Role of Interprofessional Primary Care Teams in Preventing Avoidable Hospitalizations and Hospital Readmissions in Ontario, Canada: A Retrospective Cohort Study

Abstract

Background: Improving health system value and efficiency are considered major policy priorities internationally. Ontario has undergone a primary care reform that included introduction of interprofessional teams. The purpose of this study was to investigate the relationship between receiving care from interprofessional versus non-interprofessional primary care teams and ambulatory care sensitive condition (ACSC) hospitalizations and hospital readmissions.

Methods: Population-based administrative databases were linked to form data extractions of interest between the years of 2003–2005 and 2015–2017 in Ontario, Canada. The data sources were available through ICES. The Study Design was a Retrospective longitudinal cohort. We used a “difference-in-differences” approach for evaluating changes in ACSC hospitalizations and hospital readmissions before and after the introduction of interprofessional team-based primary care while adjusting for physician group, physician and patient characteristics.

Principle Findings: As of March 31st, 2017, there were a total of 778 physician groups, of which 465 were blended capitation Family Health Organization (FHOs); 177 FHOs (22.8%) were also interprofessional teams and 288 (37%) were more conventional group practices (“non-interprofessional teams”). In this period, there were a total of 13,480 primary care physicians in Ontario of whom 4,848 (36%) were affiliated with FHOs—2,311 (17.1%) practicing in interprofessional teams and 2,537 (18.8%) practicing in non-interprofessional teams. During that same period, there were 475,611 and 618,363 multi-morbid patients in interprofessional teams and non-interprofessional teams respectively out of a total of 2,920,990 multi-morbid adult patients in Ontario. There was no difference in change over time in ACSC admissions between interprofessional and non-interprofessional teams between the pre- and post intervention periods. There were no statistically significant changes in all cause hospital re-admissions between the post- and pre-intervention periods for interprofessional and non-interprofessional teams.

Conclusion: Our study findings indicate that the introduction of interprofessional team-based primary care was not associated with changes in ACSC hospitalization or hospital readmissions. The findings point for the need to couple interprofessional team-based care with other enablers of a strong primary care system to improve health services utilization efficiency.

Introduction

Improving health system value and efficiency are considered major policy priorities internationally.^, While health system costs continue to be a challenge across jurisdictions, hospitalizations for ambulatory care sensitive conditions (ACSCs) and hospital readmissions have been a focus for policymakers.^,,, ACSC hospitalizations are potentially avoidable by preventing the inception of disease, controlling an acute episodic illness, or managing a chronic condition effectively. When care is delivered to patients when and where they need it, hospital readmissions can sometimes be prevented. Evidence has suggested a link between the burden of multi-morbidity and health services use, particularly hospitalizations.^,,, Hence, multi-morbid patients continue to be a key focus from a clinical care and population health perspective.^,,, Interprofessional team-based care may have an important role to play in caring for multi-morbid patients by offering a collaborative approach to prevent ACSC hospitalization and hospital readmissions.

During the 1990s, federal and provincial governments in Canada faced fiscal challenges that resulted in limited healthcare spending and investments in primary care innovation. In the 2000s, Ontario introduced primary care reform in response to the recommendations of various federal and provincial reports.^, Primary care reform movement in Ontario included three major policy initiatives: new physicians’ reimbursement and organizational models, patient enrolment with a primary care provider and support to interprofessional team-based care.²⁴ During the last twenty years, more than one third of Ontario primary care physicians have voluntarily transitioned from traditional fee-for-service practice to blended capitation payment and in some cases received additional funding to support interprofessional team members to join their practice. These models are described in detail elsewhere. There are many similarities between Ontario interprofessional Family Health Teams, Quebec Family Medicine Groups, Alberta Primary Care Networks and the Patient-Centered Medical Home in the United States (US).^,,,

In Ontario, reducing hospitalization for ACSC conditions and all-cause re-admission are strategic priorities.^, In this study, we examined the association between the introduction of primary care interprofessional teams and unplanned ACSC hospital admissions and all cause hospital re-admissions among multi-morbid patients. We compared changes in those outcomes over time among physicians remunerated through the same physician payment model, some of whom transitioned to interprofessional team-based practice. We hypothesised that multi-morbid patients who receive care from an interprofessional teams will have lower ACSC hospital admissions and all-cause readmissions over time when compared to patients receiving care from non-interprofessional teams.

Methods

Setting

The setting was Ontario, Canada, the country’s most populous province with a population of 14.4 million people in 2019. Permanent residents of Ontario are fully insured for primary care services through the Ontario Health Insurance Plan (OHIP) with no co-payment or deductible. Primary care organization and payment has shifted over the course of the last 18 years. In 2002, primary care physicians billed fee-for-service and worked independently. Today, most Ontario physicians are paid through some form of blended payment and are part of an organised model with formal patient enrolment. The three dominant practice models in Ontario are: enhanced fee-for-service (85% fee-for-service, 15% capitation and bonuses, no funding for non-physician health professionals); non-interprofessional team blended capitation (20% fee-for-service, 80% capitation and bonuses, no funding for non-physician health professionals), and interprofessional team blended capitation (20% fee-for-service, 80% capitation and bonuses, and funding for non-physician health professionals). Approximately one in six Ontarians is not formally enrolled to a physician practicing in a new model. The focus of this study was on the dominant blended capitation patient enrolment model—Family Health Organization (FHO)—within which groups of physicians can be practicing in either interprofessional or non-interprofessional teams. FHO have formal patient enrollment, electronic medical records, physician-led governance and a minimum of three physicians practicing together. They offer comprehensive care, including preventive health care services, chronic disease management and health promotion, through a combination of regular physician office hours and after-hours services. FHOs were eligible to apply for additional funding to become interprofessional teams and typically include primary care physicians and nurses or nurse practitioners and at least one allied health care professional such as pharmacist, social worker or dietitian. Interprofessional teams are also eligible for funding an administrator or executive director and electronic medical records.

Study design and population

We conducted a retrospective cohort study with longitudinal design given the importance of temporal effect on interprofessional teams formation and maturation and their relationship to the outcomes under investigation. We used the “difference in differences” approach, an econometric method for evaluating changes in outcomes after policy implementation. We compared outcomes of interest before and after the implementation of interprofessional teams.

Several population-based administrative databases were linked using unique encoded identifiers at ICES (formerly known as the Institute for Clinical Evaluative Sciences) to form data extractions of interest. We generated a cohort that included the same patients at two different points in time, pre- and post-teams’ formation. The study population included patients between 18 and 105 years old, who had two or more of a list of 17 chronic conditions as identified at the beginning of the pre-teams’ formation period, March 31st 2003 and who were part of a FHO blended capitation model as identified at the beginning of the post-teams formation period, March 31st, 2015. The chronic condition selection was based on clinical relevance and impact on the outcomes being investigated as described in previous literature.^,,,,, These conditions have been adopted in previous studies ^, and are consistent with the parameters outlined by the Department of Health and Human Services for defining and measuring chronic conditions. The conditions include: cancer, diabetes, asthma, chronic obstructive pulmonary disease (COPD), hypertension, chronic coronary syndrome (CCS), cardiac arrhythmia, congestive heart failure (CHF), stroke, acute myocardial infarction (AMI), renal failure, arthritis (excluding rheumatoid arthritis), rheumatoid arthritis, osteoporosis, depression, dementia and mental health conditions (full list of diagnostic information for defining the 17 selected chronic conditions under investigation in this study are included in Appendix A).

The baseline study population included people identified on March 31st, 2003 who were still identifiable on March 31st, 2015 and were part of the FHO blended capitation model. People in the baseline population were followed-up to February 28th, 2005 for first unplanned ACSC admission and up to March 31st, 2005 for first all-cause readmission and in the follow up period up to February 28th, 2017 for the first ACSC admission and up to March 31st, 2017 for all-cause readmission. Given that teams did not exist during the baseline period, assignment of patients to interprofessional and non-interprofessional teams was based on their post-intervention assignment. We excluded individuals who died and individuals who were in long term care or complex continuing care.

Measures and data sources

ACSC Admission and Hospital Re-admission

The primary outcome was hospital admissions for ACSCs, defined as the first hospital non-elective admission with a most responsible diagnosis code of: grand mal status and other epileptic convulsions, chronic obstructive pulmonary disease (COPD), asthma, diabetes, heart failure and pulmonary edema, hypertension and angina.

The secondary outcome was hospital readmissions, defined as the first subsequent non-elective all-cause readmission to an acute care hospital within 30 days of discharge, among hospitalisation for selected Case Mix Group (CMG) groups: stroke, COPD, pneumonia, congestive heart failure, diabetes, cardiac conditions, gastrointestinal conditions (List of CMGs codes in Appendix B). The primary and secondary outcomes were derived from the OHIP database and the Discharge Abstract Database (DAD) and the Registered Patient Database (RPDB) available at ICES. Both outcomes excluded people without a valid date of admission/discharge; and people who died during their hospital stay (relevant to admission but not readmission).

Physician Group and Physicians Characteristics

Physician group characteristics included the number of physicians per group and number of years under the capitation model. Physicians’ characteristics included age, gender, Canadian graduate status and number of years in practice. Those variables were derived from a health care provider data registry available at ICES.

Patient Characteristics

Patients’ characteristics included age, gender and recent OHIP registration as a proxy for immigration which were identified from a population and demographics data registry available at ICES. By linking patients’ postal code to census data we were able to derive neighborhood income quintiles. Income levels, adjusted for household size and specific to each community, were used to order postal codes into quintiles, with quintile 1 having the lowest relative income and quintile 5 the highest. Rurality was identified using the Ontario Medical Association Rurality Index of Ontario (RIO). The RIO is based on community characteristics including travel time to different levels of care, community population, presence of providers, hospitals and ambulance services, social indicators and weather conditions. RIO scores range from zero to 100 (zero indicating the most urban and 100 the most rural). RIO scores are divided into three main categories, major urban centres, semi-urban centres and rural areas. We used the Johns Hopkins Adjusted Clinical Groups case-mix system software to assign patients into expected Resource Utilization Bands (RUBs) categories. The RUBs range from 0 indicating no utilization to 5 indicating very high expected utilization.

Six chronic diseases conditions (AMI, asthma, CHF, COPD, hypertension, diabetes) were defined based on previously validated population-derived ICES cohorts.^,,,,, For the conditions where a derived ICES cohort was not available (cancer, cardiac arrhythmia, chronic coronary syndrome, dementia, depression, arthritis (excluding rheumatoid arthritis), osteoporosis, renal failure, rheumatoid arthritis, and stroke), a similar approach for the derivation was adopted—at least one diagnosis recorded in acute care, or two diagnoses recorded in physicians’ records within a two-year period. The conditions were derived using the DAD and OHIP databases available at ICES.

Results

Baseline physician group, physician and patient characteristics comparing interprofessional teams to non-interprofessional teams

As of March 31^st, 2017, there were a total of 778 physician groups in Ontario, of which 465 were FHOs; 177 FHOs (22.8%) were also interprofessional teams and 288 (37%) were non-interprofessional teams. Compared to non-interprofessional teams, interprofessional teams had: more physicians per group and more years under the capitation model.

In this period, there were a total of 13,480 primary care physicians in Ontario of whom 4,848 (36%) were affiliated with FHOs, 2,311 (17.1%) practicing in interprofessional teams and 2,537 (18.8%) practicing in non-interprofessional teams. Compared to non-interprofessional teams, interprofessional teams had: fewer patients per physician, more female physicians, more physicians in the younger age group, more physicians who were Canadian graduates and fewer years in practice (Table 1A).

During the same period, there were 475,611 and 618,363 multi-morbid patients in interprofessional and non-interprofessional teams respectively out of a total of 2,920,990 multi-morbid adult patients in Ontario. Overall interprofessional teams had fewer new immigrant patients and more patients who reside in rural areas. Other patient characteristics were relatively similar between interprofessional and non-interprofessional teams. When compared to all physician groups, both interprofessional and non-interprofessional teams had less patients with high number of co-morbidities (Table 1B).

ACSC hospital admissions and all cause 30-day re-admissions in interprofessional teams and non-interprofessional teams by physician and patient characteristics

During the period of April 1st, 2015 to March 31st, 2017, interprofessional teams were found to have higher ACSC admission rates when compared to non-interprofessional teams (2.5% versus 2.1%, respectively). When we investigated ACSC admissions during the same period across interprofessional and non-interprofessional teams by physician characteristics identified on March 31st, 2015, we found that the following were associated with higher ACSC admission rates: being a male, being in the older age group, and being a non-Canadian graduate (Table 2A).

During that same period, when we investigated ACSC admission across interprofessional and non- interprofessional teams in relation to the patient characteristics identified on March 31st, 2003, we found that the following patient characteristics were associated with higher ACSC admission rate: being a male, being in the older age category, being a non-immigrant, being in the lowest neighborhood income quintile, being a resident of a rural area, being in the highest expected resource utilization band, and having five and plus co-morbidities (Table 2B).

During that same period, interprofessional teams were found to have slightly higher all cause hospital 30-day re-admission rate when compared to non-interprofessional teams (15.0% versus 14.6%, respectively).

When we investigated hospital re-admission during the same period across interprofessional and non-interprofessional teams by physician characteristics identified on March 31st, 2015, being a non-Canadian graduate physician was associated with higher re-admission rate (Table 3A).

During that same period, when we investigated hospital re-admission across interprofessional and non-interprofessional teams in relation to the patient characteristics identified on March 31st, 2003, we found that the following were associated with higher 30-day re-admission rate: being a male, being in the older age category, residing in major urban areas, being in the highest expected resource utilization band, and having five or more co-morbidities (Table 3B).

When we stratified the results by males and females for both outcomes, we did not identify sex differences (results not presented but can be made available on request).

Association between enrolment in an interprofessional team model and ACSC hospital admission and all cause hospital re-admission

During the post-intervention period, when we adjusted for physician group, physician and patient characteristics, being in an interprofessional team increased the likelihood of having ACSC hospital admission by 7%. For the same period, we did not find significant difference between interprofessional and non-interprofessional teams for hospital all cause readmission (Table 4).

When we examined change over time between the post- and pre-intervention periods, there was a significant increase in the ACSC hospital admission rate: 1.34% for both interprofessional and non-interprofessional teams. There was no difference between interprofessional and non-interprofessional teams in the change in ACSC admissions across the pre- and post-intervention periods.

For the same period, when we compared for change over time between the post- and pre intervention there was a significant difference in hospital all cause re-admission rate with an increase of 4.90% for interprofessional teams and an non-significant increase for non-interprofessional teams of 1.47%. We found a non-significant difference between interprofessional and non-interprofessional teams in the change in hospital all cause re-admissions between the pre- and post-intervention periods, 3.43% (Table 5).

Discussion

We used administrative databases to assess the association between receiving care from interprofessional and non-interprofessional primary care teams and unplanned ACSC hospitalizations and all cause hospital readmissions among multi-morbid patients. We followed the same patients before and after teams were implemented which allowed an assessment of the effect of the intervention—introduction of interprofessional team-based care. When we investigated the outcomes during the most recent available period of April 1st, 2015 to March 31st, 2017 interprofessional teams were found to have higher ACSC admission and hospital re-admission rates as compared to non-interprofessional teams. However, when we compared the outcomes over time, interprofessional teams were not associated with either an increase or a reduction of ACSC hospital admission and hospital re-admission.

The results are consistent with previous evidence that looked at utilization in relation to interprofessional team-based care and found differences in quality but not in healthcare utilization and cost.^,,, One US study that evaluated the effect of multiplayer patient-centred medical home on healthcare utilization did not find a significant reduction in inpatient admissions. In contrast, several studies from the US assessed multiple components of the medical home model on health services utilization and found significant lower rates of avoidable hospitalization when more medical homeness was incorporated in the health system.^,, Implementation of Family Health Teams appeared to contribute to a reduction in ACSC hospitalizations in a Brazilian metropolis, Belo Horizonte.

There is a body of evidence that links chronic disease management programs to lower preventable hospitalizations.^,,, In Ontario, patients being served by both interprofessional and non-interprofessional teams have access to certain chronic disease programs including diabetes education and heart failure clinics. This could be one of the reasons for the absence of difference in our study between receiving care from interprofessional and non-interprofessional teams in ACSC hospitalizations. Additionally, there is heterogeneity of interprofessional teams features across Ontario. For instance, some interprofessional teams are co-located others are not. Hence, some interprofessional teams might not be ideally set up for care coordination and continuity of care. Continuity of care might be reduced within interprofessional teams if they are not well coordinated and might present a potential for fragmented care. Available evidence from a systematic review suggests that having an accessible and a long-term relationship with a primary care provider appeared to be more important in reducing potentially avoidable hospitalizations than how the primary care delivery is organized. Long-term relationships between primary care physicians and patients reduces hospitalizations for chronic ACSCs and continuity of care has been associated with both reduced health services utilization and patient satisfaction. ^,, Continuity of care is critical to ensuring that everyone with chronic medical needs receive effective, timely and safe health care.

Based on Startfield’s model a strong primary care system should be the first contact for care, as well as continuous, comprehensive and well-coordinated to reduce unwanted outcomes such as preventable hospitalizations. It is important for any jurisdiction that has embarked on or is planning to set up primary care interprofessional team-based care to nurture all these enablers for a strong primary care system.

Our study has several limitations that should be acknowledged. First, administrative databases have not been originally set up for research purposes, which presented a potential for measurement error. However, all the databases used in our study have been validated in Ontario’s context. Additionally, any potential measurement error will be non-deferential between interprofessional and non-interprofessional teams and should not bias the results in a meaningful way. Second, this is an observational study and is susceptible to unmeasured confounding. However, by comparing the outcomes over time, potential risk of bias from unmeasured confounders was limited. Third, due to the adopted study design, to be included in the study population, patients had to survive throughout the study period—April 1st, 2003 to March 31st, 2017. However, a potential survival bias would have affected both interprofessional and non-interprofessional teams’ patients equally and does not present a threat to internal validity. Fourth, ACSC medical admissions and all-cause readmissions are not all unnecessary and preventable.

Conclusion

Our study findings indicate that the introduction of interprofessional team-based primary care was not associated with reduction in avoidable hospitalizations and hospital readmissions. Those results were not in-line with our hypothesis as we expected that, over time, interprofessional teams would reduce the likelihood of ACSC admissions and re-admissions. For jurisdictions aiming to expand physician participation in teams, our study results point to the need to couple interprofessional team-based care with other enablers of a strong primary care system such as access, continuity, comprehensiveness and coordination. Policies and practices that enhance those features will help to implement interprofessional team-based care in a way that it is best able to deliver on intended outcomes such as improving health services utilization efficiency.

List Of Abbreviations

Declarations

Ethics approval and consent to participate:

ICES (formerly known as Institute for Clinical Evaluative Sciences) is a prescribed entity under section 45 of Ontario’s Personal Health Information Protection Act. Section 45 authorizes ICES to collect personal health information, without consent, for the purpose of analysis or compiling statistical information with respect to the management of, evaluation or monitoring of, the allocation of resources to or planning for all or part of the health system. Projects conducted under section 45, by definition, do not require review by a Research Ethics Board. This project was conducted under section 45, and approved by ICES’ Privacy and Legal Office.

Consent for publication:

Not applicable

Availability of data and materials:

The dataset from this study is held securely in coded form at ICES. While data sharing agreements prohibit ICES from making the dataset publicly available, access may be granted to those who meet pre-specified criteria for confidential access, available at www.ices.on.ca/DAS. The full dataset creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification.

Competing interests:

The authors declare that they have no competing interests

Funding:

This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). ICES is an independent, non-profit research institute funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). As a prescribed entity under Ontario’s privacy legislation, ICES is authorized to collect and use health care data for the purposes of health system analysis, evaluation and decision support. Secure access to these data is governed by policies and procedures that are approved by the Information and Privacy Commissioner of Ontario. Parts of this material are based on data and information compiled and provided by the Canadian Institute for Health Information (CIHI). The analyses, conclusions, opinions and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred. Richard H. Glazier is supported as a Clinician Scientist in the Department of Family and Community Medicine at St. Michael’s Hospital and at the University of Toronto.

Authors' contributions:

WHA: Conceptualization, Methodology, Formal Analysis, Writing—Original Draft. RM: Conceptualization, Methodology, Formal Analysis, Writing—Review & Editing.BH: Conceptualization, Methodology, Writing—Review & Editing, Supervision. WPW: Conceptualization, Methodology, Writing—Review & Editing, Supervision. RHG: Conceptualization, Methodology, Writing—Review & Editing, Supervision.

Acknowledgements:

Not applicable

Appendix

These conditions represent a subset of all possible chronic conditions that may be experienced by individuals over a lifetime but represent the most substantial conditions from a population perspective.

References

1. Chisholm D, Evans DB. Improving health system efficiency as a means of moving towards universal coverage. World Health Report (2010), Background Paper, 28 [Internet]. 2010. Available: http://www.who.int/healthsystems/topics/financing/healthreport/28UCefficiency.pdf. Accessed 02 February 2016.
2. Berwick DM, Nolan TW, Whittington J. The Triple Aim: Care, Health, And Cost. Health Aff (Millwood).
   2008; 27(3):759–69. doi: 10.1377/hlthaff.27.3.759
3. S. F. Jencks, M. V. Williams and E. A. Coleman, “Rehospitalizations Among Patients in the Medicare Fee-for-Service Program,” New England Journal of Medicine 360, 14 (2009): pp. 1418–1428.
4. K. L. Soeken et al., “Predictors of Hospital Readmission: A Meta-Analysis,” Evaluation and the Health Professions 14, 3 (1991): pp. 262–281.
5. S. E. Frankl, J. L. Breeling and L. Goldman, “Preventability of Emergent Hospital Readmission,” American Journal of Medicine 90, 6 (1991): pp. 667–674.
6. G. F. Anderson and E. P. Steinberg, “Hospital Readmissions in the Medicare Population,” New England Journal of Medicine 311, 21 (1984): pp. 1349–1353.
7. Billings J, Zeitel L, Lukomnik J, Carey TS, Blank AE, Newman L. Impact of socioeconomic status on hospital use in New York City. Health Aff (Millwood). 1993;12(1):162-173.
8. Brown, R.S., Peikes, D., Peterson, G., Schore, J., Razafindrakoto, C.M. Six Features of Medicare Coordinated Care Demonstration Programs That Cut Hospital Admissions of HighRisk Patients. 2012; Health Affairs, 31(6), 1156-1166.
9. Canadian Institute for Health Information. Seniors and the Health Care System: What is the Impact of Multiple Chronic Conditions. Toronto: Canadian Institute for Health Information; 2011.
10. Freund T, Kunz CU, Ose D, Peters-Klimm F. Patterns of multimorbidity in primary care patients at high risk of future hospitalization. Popul Health Manag. 2012;15:119–24.
11. Marengoni A, Fratiglioni L. Disease clusters in older adults: rationale and need for investigation. J Am Geriatr Soc. 2011;59:2395–6.
12. Lochner KA, Goodman RA, Posner S, Parekh A. Multiple chronic conditions among Medicare beneficiaries: state-level variations in prevalence, utilization, and cost, 2011. Medicare Medicaid Res Rev. 2013;3(3).
13. C. Boult et al., “Screening Elders for Risk of Hospital Admission,” Journal of the American Geriatrics Society 41, 8 (1993): pp. 811–817.
14. C. van Walraven et al., “Derivation and Validation of an Index to Predict Early Death or Unplanned Readmission After Discharge From Hospital to the Community,” CMAJ 182, 6 (2010): pp. 551–557.
15. H. M. Krumholz et al., “Readmission After Hospitalization for Congestive Heart Failure Among Medicare Beneficiaries,” Archives of Internal Medicine 157, 1 (1997): pp. 99–104.
16. J. S. Weissman, R. S. Stern and A. M. Epstein, “The Impact of Patient Socioeconomic Status and Other Social Factors on Readmission: A Prospective Study in Four Massachusetts Hospitals,” Inquiry 31, 2 (1994): pp. 163–172.
17. Hutchison, B., LEVESQUE, J. F., Strumpf, E., & Coyle, N. (2011). Primary health care in Canada: systems in motion. Milbank Quarterly, 89(2), 256-288.
18. Commission on the Future of Health Care in Canada. Building on values: the future of health care in Canada — final report [Internet]. Saskatoon (SK): Government of Canada; 2002. Available from: http://publications.gc.ca/collections/Collection/CP32-85-2002E.pdf
19. Standing Senate Committee on Social Affairs, Science, and Technology. The health of Canadians —federal role—final report. Vol. 6, Recommendations for reform [Internet]. Ottawa (ON); Parliament of Canada; Available from: http://www.parl.gc.ca/Content/SEN/Committee/372/soci/rep/repoct02vol6-e.htm
20. Hutchison B, Glazier R. Ontario’s primary care reforms have transformed the local care landscape, but a plan is needed for ongoing improvement. Health affairs. 2013 Apr 1;32(4):695-703. DOI: https://doi.org/10.1377/hlthaff.2012.1087
21. Coyle N, Strumpf E, Fiset-Laniel J, Tousignant P, Roy Y. Characteristics of physicians and patients who join team-based primary care practices: Evidence from Quebec's Family Medicine Groups. Health Policy. 2014 Jun 1;116(2-3):264-72. DOI: https://doi.org/10.1016/j.healthpol.2014.02.010
22. American Academy of Family Physicians. American college of physicians, American osteopathic association. Joint principles of the patient-centered medical home. 2007 Mar;3. Available at: DOI: https://www.aafp.org/dam/AAFP/documents/practice_management/pcmh/initiatives/PCMHJoint.pdf
23. Rosenthal TC. The medical home: growing evidence to support a new approach to primary care. The Journal of the American Board of Family Medicine. 2008 Sep 1;21(5):427-40. DOI: https://doi.org/10.3122/jabfm.2008.05.070287
24. Patients Medical Home available at: https://patientsmedicalhome.ca/ Accesses on 31 December 2019.
25. Anderson G.F. & Steinberg E.P. Hospital readmissions in the Medicare population. N Engl J Med. 1984 Nov 22; 311(21):1349-53.
26. Excellent Care for All. Available from: http://www.health.gov.on.ca/en/pro/programs/ecfa/action/acute/hsp_thc.aspx.
27. Ontario Ministry of Finance. Ontario Fact Sheet April 2016 Available from: https://www.fin.gov.on.ca/en/economy/ecupdates/factsheet.html Accessed 31 Dec 2019.
28. Dimick JB, Ryan AM. Methods for evaluating changes in health care policy. JAMA. 2014;312(22):2401-2402.
29. Health Canada. Economic Burden of Illness in Canada, 1998. Public Health Agency of Canada. Ottawa, ON: 2002. Available from: http://publications.gc.ca/collections/Collection/H21-136-1998E.pdf. Accessed: June 19, 2016.
30. Hanna A. Ontario medical association policy on chronic disease management. Toronto, ON: OMA; 2009.
31. Statistics Canada. Tables by subject: Diseases and health conditions. http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/ind01/l3_2966_1887-eng.htm?hili_health03. Accessed June 19, 2016.
32. Canadian Institute for Health Information. Seniors and the health care system: what is the impact of multiple Chronic Conditions? Toronto: CIHI; 2011.
33. World Health Organization. Global status report on non-communicable diseases. Geneva: WHO; 2010.
34. The Chief Public Health Officer. Annual Report on the State of Public Health in Canada, 2010. Ottawa: Public Health Agency of Canada; 2010.
35. Pefoyo, A. J. K., Bronskill, S. E., Gruneir, A., Calzavara, A., Thavorn, K., Petrosyan, Y., ... & Wodchis, W. P. (2015). The increasing burden and complexity of multimorbidity. BMC Public Health, 15(1), 1.
36. Gruneir, A., Bronskill, S. E., Maxwell, C. J., Bai, Y. Q., Kone, A. J., Thavorn, K., ... & Wodchis, W. P. (2016). The association between multimorbidity and hospitalization is modified by individual demographics and physician continuity of care: a retrospective cohort study. BMC health services research, 16(1), 1.
37. Goodman RA, Posner SF, Huang ES, Parekh AK, Koh HK. Defining and Measuring Chronic Conditions: Imperatives for Research, Policy, Program, and Practice. Prev Chronic Dis 2013; 10:120239. DOI: http://dx.doi.org/10.5888/pcd10.120239
38. Kralj B. (2008). Measuring Rurality –RIO2008 BASIC: Methodology and Results. Toronto: Ontario Medical Association
39. The Johns Hopkins University. The Johns Hopkins ACG System. Accessed March 25, 2015 at http://acg.jhsph.org/index.php
40. Austin PC, Daly PA, Tu JV. A multicenter study of the coding accuracy of hospital discharge administrative data for patients admitted to cardiac care units in Ontario. Am Heart J. 2002;144(2):290–6. doi:S0002870302000819. Koné Pefoyo et al. BMC Public Health (2015) 15:415 Page 10 of 11
41. Gershon AS, Wang C, Guan J, Vasilevska-Ristovska J, Cicutto L, To T. Identifying patients with physician-diagnosed asthma in health
    administrative databases. Can Respir J. 2009;16(6):183–8.
42. Schultz SE, Rothwell DM, Chen Z, Tu K. Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records. Chronic Dis Inj Can. 2013;33(3):160–6.
43. Hux JE, Ivis F, Flintoft V, Bica A. Diabetes in Ontario: determination ofprevalence and incidence using a validated administrative data algorithm.Diabetes Care. 2002;25(3):512–6.
44. Guttmann A, Nakhla M, Henderson M, To T, Daneman D, Cauch-Dudek K, et al. Validation of a health administrative data algorithm for assessing the epidemiology of diabetes in Canadian children. Pediatr Diabetes. 2010;11(2):122–8. doi:PDI539. 10.1111/j.1399-5448.2009.00539.x.
45. Tu K, Campbell NR, Chen ZL, Cauch-Dudek KJ, McAlister FA. Accuracy of administrative databases in identifying patients with hypertension. Open Med. 2007;1(1):e18–26.
46. Kiran T, Kopp A, Moineddin R, Glazier RH. Longitudinal evaluation of physician payment reform and team-based care for chronic disease management and prevention. CMAJ. 2015 Nov 17;187(17):E494-502. DOI: https://doi.org/10.1503/cmaj.150579
47. Strumpf E, Ammi M, Diop M, Fiset-Laniel J, Tousignant P. The impact of team-based primary care on health care services utilization and costs: Quebec’s family medicine groups. Journal of health economics. 2017 Sep 1;55:76-94. DOI: https://doi.org/10.1016/j.jhealeco.2017.06.009
48. Glazier RH, Hutchison B, Kopp A. Comparison of family health teams to other Ontario primary care models, 2004/05 to 2011/12. Toronto: Institute for Clinical Evaluative Sciences. 2015.
49. Glazier RH, Zagorski BM, Rayner J. Comparison of Primary Care Models in Ontario by Demographics, Case Mix and Emergency Department Use, 2008/09 to 2009/10. ICES Investigative Report. Toronto: Institute for Clinical Evaluative Sciences; 2012.
50. Rosenthal MB, Friedberg MW, Singer SJ, Eastman D, Li Z, Schneider EC. Effect of a multipayer patient-centered medical home on health care utilization and quality: the Rhode Island chronic care sustainability initiative pilot program. JAMA Intern Med 2013; 173: 1907–13.
51. Cooley WC, McAllister JW, Sherrieb K, Kuhlthau K. Improved outcomes associated with medical home implementation in pediatric primarybcare. Pediatrics 2009; 124: 358–64.
52. Fishman PA, Johnson EA, Coleman K et al. Impact on seniors of the patient-centered medical home: evidence from a pilot study. Gerontologist 2012; 52: 703–11.
53. Yoon J, Rose DE, Canelo I et al. Medical Home Features of VHA Primary Care Clinics and Avoidable Hospitalizations. J Gen Intern Med 2013; 28: 1188–94.
54. Mendonça, Claunara Schilling, et al. "Trends in hospitalizations for primary care sensitive conditions following the implementation of Family Health Teams in Belo Horizonte, Brazil." Health policy and planning 27.4 (2011): 348-355.
55. Cohen R, Lemieux J, Schoenborn J, Mulligan T. Medicare Advantage Chronic Special Needs Plan boosted primary care, reduced hospital use among diabetes patients. Health Aff (Millwood) 2012; 31: 110–9
56. Greisinger AJ, Balkrishnan R, Shenolikar RA, Wehmanen OA, Muhammad S, Champion PK. Diabetes care management participation in a primary care setting and subsequent hospitalization risk. Dis Manag 2004; 7: 325–32
57. Patel PH, Welsh C, Foggs MB. Improved asthma outcomes using a coordinated care approach in a large medical group. Dis Manag 2004; 7: 102–11.
58. Rea H, McAuley S, Stewart A, Lamont C, Roseman P, Didsbury P. A chronic disease management programme can reduce days in hospital for patients with chronic obstructive pulmonary disease. Intern Med J 2004; 34: 608–14.
59. van Loenen, T., van den Berg, M. J., Westert, G. P., & Faber, M. J. (2014). Organizational aspects of primary care related to avoidable hospitalization: a systematic review. Family practice, 31(5), 502-516.
60. Cabana MD, Jee SH. Does continuity of care improve patient outcomes?
    J Fam Pract 2004; 53: 974–80.
61. Kringos DS, Boerma WG, Hutchinson A, van der Zee J, Groenewegen PP. The breadth of primary care: a systematic literature review of its core dimensions. BMC Health Serv Res 2010; 10: 65.
62. Fishman PA, Johnson EA, Coleman K et al. Impact on seniors of the patient-centered medical home: evidence from a pilot study. Gerontologist 2012; 52: 703–11.
63. Starfield B. Is primary care essential? Lancet 1994; 344: 1129–33.