Health Human Resource Planning of Physiotherapists and Occupational Therapists in Newfoundland and Labrador

Transcription

1 Health Human Resource Planning of Physiotherapists and Occupational Therapists in Newfoundland and Labrador by Tsoleen Ayanian A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Graduate Department of Mechanical and Industrial Engineering University of Toronto Copyright 2015 by Tsoleen Ayanian

2 Abstract Health Human Resource Planning of Physiotherapists and Occupational Therapists in Newfoundland and Labrador Tsoleen Ayanian Master of Applied Science Graduate Department of Mechanical and Industrial Engineering University of Toronto 2015 This research seeks to substantiate the gap between demand and supply of physiotherapists (PTs) and occupational therapists (OTs) in Newfoundland and Labrador through the development of a system dynamics (SD) model. The model was used to test "what-if" scenarios by exploring various school sizes at Memorial University of Newfoundland (MUN). A needs-based modeling approach was used to define the population's requirement for PTs and OTs. Data obtained from Newfoundland Centre for Health Information was used to populate the demand model, and the workforce data obtained through Canadian Institute for Health Information was used to populate the SD supply model; both demand and supply were projected to the year The results show a shortage of PTs and OTs, with this gap expected to increase over time. It is recommended that schools with eventual sizes of 90 PT and 34 OT students be opened at MUN to gradually close the gap. ii

3 Acknowledgements I would like to thank my supervisor, Professor Michael Carter, for allowing me the opportunity to pursue and explore my interest in healthcare. His expertise in the field, belief in the project, and endless guidance and support allowed for the completion of this research. I would like to thank Dr. Michelle Ploughman for being a constant supporter and advocate of the research and for her work and commitment to the project. Thank you to Adrienne Castellino and Neil McEvoy for your counsel during the initial phase of the research. I would like to thank my committee members, Dr. Michel Landry and Professor Dionne Aleman, who have provided me with invaluable feedback and guidance. To Professor Mike Landry, a special thank you for organising my trip to the International PT workforce summit, which provided me with further insight and knowledge to complete this project. To my colleagues in the Centre for Research in Healthcare Engineering, thank you for providing me with an open and inviting environment to work in and promoting meaningful discussions. Last but not least, I would like to thank my family and friends for their continued support and patience while pursuing my Masters degree. iii

4 TABLE OF CONTENTS 1 Background Introduction Literature Review Definitions of Demand for Health Services Provider-to-population ratio Utilization-based modeling approach Needs-based modeling approach Healthcare Demand Forecasting Methods Simulation Modeling Methods Patient Population (Demand) Provider Population (Supply) Stock and flow based model Inflows Outflows MUN schooling option Validation of Model Linear regression to forecast incidence rates Validating the entry and attrition values used for modeling supply Results Demand vs. Supply Sensitivity Testing iv

5 5.2.1 Percent of Patients PTs and OTs treat Time Spent with Patients OT Non-Direct Care Roles Alternate care Varying Attrition Rate of PT and OT Workforce Scenario Testing MUN Schooling Option for Physiotherapy MUN Schooling Option for Occupational Therapy Discussion Analysis of Results Limitations Future Work Conclusion References Appendix A: Treatment Time of PT Patients Appendix B: Treatment Time of OT Patients Appendix C: NFLD Population Projections Appendix D: Percent Distribution of PTs by Age Cohorts Appendix E: Percent Distribution of OTs by Age Cohorts Appendix F: EHA Population Projections Appendix G: CHA Population Projections Appendix H: WHA Population Projections Appendix I: LGHA Population Projections v

6 Appendix J: Forecasting Accuracy of Cancer Surgery Incidence Rates, by Age-Cohorts Appendix K: Forecasting Accuracy of Cancer Incidence Rates, by Age-Cohorts Appendix L: PT Demand vs. Supply, Health Authority Regions Appendix M: OT Demand vs. Supply, Health Authority Regions Appendix N: Sensitivity Testing of Percent of Patients Seen by a PT Appendix O: Sensitivity Testing of Percent of Patients Seen by an OT vi

7 List of Tables Table 1: Physiotherapy and occupational therapy general areas of practice Table 2: Top medical conditions requiring PT treatment by area of practice Table 3: Top medical conditions requiring OT treatment by area of practice Table 4: PTs and OTs average annual hours worked by five-year age cohorts, Table 5: Number of employed PTs by Regional Integrated Health Authorities, Table 6: Number of employed OTs by Regional Integrated Health Authorities, Table 7: Sensitivity testing of PT attrition rates Table 8: Sensitivity testing of OT attrition rates vii

8 List of Figures Figure 1: Population projections for Newfoundland and Labrador [7]... 3 Figure 2: Age distribution of NFLD population by 15-year age cohorts, (a) 2012 and (b) Figure 3: Map of Newfoundland and Labradors four Regional Integrated Health Authorities [9] 4 Figure 4: Population projections for EHA, CHA, WHA, and LGHA [7]... 5 Figure 5: Demand for the (a) physiotherapy workforce and (b) occupational therapy workforce 25 Figure 6: Simple stock and flow structure [49] Figure 7: Information feedback between stocks and flows [49] Figure 8: Population causal loop diagram [49] Figure 9: The use of stocks and flows to depict the aging of a population Figure 10: Number of employed PTs and OTs in NFLD, Figure 11: Number of PTs and OTs entering the NFLD workforce, Figure 12: Average number of PTs and OTs entering the workforce per year Figure 13: Average number of (a) PTs and (b) OTs entering the workforce per year by university of graduation Figure 14: Number of PTs and OTs leaving the NFLD workforce, Figure 15: Average number of PTs and OTs leaving the workforce per year Figure 16: Average number of employed PTs and OTs, Figure 17: Average annual PT and OT attrition rate Figure 18: SD stock and flow model for PT Figure 19: Supply of (a) PTs and (b) OTs Figure 20: Actual and predicted incidence rates for PT cancer surgery patients for age cohort (a) years with calculated Se values and (b) years with calculated two Se values Figure 21: Actual and predicted incidence rates for OT cancer patients for age cohort (a) years with calculated Se values and (b) years with calculated two Se values Figure 22: Comparison of supply between CIHI data and SD model Figure 23: Annual number of (a) PTs and (b) OTs in NFLD based on CIHI data and SD model 43 Figure 24: Demand vs. supply of (a) PTs and (b) OTs viii

9 Figure 25: Gap between demand and supply for (a) PTs and (b) OTs Figure 26: Distribution of PT time requirement based on patient sub groups for year Figure 27: Demand for PTs based on varying percent of cancer surgery patients seen Figure 28: Distribution of OT time requirement based on patient sub groups for year Figure 29: Demand for OTs based on varying percent of cancer patients seen Figure 30: Number of PTs required based on varying percent (a) of minimum treatment time, (b) maximum treatment time, and (c) typical treatment time for cancer surgery patients Figure 31: Number of OTs required based on varying percent (a) of minimum treatment time, (b) maximum treatment time, and (c) typical treatment time for cancer patients Figure 32: Demand for OTs including non-healthcare related roles Figure 33: Demand for (a) PTs and (b) OTs based on varying percent of patients seeking alternative care Figure 34: Supply of (a) PTs and (b) OTs with varying attrition rates Figure 35: Supply of (a) PTs and (b) OTs when varying attrition rate Figure 36: Size of workforce by age cohorts for (a) PTs and (b) OTs Figure 37: PT gap, constant school size Figure 38: (a) PT gap for scenarios 2 to 6 and (b) PT MUN school size Figure 39: OT gap, constant school size Figure 40: (a) OT gap for scenarios 2 to 7 and (b) OT MUN school size Figure 41: Actual and predicted incidence rates for PT cancer surgery patients for age cohort (a) years, (b) years, and (c) 75+ years with calculated Se values Figure 42: Actual and predicted incidence rates for PT cancer surgery patients for age cohort (a) years, (b) years, and (c) 75+ years with calculated two Se values Figure 43: Actual and predicted incidence rates for OT cancer surgery patients for age cohort (a) years, (b) years, (c) years, and (d) 75+ years with calculated Se values Figure 44: Actual and predicted incidence rates for OT cancer surgery patients for age cohort (a) years, (b) years, (c) years, and (d) 75+ years with calculated two Se values Figure 45: Demand vs. supply of PTs for (a) Eastern Health Authority, (b) Central Health Authority, (c) Western Health Authority, and (d) Labrador-Grenfell Health Authority ix

10 Figure 46: Distribution of PT (a) demand and (b) supply among NFLD 4 health authority regions, Figure 47: Distribution of PT (a) demand and (b) supply among NFLD 4 health authority regions, Figure 48: Demand vs. supply of OTs for (a) Eastern Health Authority, (b) Central Health Authority, (c) Western Health Authority, and (d) Labrador-Grenfell Health Authority Figure 49: Distribution of OT (a) demand and (b) supply among NFLD 4 health authority regions, Figure 50: Distribution of OT (a) demand and (b) supply among NFLD 4 health authority regions, Figure 51: Demand for PTs based on varying percent of multisystem trauma patients seen Figure 52: Demand for PTs based on varying percent of cancer care patients seen Figure 53: Demand for PTs based on varying percent of critical care intervention patients seen Figure 54: Demand for PTs based on varying percent of traumatic brain injury patients seen Figure 55: Demand for PTs based on varying percent of stroke trauma patients seen Figure 56: Demand for OTs based on varying percent of arthritis patients seen Figure 57: Demand for OTs based on varying percent of traumatic brain injury patients seen. 137 Figure 58: Demand for OTs based on varying percent of knee replacement patients seen Figure 59: Demand for OTs based on varying percent of stroke patients seen Figure 60: Demand for OTs based on varying percent of addiction patients seen Figure 61: Demand for OTs based on varying percent of back injury/surgery patients seen x

11 Chapter 1 1 BACKGROUND Physiotherapists (PTs) are primary care providers, whose role ranges from preventative care, management of chronic conditions, education of patient to prevent or improve patient s health and well-being, and improving patient s mobility and functionality due to injury, disability, or other medical conditions with the goal of increasing a patient s independence, allowing them to live at home longer [1]. Similar to physiotherapy, occupational therapists (OTs) objective is to enhance patient s functionality; however, they achieve this goal through the engagement of patients in their everyday activities, which helps define an individual, leading to improved health [2]. Research has shown the effectiveness of patients receiving PT or OT treatment reporting better health status [3], and is correlated with positive health outcomes for patients, resulting in reduced complications, negative consequences, and length of stays for inpatients [3,4]. In addition, it has been found that rehabilitation services prior to surgery yield better outcomes, and reduces the requirement for rehab services post-surgery [3]. Page 1

12 Evidence points to increasing demand for these services; however, it has not yet been established what optimal levels of the PT and OT workforce should be so as to satisfy this demand [3, 5]. The main contributing factor to increased demand is the growth of the population and an increase in the number of older individuals [3]. Longer life spans may mean that the requirement for rehabilitation to improve an individual s functionality and mobility will increase [6]. Moreover, the increased likelihood of individuals surviving traumatic injuries, diseases, disabilities or chronic conditions due to advancing medical treatment may increase the requirement for rehabilitation, especially given the increased chance of individuals with complex conditions developing co-morbidities [3]. Issues pertaining to the demand for rehabilitation arise due to increased complexity of individuals health issues, and not just due to the increasing age of the population [3]. Other factors influencing demand for rehabilitation services include the increased expectation of the public from the healthcare system [5]. Newfoundland and Labrador (NFLD) is a province with a small population of around 526,000 people in 2012, with the size of the population expected to decrease by approximately 2,900 between 2012 and 2055 according to NFLDs Economic Research and Analysis Division (Figure 1) [7]. Population projections were only available up to 2035; to examine long term changes they were extended, based on observed trends, to The national trend of growing number of older individuals is present in NFLD. Figure 2 shows an increase of the percent of individuals in NFLD above the age of 45 years. In 2012, 47% of the population were individuals 45 years and older, which will increase to 60% by Page 2

13 580, , , ,000 Population size 540, , , , , , , Year Figure 1: Population projections for Newfoundland and Labrador [7] The province of Newfoundland and Labrador is comprised of four Regional Integrated Health Authorities (Figure 3): Central Health Authority (CHA), Eastern Health Authority (EHA), Western Health Authority (WHA), and Labrador-Grenfell Health Authority (LGHA). The EHA region has the largest population size of all four Health Authority Regions and is expected to increase in size followed by the CHA and WHA region, which are both expected to decrease in population size (Figure 4). The LGHA region has the smallest population size even though it has the greatest land area, which could lead to issues with attracting healthcare personnel to its numerous rural communities [8]. Page 3

14 % % 75+ 6% % % % % % % % % % (a) Figure 2: Age distribution of NFLD population by 15-year age cohorts, (a) 2012 and (b) 2055 (b) Figure 3: Map of Newfoundland and Labradors four Regional Integrated Health Authorities [9] Page 4

15 400, , ,000 Eastern Health Authority Central Health Authority Western Health Authority Labrador-Grenfell Health Authority 280,000 Population size 240, , , ,000 80,000 40, Year Figure 4: Population projections for EHA, CHA, WHA, and LGHA [7] Page 5

16 Chapter 2 2 INTRODUCTION Even though there is evidence pointed towards increased demand for PTs and OTs, and improvement to patients well-being as a result of treatment, these services are gradually being pushed out of publicly funded sector [3]. The result has been an increased number of private physiotherapy and occupational therapy clinics and limited number of publicly funded positions [3]. While physiotherapy and occupational therapy are covered in NFLD under the Canada Health Act [10], there is increasingly restrictive eligibility criterion in place if these services are required outside of the hospital setting [11]. The province is already experiencing a shortage of PTs and OTs, as evidenced from the presence of waitlists [12-14], which suggests a disequilibrium between demand and supply of providers [4]. The increasing number of elderly in NLFD combined with the existence of wait lists and increased public expectations from the healthcare system [3, 4] would mean an increase in the demand for physiotherapy and occupational therapy, and thereby result in an increasing disparity between demand and available supply of PTs and OTs. As a result, there exist multiple Page 6

17 barriers to access to physiotherapists and occupational therapist, including financial barriers and scarcity in human resources [15]. Physiotherapy and occupational therapy are 25-month and 22-month Masters level programs, respectively. Currently, there is no in-province training for PTs or OTs in Newfoundland and Labrador. Instead, the government of NFLD buys seats from Dalhousie University at Nova Scotia; 10 seats are bought for PT students at Dalhousie University and eight for OT students [16]. However, there is no returnto-service agreement in place resulting in only a few students returning back to NFLD to work as a PT or OT upon completion of their education [16]. Based on data obtained from the Canadian Institute for Health Information (CIHI), on average six physiotherapy students and four occupational therapy students return to NFLD to work as a PT or OT after the completion of their education at Dalhousie University. The objective of this project is to develop a system dynamics (SD) model to forecast the future physiotherapy and occupational therapy patient population and provider population in the province of Newfoundland and Labrador. These projections would then be used to quantify the existence of a shortage of PTs and OTs in the province. Moreover, the SD model would be used to explore different health policy scenarios, and determine their effectiveness in addressing the shortage. The scenarios that will be examined are specifically those aimed at opening a school for PTs and OTs in the province. The steps involved in achieving the goal of the project include the following: Page 7

18 determine the scope of practice of PT/OT in terms of patients treated, collect data pertaining to the patient and provider population, develop models for projecting the patient and provider population, determine the magnitude of the shortage of PT/OT, and test different schooling options. In addition to analyzing the province, Newfoundland and Labrador s four health authority regions will also be examined individually to observe differences in demand and supply and the gap between the two. Some regions might be facing greater shortages as compared to other regions, which could call into consideration health policies aimed at redistributing the workforce among the health regions. Page 8

19 Chapter 3 3 LITERATURE REVIEW Methodologies and previous research pertinent to the project include health human resource planning (HHRP) modeling frameworks used to define the patient population, methods used to project the future patient population, and simulation modeling tools to be used for projecting the provider population and for what-if scenario testing. The various alternatives that can be employed and that have been used in the literature will be described in this section. 3.1 DEFINITIONS OF DEMAND FOR HEALTH SERVICES In HHRP, a projection of the populations future demand, and in turn the future required number and type of providers, can vary based on how demand has been defined [17, 18]. There exist different HHRP frameworks that can be employed for projection models, and includes provider-to-population ratio, utilization-based modeling approach, and needs-based modeling approach. Page 9

20 3.1.1 Provider-to-population ratio Provider-to-population ratio describes the number of providers available within a region in relation to the size of the population; it does not take into account the demographics of the population for the region in question [19]. During HHRP analysis an ideal ratio is chosen based on trends observed in other regions, international benchmarking, or through the use of experts within the field [19]. The use of ratios as a method of identifying optimal provider levels for a certain region is not representative of the population s requirements, especially if they are based on ratios observed in other regions as the requirements of one region cannot be compared or adjusted to reflect the requirements of another. Furthermore, ratios are not useful when discussing geographical issues such as provider availability in rural versus urban settings, and they do not provide insight as to whether these provider densities are optimal or not Utilization-based modeling approach An extension to the provider-to-population ratio method is the utilization-based modeling approach which considers the demographics of the population, usually age and gender [19, 20]. It assumes that the current number of providers servicing the population is appropriate and that this comprises the entire demand for the service; therefore, projections regarding future demand are based on the current demographics of the serviced population and current provider patterns [19, 20]. These assumptions imply that all individuals who require a particular health service are already receiving it, and it therefore disregards unmet demand [20]. Page 10

21 Knowing that long wait lists are prevalent, and taking into account financial and geographical barriers to access to healthcare services, this assumption is unrealistic. It does not consider the possibility that the current level of service utilization might not be sufficient; furthermore, it assumes that healthcare requirements will not change [21]. The use of this approach would perpetuate any inadequacies with regards to the delivery of health care services into the future [22]. Utilization-based modeling approach has been used for HHRP of oncologists [23], and determining the physician requirement for critically ill patients, and patients with pulmonary disease [24]. It has also been used for forecasting the supply and demand for physiotherapists in the United States by examining the portion of the population with health insurance [25], as well as determining the shortage of OTs in United States northwest region based on the number of vacant job positions [26] Needs-based modeling approach For improved accuracy regarding population health requirements, it is important to establish the amount of unmet demand that exists, that is, individuals who require specific healthcare services but for reasons, such as lack of healthcare resources, and financial or geographical barriers, inhibit them from receiving treatment [21]. Hence, demand is satisfied through the provision of adequate number and mix of resources so as to meet known demand as well as identified unmet demand [22]. Page 11

22 This approach has been used for estimating demand requirements for nurses in British Columbia [22], cardiac surgeons in Canada [27], and the global physician requirement [17]. Moreover, the use of needs-based framework for HHRP has been identified by researchers as an important factor for understanding and accurately forecasting demand in areas of mental health [28] and rehabilitation services [3]. However, lack of epidemiological data has prevented them from being able to quantifying need [19, 21]. To implement a needs-based modeling approach for projecting demand for PTs and OTs, there should be a link between the epidemiological patterns of patients to the need for PT and OT treatment, and a method for dividing the population into different patient sub-groups, such as the demography of the patient population, which can include age and sex, the body system and body part being treated, and the health care sector [29]. Therefore, a needs-based approach for defining demand for PT and OT services would require that patient s medical conditions, that would necessitate PT or OT treatment be identified [19, 21]. While there has been research conducted aimed at studying the need and supply for physiotherapy and occupational therapy, they are not comprehensive in terms of all areas of need for physiotherapy or occupational therapy [29]. In the case of the work on the demand for rehabilitation services for elderly in Ontario, the investigators identified health conditions present in the elderly that would bring about need for rehabilitation services [5]. However, they were unable to translate observed trends into number of health personnel required. Page 12

23 Instead they relied on surveys to estimate changes in demand for rehabilitation based on participants assessments [5]. There exist issues with attempting to define the need for a particular healthcare service. For establishing the need for nurses, the healthcare sectors that the provider would practice in were determined and within those healthcare sectors they ascertained the characteristics of the patients that would require to be seen by the provider based on age, gender, health condition, and health status [22]. Whereas for determining demand for cardiac surgeons the need for surgery was examined and was based on population surgery demand rates calculated by age, gender, and for different procedure types [27]. Once the relevant medical conditions had been identified, historical incidence rates were then acquired [19], [21]. To determine future demand for a health service the incidence rates of identified medical conditions would then have to be forecasted. 3.2 HEALTHCARE DEMAND FORECASTING METHODS Disease-specific incidence rates are used to determine the future patient population by applying them to readily available population projections. The use of rates allows for the examination of the needs of the population based on the distribution of the disease, or condition, within the population [19, 30].Based on research which utilized this methodology, incidence rates of specific diseases were agespecific, and in some cases, gender-specific as well. Since the population projections were also age, and gender specific, the changes to the demographics of Page 13

24 the population were factored into the model. The method used to forecast these rates differed by the research group. From the relevant literature surveyed, numerous research groups assumed that the incidence rate calculated, either for a specific year or as an average of numerous years of data, remained constant for the duration of the modeling time frame [23-25, 31-33]. For determining demand for nurses in Ontario, the population was categorized into different levels of health, where it was assumed that the future level of health of the population would remain constant [32]. However, the research group also explored two alternate scenarios; the first assumed the health level of the population would improve to match national levels over a time period of 15 years [32]. The second scenario assumed that the health level of the population would continue to follow historical trends observed [32]; the commonly employed percent adjustment forecasting method could be utilized, whereby the yearly percent change in health levels is calculated [34]. For subsequent years the health level of the population is expected to change by this calculated percent change [34]; this method does not consider long-term historical trends [34]. For improved staffing and resource planning the future demand for autopsy services were forecasted by calculating the historical usage rates for autopsy services [31]. While the authors did not test the impact in the case that future demand would change, they suggested the use of a linear model with multiple independent variables, or a multiple regression model, for predicting changes to daily and monthly demand [31]. Page 14

25 Linear regression is a commonly utilized method for forecasting healthcare demand as it produces results with reasonable accuracy and is used when the data exhibits a linear trend [34, 35]. An issue with using linear regression for forecasting incidence rates occurs when the rates show a decreasing trend, as forecasting these rates may result in negative values [36]; in the case of disease count this is unrealistic. Other methods used for healthcare forecasting include moving average and exponential smoothing [35]. Both these methods provide accurate forecasts in situations where the time series fluctuates about a constant base level and where no trend or seasonality is observed [37]. 3.3 SIMULATION MODELING Different modeling methodologies are available and utilized for HHRP purposes to determine the required supply of healthcare personnel to satisfy changing demand for both long term and short term planning periods [37]. These modeling methodologies include discrete event simulation (DES), linear programming (LP), Markov models, and system dynamics. The use of different HHR models will result in different interpretations and classifications of the healthcare system [37]. HHRP models do not aim to provide an exact estimate on number of human resources required [21], rather they are useful for understanding how a system might behave under different circumstances and provide an idea as to how it is expected to behave in the future. Page 15

26 The use of simulation software to develop models for the purpose of HHRP requires a comprehensive understanding of the system that is being modeled. The usefulness of such models is their ability to inexpensively test different scenarios and determine the outcomes of the system as a way of determining the most effective policy for addressing a problem. It is a non-biased, evidence-based method for determining the solution that yields the most significant outcomes. Discrete event simulation (DES) is widely used in healthcare and has been implemented in a variety of healthcare settings [38, 39]. They are often used to model patient flow within a small clinic or unit of a hospital, for improving patient flow, staff scheduling, or reducing wait times [38-41]. It has also been used for forecasting future service requirements by renal patients [42] and traumatic spinal cord injury patients [43]. However, DES is not well-suited for modeling complex or highly detailed systems and requires that assumptions and simplifications about the operations and flow within a particular setting be made [39]. Linear Programming (LP) is a mathematical model that is commonly used for the purposes of resource allocation problems with the intent of optimizing the outcomes of a given problem. It is found to be an easy as well as transparent methodology to utilize with the ability to explore what-if scenarios [44, 45]. LP has been used to determine the optimal number and mix of nurses in British Columbia [44] and dental therapists in England s South Central Strategic Health Authority [46, 47]. While the LP model was applicable on the provincial level, it required significant simplification so as to attain a solvable problem, as well as Page 16

27 extensive and accurate data [44]. There exists a trade-off between simplifying the model so as to determine an optimal solution and accurately portraying the current situation [21]. Markov models have been used for policy planning when dealing with movement of human health care resources as an inexpensive method of examining the effectiveness of certain policies and to determine the optimal policy for a certain problem [18, 48]. Markov models use transition probabilities to define how the system is to progress in the future; these probabilities are the major source of error [18]. A Markov model was developed to depict movements of the nursing workforce in South Africa to examine the effects of policies aimed at attracting and retaining nurses in rural settings [48]. Other examples of Markov models developed for HHRP includes the modeling of the primary health care system in a region of the United States with the goal of predicting the future supply of primary care health providers so as to compare the results with the future demand of the population [18]. System dynamics is a modeling method that is able to characterize the behaviour and the relationships that exist within a complex macro-level system over long periods of time [49], where the structure of the system influences its behaviour [38]. With reduced requirements on the quantity and quality of data, as compared to other modeling methods mentioned, and the ease of testing what-if scenarios, SD models are useful for HHRP purposes [38]. Page 17

28 A needs-based approach was utilized to determine the number of registered nurses required in Canada through the development of an SD model [22]. To determine the number of providers required to satisfy the needs of the population, information pertaining to the demography, epidemiology, required level of service, and the productivity of health care providers were considered [22]. Results showed that a shortage of RNs is present in Canada and would continue to increase if no changes were made to existing HHR policies [22]. Different policy scenarios were simulated through the use of the model to determine their effects on the RN shortage gap. Only modest changes were considered so as simulate feasible options. The authors found that when these different policy scenarios were combined, it was possible to alleviate the RN shortage within a time frame of 5 years [22]. Other examples of HHRP through the use of SD include projecting the demand and supply of cardiac surgeons in Canada [27, 33], medical specialists in Spain [50], child health physicians in the United States [51], and for forecasting demand for ambulatory services in the United States [52]. Through the use of SD, research groups have been able to develop and utilize high-level models to depict the demand and supply for different healthcare providers for large regions over long time frames and run various what-if scenarios with relative ease. The proposed scope of this project is similar to those of the research groups mentioned; accordingly SD is found to be the most appropriate method of modeling the PT and OT workforce. Page 18

29 Chapter 4 4 METHODS This section describes the procedures and data sources used for projecting the demand and supply for physiotherapy and occupational therapy for the years 2012 to 2055 and states the various assumptions made during the development of the model. 4.1 PATIENT POPULATION (DEMAND) Many of the roles of PTs and OTs are associated with patients medical conditions. By identifying and projecting these medical conditions, it was possible to predict the future demand for physiotherapy and occupational therapy. PTs and OTs treat patients for a variety of medical conditions; for this reason general areas of practice were first identified for both physiotherapy and occupational therapy (Table 1). Interviews were conducted with physiotherapists and occupational therapists from each of these general areas of practice. Michelle Ploughman, an Assistant Professor at Memorial University of Newfoundland (MUN) in the Faculty of Medicine, acted Page 19

30 as the project s NFLD contact and as the interviewer. The purpose of the interviews was to define the physiotherapy and occupational therapy patient populations so that the majority of demand was included. The selected PTs and OTs were asked to identify medical conditions that they thought occupied the majority of their available time (Table 2 and ). It is expected that patients with differing medical conditions would require different treatment durations. Therefore, the PTs and OTs were also asked to estimate treatment times for each medical condition based on three-levels of acuity (Appendix A and B). Maximum, minimum and typical treatment times were identified for each medical condition. It is assumed that for all medical conditions 15% of patients would require minimum treatment times, 70% would require typical treatment times, and 15% would require maximum treatment times. Multiplying these percentages by their respective treatment times would yield the mean treatment time. Historical inpatient hospital data for the years 2001 to 2011 for each of the medical conditions was requested from the Newfoundland and Labrador Centre for Health Information (NLCHI), with the data being organized into 15-year age cohorts. The data included patients that were admitted into acute care and surgical day care facilities. It should be noted that patient counts for Alzheimer s and other dementias and long term care admissions was unavailable through NLCHI, instead statistics regarding the annual number of individuals affected by Alzheimer s and other Page 20

31 dementias in Newfoundland and Labrador was obtained from the Provincial Strategy for Alzheimer Disease & Other Dementias report [53]. For new long term care patients, data regarding total number of available long-term care (LTC) beds in NFLD from the report Close to Home: A Strategy for Long Term Care and Community Support Services (2012) [54] and the average length of stay of patients at a Canadian LTC facility, obtained from Osteoporosis Update: A Practical Guide for Canadian Physicians [55] were used to estimate the annual number of new LTC patients in NFLD. This historical count of patients with the identified medical conditions coupled with historical 15-year age cohort population data from Newfoundland and Labradors Economic Research and Analysis Division were used to calculate the yearly incidence rates for each medical condition by dividing the total number of patients with medical condition Y, in age cohort A, and in year T by the total number of individuals in age cohort A in year T. The calculated incidence rates showed to follow either an increasing or decreasing trend. For those rates that showed an increasing trend, linear regression was utilized to forecast rates to For the medical conditions where incidence rates exhibited decreasing trends a variation of the percent adjustment method was employed, as opposed to using linear regression, so as to avoid negative rates when forecasted. The percent change in incidence rates between each year of the available data was calculated; the average of these percent changes was then Page 21

32 determined. To forecast the yearly incidence rate, it was assumed that incidence rate would change by the average percent change. These projected yearly incidence rates, by 15-year age cohorts, were then converted into number of patients through the use of Newfoundland and Labrador s population projections for years 2012 to 2055 (Appendix C). The future number of patients for each medical condition and age cohort was calculated by multiplying the incidence rate for age cohort A and year T by the population size for age cohort A and year T up to year Determining incidence rates by age cohorts allows for changes to the population demographics to be reflected when determining future patient volume. Table 1: Physiotherapy and occupational therapy general areas of practice Physiotherapy Occupational Therapy 1. Orthopedic 1. Mental Health 2. Neuromuscular 2. Neurological and complex conditions 3. Medicine (hospital inpatients) 3. Orthopedics 4. General surgery (excludes orthopedic) 4. Pediatrics 5. Hospital adult orthopedics outpatients 5. Long term care admissions 6. For profit (private) PT clinics 7. Outpatient pediatrics 8. Inpatient pediatrics 9. Long term care admissions Page 22

33 Table 2: Top medical conditions requiring PT treatment by area of practice PT AREAS OF PRACTICE TOP MEDICAL DIAGNOSES Orthopedic Surgery (hospital inpatient) Neuromuscular (hospital inpatients and outpatients) Medicine (hospital inpatients) General surgery (inpatients- excludes orthopedic) Hospital Adult Orthopedics Outpatients For profit (private) physiotherapy clinic Outpatient Pediatrics Inpatient Pediatrics Long Term Care (LTC) Admissions 1. Hip replacement 2. Knee replacement * 3. Hip fracture 4. Spinal surgery (laminectomy/decompression) 5. Multisystem trauma 1. Stroke/cerebrovascular accident 2. Multiple Sclerosis 3. Parkinson s 4. Traumatic brain injury 5. Spinal cord injury 1. Chronic obstructive pulmonary disease/ emphysema/ bronchitis 2. Cardiology myocardial infarction 3. Cancer care 1. Thoracic surgery/lobectomy and thoracotomy 2. Vascular surgery, abdominal aneurysm repairs and amputees 3. Cancer surgery ex. Breast cancer, bowel 4. post MI and cardiac intervention recovery 5. Critical care interventions/ intensive care unit 1. Breast cancer/shoulder/arm rehabilitation post mastectomy 2. Hand injury/surgery 3. Total knee replacement * 4. Osteoarthritis and rheumatoid arthritis 1. Back injury 2. Shoulder injury 3. Neck injury 4. Knee injury 5. Ankle injury 1. Cerebral Palsy 2. Plagiocephaly/torticollis 3. Gross motor delays screening (undiagnosed) 4. Post-op fractures 5. Post-op lower extremity surgeries (usually anterior cruciate ligament repairs) 6. Hemophilia 7. Soft-tissue injuries 1. Cancer 2. Complex medical 3. Neonatal ICU 1. PTs see 80% of new LTC patients 2. PTs see 10% of Alzheimer s and other Dementias * To avoid double counting of these patients, these patients are divided equally between the two areas of practice Page 23

34 Table 3: Top medical conditions requiring OT treatment by area of practice OT AREAS OF PRACTICE TOP MEDICAL DIAGNOSES Mental health 1. Major Depressive Disorder (Depression) 2. Anxiety 3. Bipolar Disorder 4. Schizophrenia 5. Personality disorder/ptsd 6. Addictions (Substance abuse) Neurological and 1. Stroke Complex Conditions 2. Cancer 3. Amyotrophic lateral sclerosis 4. Amputations and complications of diabetes 5. Traumatic brain injury 6. Spinal cord injury 7. Multiple sclerosis Orthopedic 1. Arthritis-osteoarthritis, rheumatoid arthritis 2. Hip replacement 3. Knee replacement 4. Hip fracture 5. Back injury Pediatric 1. Autism and autism spectrum 2. Cerebral palsy 3. Down s syndrome 4. ADHD 5. Development coordination disorder 6. Spina bifida 7. Neonatal intensive care Long Term Care 1. Long term care admissions To obtain the number of PT/OT hours required the mean treatment time for each medical condition Y was multiplied by the number of patients with medical condition Y. To convert the PT/OT hours required to number of PTs and OT required, information regarding annual hours worked was utilized. For the years 2008 to 2012, information about each PT and OT from the province was obtained from the Canadian Institute for Health Information (CIHI) including self-reported annual hours worked, which allowed for the extraction of average annual hours Page 24

35 worked for PTs and OTs by five-year age cohorts for the year 2012 (Table 4). It was assumed that the average annual hours would not change with time. Table 4: PTs and OTs average annual hours worked by five-year age cohorts, 2012 Age cohorts PTs average annual hours worked, 2012 OTs average annual hours worked, years 1,492 1, years 1,657 1, years 1,385 1, years 1,691 1, years 1,438 1, years 1,696 1, years 1,703 1, years 1,483 1, years 920 1,150 Number of PTs 1,800 1,600 1,400 1,200 1, Year (a) Number of OTs Year (b) Figure 5: Demand for the (a) physiotherapy workforce and (b) occupational therapy workforce Page 25

36 The distribution of PTs and OTs by the nine age cohorts were determined for the years 2012 to 2055 based on the resulted obtained from the system dynamics model (Appendix D and E); the SD model is explained in section 4.2. Multiplying the average distribution by their respective average annual hours worked for each age cohort and then summing them all yields the mean annual hours worked per PT and OT for each year of the modeling period. By dividing the PT and OT hours required by the mean annual hours per PT and OT, respectively, the total number of PTs and OTs required was calculated (Figure 5). To examine differences in changing demand, the incidence rates calculated on the provincial level were used to project the future patient population for each region. The provincial-level incidence rates of each medical condition, by 15-year age cohorts, were multiplied with each regions population projections (Appendix F, G, H, and I). The use of provincial-level incidence rates to project regional level patient populations assumes that trends observed to the changes to incidence of medical conditions on the provincial-level are true on the regional level. 4.2 PROVIDER POPULATION (SUPPLY) An SD model was developed to project the supply of PTs and OTs to the year 2055, the supply was then compared to their respective demands to determine the gap. The SD model was also used to test scenarios, specifically the opening of a PT and OT program at MUN to examine how the increased influx of PTs and OTs into the province would affect the gap between demand and supply. Page 26

37 4.2.1 Stock and flow based model Stocks represent accumulations that can be altered only by inflows and outflows [49]; inflows add to a stock whereas outflows reduce a stock. The quantity of inflow and outflow is moderated by valves dictating the rate of change of a stock (Figure 6). Flows can be determined through additional variables, which allows for better understanding of all variables that play into the changing of an inflow or outflow [49] (Figure 7). The rate of change of the stock is governed by the net flow for the stock and is mathematically related to flows by Equation 2 where stock (t0) is the initial size of the stock. = + Eqn 1 stock inflow outflow Figure 6: Simple stock and flow structure [49] S1 Exogenous Input Constant Figure 7: Information feedback between stocks and flows [49] Page 27

38 + - birth rate population death rate fractional birth rate average lifetime Figure 8: Population causal loop diagram [49] Causal loops are another tool that can be used for the purposes of capturing feedback structures present within a system [49]. It depicts the cause and effect relationship between dependent and independent variables where the link between variables is represented by arrows. A simple population model can be used to illustrate the different aspects of a causal loop diagram (Figure 8). As birth rate increases it causes in increase in population, above what it might otherwise have been. Moreover, an increase in population results in an increase birth rate, above what it might have otherwise been. Birth rate and population act as both the dependent and independent variable. The loop created by these two variables is called a reinforcing loop, indicating that the loop is positive [49]. A balancing loop, or negative feedback, is present between population and death rate [49]. An SD model was developed, through the use of Vensim Professional 32 software, to project the PT and OT workforce from the years 2012 to 2055, with both workforces divided into five-year age cohorts to factor in age-related differences in entry and attrition. Both the physiotherapy and occupational therapy workforces Page 28

39 were modeled through the use of stocks and flows to depict the status quo case, where no changes are made to the current PT and OT health landscape. For the physiotherapy and occupational therapy model, nine stocks were utilized to represent each of the five-year age cohorts (Figure 9). The model was populated using data obtained from CIHI, which detailed record-level information pertaining to all registered physiotherapists and occupational therapists in the province between the years 2008 to The model s data elements include initial workforce size, average number of PTs and OTs entering the workforce, and average attrition rate by five-year age cohorts to factor in age differences in rates. The initial time of the SD model was set to the year 2012, and therefore the initial values of the stocks for the PT and OT models were set as the number of employed PTs and OTs in the year 2012 based on the CIHI data (Figure 10). Figure 9: The use of stocks and flows to depict the aging of a population Page 29

40 Number of PTs/OTs Number of employed PTs, 2012 Number of employed OTs, Age cohort Figure 10: Number of employed PTs and OTs in NFLD, 2012 Table 5: Number of employed PTs by Regional Integrated Health Authorities, 2012 Regional Integrated Health Authority Age cohort EHA CHA WHA LGHA Page 30

41 Table 6: Number of employed OTs by Regional Integrated Health Authorities, 2012 Regional Integrated Health Authority Age cohort EHA CHA WHA LGHA The supply of physiotherapists and occupational therapists were projected for each of NFLDs four integrated health authority regions so as to compare the regional demand to their respective supply. As with the provincial-level model, the initial time was set to Therefore, the initial stock values for each health authority, obtained from the CIHI data, were for the year 2012 (Table 5 and ) Inflows The CIHI data for the PT and OT workforce detailed the year of entry of PTs and OTs into the workforce for every registered PT and OT in the province. The number of PTs and OTs entering the workforce between the years 2000 to 2012 were counted by five-year age cohorts (Figure 11). To calculate the average number of PTs and OTs entering the workforce (Figure 12) these values were divided by the length of the time period used (13 years). Page 31