Law & Governance

Law & Governance 17(6) January 2016
Data Matters

High-Cost Users of Ontario's Healthcare Services

Saad Rais, Amir Nazerian, Sten Ardal, Yuriy Chechulin, Namrata Bains and Kamil Malikov


 [This article was originally published in Healthcare Policy, 9(1)]

Approximately 1.5% of Ontario's population, represented by the top 5% highest cost-incurring users of Ontario's hospital and home care services, account for 61% of hospital and home care costs. Similar studies from other jurisdictions also show that a relatively small number of people use a high proportion of health system resources. Understanding these high-cost users (HCUs) can inform local healthcare planners in their efforts to improve the quality of care and reduce burden on patients and the healthcare system. To facilitate this understanding, we created a profile of HCUs using demographic and clinical characteristics. The profile provides detailed information on HCUs by care type, geography, age, sex and top clinical conditions.

Studies have shown that high-cost users (hcus) of healthcare, i.e., patients who incur the highest healthcare costs, represent only a small proportion of the population but consume a large proportion of healthcare funding. In British Columbia, for example, 5% of users spent 30% of the provincial physician service funding (Reid et al. 2003). A study in Manitoba also showed that 5% of prescription drug users accounted for 41% of prescription expenditures (Kozyrskyj et al. 2005). In Manitoba, the highest 1% population accounted for 54% of hospital expenditures (Deber and Lam 2009). In the United States, 5% of the population accounted for 49% of total healthcare spending (Center for Healthcare Research and Transformation 2010). The resulting spotlight on HCUs prompted economists and policy makers to acknowledge the influence of HCUs on quality of care and cost-effectiveness of the healthcare system. Gawande's 2011 article in The New Yorker ("The Hot Spotters"), for example, garnered considerable attention from policy makers, arguing that a focus on a few areas or individuals will have significant impact on patient outcomes and system costs. A 2012 report by The Commonwealth Fund also emphasized the need to address HCUs as the first step to achieving "rapid improvements in the value of services provided."

Recognizing the importance of HCUs, the Ontario Ministry of Health and Long-Term Care used clinical and demographic patient information to profile HCUs of Ontario's hospital and home care healthcare services. This profile, as presented below, should inform the management of healthcare funding, support the development of policies and programs that provide better access, quality and value to Ontario patients, and motivate further research on HCUs.


HCUs were defined as the top 5% cost-consuming users of hospital and home care services at the provincial level during the fiscal year 2009/10. Primary care and long-term care use were excluded. The patient count, total cost and cost per patient were measured for selected demographics, care types and clinical conditions, both for HCUs and for all users. Cost was calculated using the Ontario Cost Distribution Methodology as the product of the unit cost (of a care type within a specific hospital) and the case weight (of a case-mix group) (Ministry of Health and Long-Term Care 2011).

The demographic characteristics examined were geography (by Local Health Integration Network [LHIN] of service), age group (<1, 1–17, 18–45, 45–64, 65–79, 80+) and sex. The care types included Acute In-Patient Care, Acute Day Surgery, Emergency, In-Patient Mental Health, Rehabilitation, Complex Continuing Care and Home Care. The clinical care types studied were limited to In-Patient (by major clinical category), Day Surgery (by major ambulatory cluster) and Emergency (by major ambulatory cluster).

Data used for the analysis were extracted from ministry-accessible administrative databases specific to each care type: In-Patient from the Discharge Abstract Database, Day Surgery and Emergency from the National Ambulatory Care Reporting System, Mental Health from the Ontario Mental Health Reporting System, Chronic from the Continuing Care Reporting System, Rehabilitation from the National Rehabilitation Reporting System and Home Care from the Home Care Database. Records were screened out if they represented services not covered by the Ontario Health Insurance Plan (OHIP), hospital services not funded through Ontario's case-mix funding model, or services with zero resource intensity measures. Each patient's age, sex and LHIN of service was based on his/her most recent record.

Formal ethics review was not required because de-identified ministry administrative data were used.


Tables 1 through 3 summarize the results of the analysis. Each table presents the patient count, total cost and average cost per patient both for HCUs and for all users (including HCUs) across specified characteristics. Table 1 also includes the standard deviations (SD) of average cost per patient. The tables enable comparison of measures between categories and between HCUs and all users.

Table 1. Distribution of patients and costs across demographic characteristics, 2009/10
  High-Cost Users All Users
Demographic # of Patients Total Cost ($M) Average Cost per Patient ($K) (SD) # of Patients Total Cost ($M) Average Costper Patient ($K) (SD)
   ESC 8,758 342 39.07 (37.76) 203,149 634 3.12 (11.20)
   SW 18,822 820 43.56 (48.01) 371,313 1,318 3.55 (14.43)
   WW 7,604 292 38.40 (39.46) 191,818 557 2.90 (10.92)
   HNHB 23,400 1,025 43.82 (51.79) 435,571 1,670 3.83 (15.53)
   CW 6,700 265 39.51 (42.64) 168,255 514 3.05 (11.54)
   MH 10,507 403 38.40 (40.53) 279,322 762 2.73 (10.80)
   TC 38,682 1,954 50.51 (62.69) 407,156 2,721 6.68 (24.14)
   C 14,224 546 38.35 (44.63) 386,414 1,070 2.77 (11.26)
   CE 16,157 689 42.64 (45.69) 412,740 1,237 3.00 (12.29)
   SE 8,659 378 43.68 (49.53) 181,826 624 3.43 (14.27)
   CH 20,039 940 46.89 (53.87) 372,130 1,465 3.94 (16.34)
   NSM 5,940 278 46.80 (55.41) 152,616 470 3.08 (14.21)
   NE 10,784 488 45.22 (52.49) 234,131 812 3.47 (14.73)
   NW 4,805 221 46.07 (46.24) 105,180 362 3.44 (13.79)
Age Group
   <1 5,201 311 59.79 (77.79) 161,602 540 3.34 (17.43)
   1–17 6,723 365 54.30 (76.95) 707,323 857 1.21 (9.24)
   18–44 19,976 987 49.39 (65.52) 1,240,331 2,491 2.01 (10.47)
   45–64 47,021 2,100 44.65 (53.11) 983,463 3,543 3.60 (15.03)
   65–79 59,896 2,562 42.78 (47.01) 526,686 3,687 7.00 (20.63)
   80+ 56,264 2,316 41.17 (39.92) 282,216 3,096 10.97 (23.60)
   Female 98,259 4,189 42.63 (47.56) 2,088,726 7,390 3.54 (13.71)
   Male 96,822 4.452 45.98 (54.92) 1,812,895 6,824 3.76 (16.35)
Provincial 195,081 8,641 44.29 (51.37) 3,901,621 14,214 3.64 (14.99)
ESC=Erie St. Clair; SW=South West; WW=Waterloo Wellington; HNHB=Hamilton Niagara Haldimand Brant; CW=Central West; MH=Mississauga Halton; TC=Toronto Central; C=Central; CE=Central East; SE=South East; CH=Champlain; NSM=North Simcoe Muskoka; NE=North East; NW=North West


Note that the patient count and cost per patient may not be consistent across tables because patients may have contributed to multiple categories for a given characteristic. Ninety-one per cent of HCUs received care in multiple care types, and within In-Patient, Day Surgery and Emergency, 83% of HCUs received care for multiple clinical conditions.

Table 1 presents analyses by demographic characteristics and at the provincial level. Provincially, HCUs accounted for 61% of all costs and had an average cost per patient that was 12 times that of all users. Within each LHIN, the percentage of all users that were HCUs ranged from 3.7% in Central (C) to 9.5% in Toronto Central (TC), and the percentage of total costs attributed to HCUs ranged from 51.0% in C to 71.8% in TC. TC also incurred the highest total cost and average cost per patient, among both HCUs and all users.

The 65+ age group accounted for the largest proportion (60%) of HCUs and 56% of HCU costs. Furthermore, the percentage of total costs attributed to HCUs was disproportionately higher in the 65+ age group (72%). Among HCUs, while the number of patients and total cost increased with increasing age, the average cost per patient decreased with increasing age. Thus, the age group with the highest average cost per HCU was the <1 group ($59,795), but not for all users, for whom the cost per patient increased with age (after the <1 age group). The cost per patient was slightly – but with statistical significance – higher among males versus females. The percentage of total costs attributed to HCUs was also higher among males (65% versus 57%).

Table 2 presents results by care type. In-Patient, the most costly one, represented 62% of HCU costs and 57% of all costs. Mental Health was the care type with the highest cost per HCU ($54,140). Most of Mental Health, Rehabilitation and Chronic costs – 89%, 98% and 99%, respectively – were attributed to HCUs. By contrast, only 15% of Emergency and Day Surgery costs combined were attributed to HCUs, as the cost per patient for these care types was relatively small.

Table 2. Distribution of patients and costs across care types, 2009/10
  High-Cost Users All Users
Care Type # of Patients Total Cost ($M) Average Cost per Patient ($K) # of Patients Total Cost ($M) Average Costper Patient ($K)
IP 170,035 5,365 31.55 819,971 8,096 9.87
DS 54,775 129 2.35 968,344 1,158 1.20
ER 158,667 233 1.47 2,926,568 1,319 0.45
MH 14,868 805 54.14 35,517 904 25.45
Rehab 23,239 465 20.01 25,536 477 18.68
CCC 16,852 824 48.92 18,265 833 45.61
HC 114,270 819 7.17 430,465 1,427 3.32
IP=In-Patient; DS=Day Surgery; ER=Emergency; MH=Mental Health; Rehab=Rehabilitation; CCC=Chronic Continuing Care; HC=Home Care


Table 3 presents the top five cost-incurring clinical conditions among HCUs for In-Patient, Day Surgery and Emergency. In total, there are 21 conditions in In-Patient, 19 in Day Surgery and 19 in Emergency. The top five conditions accounted for 59% of all HCU costs in In-Patient, 81% in Day Surgery and 63% in Emergency.

Table 3. Distribution of patients and costs across top five care-type specific clinical conditions, 2009/10
  High-Cost Users All Users
Condition # of Patients Total Cost ($M) Average Cost per Patient ($K) # of Patients Total Cost ($M) Average Cost perPatient ($K)
Acute In-Patient Care
   D&D circulatory system 46,039 1,060 23.03 102,802 1,379 13.42
   D&D Respiratory System 25,743 613 23.83 63,532 808 12.72
   D&D digestive system 27,708 556 20.08 89,260 834 9.35
   Trauma inj pois & tox eff drug 23,454 476 20.29 56,682 643 11.35
   D&D nervous system 18,276 458 25.05 36,777 566 15.40
   All categories 252,142 5,365 21.28 930,508 8,096 8.70
Day Surgery
   D&D circulatory system 15,109 65 4.30 50,314 162 3.22
   Mental diseases & disorders 9,013 11 1.18 70,504 76 1.07
   Examination & other health factors 7,997 11 1.32 150,417 84 0.56
   D&D digestive system 11,325 10 0.87 269,210 208 0.77
   D&D of the kidney, GU, M&F repro 8,274 8 0.91 163,959 139 0.85
   All categories 67,839 129 1.90 1,069,137 1,158 1.08
   D&D circulatory system 43,038 39 0.92 302,578 153 0.51
   D&D digestive system 38,418 33 0.86 447,351 192 0.43
   D&D respiratory system 31,011 30 0.96 273,565 126 0.46
   Oncological D&D 42,249 25 0.60 840,653 217 0.26
   D&D nervous system 25,963 19 0.75 230,786 93 0.40
   All categories 334,388 233 0.70 4,167,398 1,319 0.32
D&D=Diseases and Disorders


The table shows that all but one of the top five clinical conditions in In-Patient and Emergency were identical, though ranked differently. Furthermore, in all three care types, circulatory system conditions incurred the highest total HCU costs. Within Day Surgery, circulatory system conditions had a notably higher average cost per patient than any other condition, whether for HCUs or for all users.


This HCU profile highlights the preponderant characteristics among HCUs. HCUs are most costly and prevalent in the TC LHIN, possibly because TC is host to hospitals that provide more specialized, costly acute services. Males are more costly than females, but neither age distribution nor frequency of care types was found to explain this observation. Seniors predictably accounted for the majority of HCU patients and costs, but the average cost per patient decreased with age; with age, the increase in patient count was greater than the increase in total costs, suggesting a higher frequency of less costly visits at older ages. Of the different clinical conditions, circulatory system conditions incurred the most costs in In-Patient, Day Surgery and Emergency. In In-Patient and Emergency, the high cost for circulatory system conditions was due to volume of patients, not due to the cost per patient. In Day Surgery, however, both cost per patient and volume of patients contributed to the high costs, illustrating that the cost and cost drivers associated with a condition vary by care type. In In-Patient, 92% of circulatory system condition costs were from patients aged 45+, 58% of these costs were from males, and 23% were from patients in TC, reconfirming the role of demographics in driving prevalence of conditions. Further investigations concerning the types of treatments used in each demographic may give added insights into the differences observed between demographic categories.

The profile of high-cost users in Ontario presented in this paper is an original contribution to the wide body of published literature on HCUs in other jurisdictions. It confirms previously published findings that a relatively small proportion of patients consume the majority of healthcare resources, but also looks at characteristics that are specific to Ontario.

Moving forward, this profile should guide the development of policies and programs supporting Ontario's Action Plan for Health Care (Government of Ontario 2012). Furthermore, efforts to manage HCUs should address their complex profile through integrated, multidisciplinary healthcare delivery. The focus of the delivery, moreover, should be on appropriate care as opposed to simply more frequent or more costly care, as Stukel and colleagues (2012) and The Commonwealth Fund (2012) have emphasized. This profile should also help in providing coordinated healthcare services to HCUs by all related care providers in each LHIN. Further research should build upon the profile presented, investigating, for example, how HCUs transition through the system and how different interventions contribute to high costs. Currently, we are looking at the histories of HCUs and the progression of chronic conditions to identify precursors and interventions that may help identify patients at risk of becoming HCUs. Proper interventions and proactive care for such high-risk patients may improve health outcomes and ease fiscal pressures on the healthcare system.



Usagers qui coûtent cher aux services de santé en Ontario

Environ 1,5 % de la population ontarienne, qui correspond à 5 % des usagers qui génèrent le plus de coûts pour les services hospitaliers et les soins à domicile en Ontario, comptent pour 61 % des frais hospitaliers et de frais pour les soins à domicile. Des études semblables menées ailleurs montrent également qu'un nombre relativement petit de personnes utilisent une grande partie des ressources du système de santé. Une meilleure compréhension des usagers qui coûtent cher peut aider les planificateurs à améliorer la qualité des services et à réduire le fardeau sur les patients et sur le système de santé. Afin de faciliter cette compréhension, nous avons brossé un profil des usagers qui coûtent cher à l'aide de caractéristiques cliniques et démographiques. Ce profil donne des renseignements détaillés sur ces patients, en fonction du type de soins, de la géographie, de l'âge, du sexe et des principaux états cliniques.

About the Author(s)

Saad Rais, MSc, Methodologist, Health Analytics Branch, Ontario Ministry of Health and Long-Term Care, Toronto, ON

Amir Nazerian, MSc, Methodologist, Health Analytics Branch, Ontario Ministry of Health and Long-Term Care, Toronto, ON

Sten Ardal, MA, Director, Health Analytics Branch, Health System Information Management and Investment, Ontario Ministry of Health and Long-Term Care, Adjunct Faculty, School of Health Policy and Management, York University, Adjunct Faculty, Department of Health Policy Management and Evaluation, University of Toronto, Toronto, ON

Yuriy Chechulin, MPH, MD (Ukraine), Senior Methodologist, Health Analytics Branch, Ontario Ministry of Health and Long-Term Care, Toronto, ON

Namrata Bains, MSc, Senior Manager, Health Analytics Branch, Health System Information Management and Investment, Ontario Ministry of Health and Long-Term Care, Adjunct Lecturer, Department of Community Health and Epidemiology, Queen's University, Kingston, ON

Kamil Malikov, MBA, MD (Russia), Senior Manager, Health Analytics Branch, Health System Information Management and Investment, Ontario Ministry of Health and Long-Term Care, Toronto, ON

Correspondence may be directed to: Saad Rais, Methodologist, Methods and Modelling Unit, Health Analytics Branch, Health System Information Management & Investment Division, Ontario Ministry of Health and Long-Term Care, 1075 Bay Street 13th Floor, Toronto ON M5S 2B1; Tel: 416-212-2638; Fax: 416-326-6560; e-mail:


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