient appointments and subsequent surgery. Thereafter, the MRI booking system was integrated with outpatient review appointments. Times from referral to first outpatient appointment and from scan to outpatient review and time on waiting list for surgery were analysed before and after implementation of these changes.
Despite constant unit capacity there was a global decrease in waiting times. Before introduction of the generic waiting list, 37% of listed patients waited for more than 9 months; this figure fell to zero. Time from scan to outpatient review was 185 days before integration, 30 days after.
Changes of this sort demand a quorum of consultants who will accept each others' recommendations. The generic waiting list will have impact only when there are large disparities in waiting times for different consultants. Targets are met at the expense of continuity of care.
PMCID: PMC1079320  PMID: 14996957
22.  Remote Speech and Language Therapy services in Buckinghamshire 
Executive summary
Speech and Language Therapists work with people with aphasia following Stroke. For many clients aphasia is a life-long condition and an individual’s ability to adjust to this is very variable. Buckinghamshire Healthcare Trust has an integrated adult Speech and Language Therapy (SLT) Service with 3.6 WTE therapists allocated to the Community Long-Term Conditions Team which serves patients with aphasia as well as other conditions including dementia and progressive neurological conditions. Obviously there is a limit in the capacity of this service and we need to look to alternative and innovative methods of service provision. These have included:
group therapy
strengthen links with the voluntary sector
Conversation Partner Scheme training volunteers to visit people in their own homes
Strategic context
A local healthcare need was identified by Buckinghamshire County Council (BCC); difficulty accessing intensive SLT for clients with aphasia post stroke. BCC had experience and expertise in Telecare/Telehealth and wanted to extend the role of Telehealth to these clients, funding was identified. BCC and the SLT Department worked together for several months identifying the best quality package for people with aphasia. This was potentially very exciting but the initial concept required adjustment as the current Telecare was not sophisticated enough to cope with the data produced by complex aphasia computer therapy. An additional partner was brought in ‘Steps Consultancy’ and this enabled us to produce a package which achieved our initial objective. The provision of the following is secure for 3 years: 7 MSI touchscreen computers (with keyboard and mouse). Three laptops and laptop bags. Multiple user Licence for Step-By-Step software. Licence x8 REACT software. Ten finger scanners, microphones and headphones. Contract with a local company for hardware support. Continued software support form Steps including staff training.
Case for change
Currently we are set up to begin using the devices remotely, this took a lot of technical work with Steps Consultancy and Buckinghamshire Healthcare Trust (BHT) IT Department. SLT staff was trained in software use but are not yet highly familiar with the package. All devices were assigned to clients and we are in the process of setting these clients up with a system by connecting the device to the central server through their home internet system. The outdated IT equipment we are trying to use within the Trust is slowing us down. Also, due to staffing constraints in the department the assessment of patients, setting up of the programme, delivering and setting up the equipment and the following up on results and updating therapy packages is time consuming and has been difficult to absorb into our current capacity, but we are making progress.
Objective
To provide people with aphasia access to Teletherapy so that they can increase the amount of therapy practice.
Service Proposal for stage 2
The SLT department has identified 3.75 hours a week of therapy time to dedicate to the Teletherapy Project, this will be subject to BHT approval, if approved will commence January 2012. This is in order to use the equipment to its full potential and achieve timely distribution of computer therapy packages. We have concerns that, as all touchscreens are allocated to patients, and patients keep the screens for approximately 6 months there is going to be a waiting list for equipment as we receive new referrals for Teletherapy. The SLT Department proposes further development of the project in the following ways:
investment in upgrading the project to teleconferencing to use the equipment we currently have to its full potential. This would involve investment in Broadband, new up to date Windows7 PCs and 24” monitors (3 of each), webcams and appropriate software to allow teleconferencing;
purchasing a further 10 touchscreen computers so that the programme is available to new strokes without having to go on a waiting list;
increased consultancy fee to Steps so that we can have mentorship of our new post-holder who will have 3.75 hours per week allocated to the project;
to provide SLT services to other neuro-conditions that result in a SLT requirement;
integration of telehealth service into existing community equipment services and countywide Telehealth monitoring contract to test model for Telehealth at scale in next 12–24 months.
PMCID: PMC3571123
telehealth; speech and language therapy; joint commissioning
23.  Effect of a Brief Video Intervention on Incident Infection among Patients Attending Sexually Transmitted Disease Clinics  
PLoS Medicine  2008;5(6):e135.
Background
Sexually transmitted disease (STD) prevention remains a public health priority. Simple, practical interventions to reduce STD incidence that can be easily and inexpensively administered in high-volume clinical settings are needed. We evaluated whether a brief video, which contained STD prevention messages targeted to all patients in the waiting room, reduced acquisition of new infections after that clinic visit.
Methods and Findings
In a controlled trial among patients attending three publicly funded STD clinics (one in each of three US cities) from December 2003 to August 2005, all patients (n = 38,635) were systematically assigned to either a theory-based 23-min video depicting couples overcoming barriers to safer sexual behaviors, or the standard waiting room environment. Condition assignment alternated every 4 wk and was determined by which condition (intervention or control) was in place in the clinic waiting room during the patient's first visit within the study period. An intent-to-treat analysis was used to compare STD incidence between intervention and control patients. The primary endpoint was time to diagnosis of incident laboratory-confirmed infections (gonorrhea, chlamydia, trichomoniasis, syphilis, and HIV), as identified through review of medical records and county STD surveillance registries. During 14.8 mo (average) of follow-up, 2,042 patients (5.3%) were diagnosed with incident STD (4.9%, intervention condition; 5.7%, control condition). In survival analysis, patients assigned to the intervention condition had significantly fewer STDs compared with the control condition (hazard ratio [HR], 0.91; 95% confidence interval [CI], 0.84 to 0.99).
Conclusions
Showing a brief video in STD clinic waiting rooms reduced new infections nearly 10% overall in three clinics. This simple, low-intensity intervention may be appropriate for adoption by clinics that serve similar patient populations.
Trial registration: http://www.ClinicalTrials.gov (#NCT00137670).
In a controlled trial at three urban STD clinics, Lee Warner and colleagues find that showing an educational video in waiting rooms reduced new infections by approximately 10%.
Editors' Summary
Background.
In the US alone there are 19 million new cases of sexually transmitted diseases (STDs) every year. STDs are infections that pass between people during sexual activity (through semen, vaginal fluids, blood, or skin-to-skin contact). Some STDs are caused by bacteria (for example, chlamydia, gonorrhea, and syphilis). Others are caused by parasites (for example, trichomoniasis) or viruses (for example, herpes simplex virus and HIV). Symptoms vary among STDs but may include sores, unusual lumps and itching in the genital region, pain when urinating, and unusual genital discharge. While symptoms are generally more common in men than women, many STDs cause no symptoms. Untreated STDs are more serious for women and may include pelvic inflammatory disease (PID), ectopic pregnancy, infertility, and chronic pain. Bacterial and parasitic STDs can be cured with various drugs; STDs caused by viruses cannot be cured although they can be treated with antiviral drugs.
Why Was This Study Done?
Several interventions have been developed to educate people at risk of infection about risky sexual behavior and to teach them the personal skills needed to avoid unsafe sex (for example, negotiation skills that help them persuade their partner to use a condom). Although these interventions reduce the incidence of STDs, they usually involve several sessions of individual or group counseling and are likely too complex and expensive to implement in busy STD clinics. In this study, the researchers ask whether a short video that contains key STD prevention messages can reduce the acquisition of new infections among patients who watch the video while sitting in the waiting room of an STD clinic (a “teachable moment” when people are likely to be receptive to messages about health risks).
What Did the Researchers Do and Find?
The researchers developed a 23-minute soap-opera style video—“Safe in the City”—that contained three interwoven dramas about young people in various types of relationships negotiating safer sexual behavior, and two animation segments about condoms. The researchers showed this video (and displayed related posters) in the waiting rooms of three US publicly funded STD clinics every alternate month over a 20-month period. Nearly 20,000 patients were exposed to this intervention. Another 20,000 “control” patients who attended the clinics in the months when the video was not shown were exposed to a standard waiting room environment in which only leaflets about STDs and condoms were available. The researchers then reviewed medical records and STD surveillance registries to find out how many patients in each group developed laboratory-confirmed STD after their initial clinic visit. Their statistical analyses show that the intervention reduced the number of new STD diagnoses by nearly 10%. The intervention was most effective among patients who had had an STD at their first visit and among men, but did not appear to reduce the chances of women acquiring an STD.
What Do These Findings Mean?
These findings suggest that showing a brief, carefully designed video in STD clinic waiting rooms might be a simple, effective way to reduce the incidence of STDs. More research is needed to discover which parts of the video—those that increase knowledge and perception of STD risk, those that promote positive attitudes toward condom use, or those that provide the necessary skills to negotiate safe sexual practices—are the most effective and why the video appeared to be more effective for some groups of patients than others. The intervention also needs to be tested in other types of clinics but if it works as well elsewhere as in the three study clinics, the widespread implementation of this low-cost, low-intensity waiting room intervention could produce a meaningful reduction in the incidence of STDs in the US and elsewhere.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050135.
Information is available from Avert, an international AIDS charity, on sexually transmitted diseases
The US Centers for Disease Control and Prevention provides detailed information about sexually transmitted diseases, including information about STD prevention (in English and Spanish)
MedlinePlus also provides a list of links to information about sexually transmitted diseases (in English and Spanish)
The MedlinePlus encyclopedia has a page on safe sex (in English and Spanish)
The Safe in the CIty Study Group has a project-specific Web site that provides additional details about the intervention and a mechanism for ordering the video
doi:10.1371/journal.pmed.0050135
PMCID: PMC2504047  PMID: 18578564
24.  Health services reform in Bangladesh: hearing the views of health workers and their professional bodies 
BMC Health Services Research  2011;11(Suppl 2):S8.
Background
In Bangladesh, widespread dissatisfaction with government health services did not improve during the Health and Population Sector Programme (HPSP) reforms from 1998-2003. A 2003 national household survey documented public and health service users' views and experience. Attitudes and behaviour of health workers are central to quality of health services. To investigate whether the views of health workers influenced the reforms, we surveyed local health workers and held evidence-based discussions with local service managers and professional bodies.
Methods
Some 1866 government health workers in facilities serving the household survey clusters completed a questionnaire about their views, experience, and problems as workers. Field teams discussed the findings from the household and health workers' surveys with local health service managers in five upazilas (administrative sub-districts) and with the Bangladesh Medical Association (BMA) and Bangladesh Nurses Association (BNA).
Results
Nearly one half of the health workers (45%) reported difficulties fulfilling their duties, especially doctors, women, and younger workers. They cited inadequate supplies and infrastructure, bad behaviour of patients, and administrative problems. Many, especially doctors (74%), considered they were badly treated as employees. Nearly all said lack of medicines in government facilities was due to inadequate supply, not improved during the HPSP. Two thirds of doctors and nurses complained of bad behaviour of patients. A quarter of respondents thought quality of service had improved as a result of the HPSP.
Local service managers and the BMA and BNA accepted patients had negative views and experiences, blaming inadequate resources, high patient loads, and patients' unrealistic expectations. They said doctors and nurses were demotivated by poor working conditions, unfair treatment, and lack of career progression; private and unqualified practitioners sought to please patients instead of giving medically appropriate care. The BMA considered it would be dangerous to attempt to train and register unqualified practitioners.
Conclusions
The continuing dissatisfaction of health workers may have undermined the effectiveness of the HPSP. Presenting the views of the public and service users to health managers helped to focus discussions about quality of services. It is important to involve health workers in health services reforms.
doi:10.1186/1472-6963-11-S2-S8
PMCID: PMC3332567  PMID: 22375856
25.  Waiting times: monitoring the total postreferral wait. 
BMJ : British Medical Journal  1994;309(6954):593-596.
OBJECTIVES--To determine whether the period spent on the true inpatient waiting list is a valid indication of the total time that patients have to wait for an operation; and to assess the feasibility of monitoring the total "postreferral waiting time" by using existing computerised information systems. SETTING--Three randomly selected Scottish hospitals. SUBJECTS--Waiting list patients admitted to hospital for operations during June to August 1993 in six major specialties, separate attention being focused on cataract operations and hip and knee replacements. MAIN OUTCOME MEASURE--The total time that patients have to wait for an operation after the initial general practitioner referral--the postreferral waiting time--compared with that spent at the final stage of the process on the true inpatient waiting list. RESULTS--In the specialties investigated roughly half (58 days; 53%) of the average postreferral wait of 110 days was spent on the true inpatient waiting list, one third (35 days; 32%) being spent on the outpatient waiting list and one sixth (17 days; 15%) waiting between waiting lists. Only a quarter of cataract patients (73/292) were treated within three months of general practitioner referral compared with over three quarters (228/292) within three months of being placed on the inpatient waiting list. Nevertheless, within a year over 99% of patients (290) had been treated whichever date was taken as the starting point. CONCLUSIONS--Monitoring postreferral waiting times would provide a much more accurate picture for purchasers and patients of waiting times for treatment than is obtained by focusing exclusively on the true inpatient waiting list and facilitate fairer comparisons between NHS trusts in national league tables. Stringent national and local monitoring is essential to ensure (a) that future reductions in the time waiting on true inpatient waiting lists are not gained at the expense of longer periods waiting to be placed on the lists, and (b) that no increases occur in the number of patients placed instead on deferred waiting lists or exempted from the normal maximum waiting time guarantees.
PMCID: PMC2541407  PMID: 8086952

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