Evaluating the cost of NP-led vs. GP-led primary care in British Columbia.

In 2020, British Columbia (BC) opened four pilot Nurse Practitioner Primary Care Clinics (NP-PCCs) to improve primary care access. The aim of this economic evaluation is to compare the average cost of care provided by Nurse Practitioners (NPs) working in BC's NP-PCCs to what it would have cost the government to have physicians provide equivalent care. Comparisons were made to both the Fee-For-Service (FFS) model and BC's new Longitudinal Family Physician (LFP) model. The analyses relied on administrative data, mostly from the Medical Services Plan (MSP) and Chronic Disease Registry (CDR) via BC's Health Data Platform. Results show the cost of NPs providing care in the NP-PCCs is slightly lower than what it would cost to provide similar care in medical clinics staffed by physicians paid through the LFP model. This suggests that the NP-PCC model is an efficient approach to increase accessibility to primary care services in BC and should be considered for expansion across the province.

Population segments as a tool for health care performance reporting: an exploratory study in the Canadian province of British Columbia.

BACKGROUND: Primary care serves all age groups and individuals with health states ranging from those with no chronic conditions to those who are medically complex, or frail and approaching the end of life. For information to be actionable and guide planning, there must be some population disaggregation based on differences in expected needs for care. Promising approaches to segmentation in primary care reflect both the breadth and severity of health states, the types and amounts of health care utilization that are expected, and the roles of the primary care provider. The purpose of this study was to assess population segmentation as a tool to create distinct patient groups for use in primary care performance reporting. METHODS: This cross-sectional study used administrative data (patient characteristics, physician and hospital billings, prescription medicines data, emergency department visits) to classify the population of British Columbia (BC), Canada into one of four population segments: low need, multiple morbidities, medically complex, and frail. Each segment was further classified using socioeconomic status (SES) as a proxy for patient vulnerability. Regression analyses were used to examine predictors of health care use, costs and selected measures of primary care attributes (access, continuity, coordination) by segment. RESULTS: Average annual health care costs increased from the low need ($ 1460) to frail segment ($10,798). Differences in primary care cost by segment only emerged when attributes of primary care were included in regression models: accessing primary care outside business hours and discontinuous primary care (≥5 different GP's in a given year) were associated with higher health care costs across all segments and higher continuity of care was associated with lower costs in the frail segment (cost ratio = 0.61). Additionally, low SES was associated with higher costs across all segments, but the difference was largest in the medically complex group (cost ratio = 1.11). CONCLUSIONS: Population segments based on expected need for care can support primary care measurement and reporting by identifying nuances which may be lost when all patients are grouped together. Our findings demonstrate that variables such as SES and use of regression analyses can further enhance the usefulness of segments for performance measurement and reporting.

Predicting the Cost of Health Care Services: A Comparison of Case-mix Systems and Comorbidity Indices That Use Administrative Data.

BACKGROUND: Case-mix systems and comorbidity indices aggregate clinical information about patients over time and are used to characterize need for health care services. These tools were validated for their original purpose, but those purposes are varied, and they have not been compared directly in the context of predicting costs of health care services. OBJECTIVE: To compare predictions of next-year health care service costs across 4 tools, including: the Johns Hopkins Adjusted Clinical Groups (ACG), the Elixhauser Comorbidity Index, Charlson-Deyo Comorbidity Index, and the Canadian Institute for Health Information (CIHI) population grouper. METHODS: British Columbia administrative data from fiscal years 2012-2013 were used to generate case-mix variables and the comorbidity indices. Outcome variables include next-year (2013-2014) total, physician, acute care, and pharmaceutical costs, Outcomes were modeled using 2-part models. Performance was compared using adjusted R, root mean squared error, and mean absolute error using the predicted and the actual next-year cost. RESULTS: Models including the CIHI grouper (239 conditions) and ACG system had similar performance in most cost categories and slightly better fit than Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). Adding a dummy variable for nonusers in the models for CCI and ECI increased R values slightly. CONCLUSIONS: All these systems have empirical support for use in predicting health care costs, despite in some cases being developed for other purposes. No system is particularly effective at predicting next-year acute care cost, likely because acute events are often by definition unexpected. The freely available ECI and CCI comorbidity indices implemented using the highest-performing methods developed here may be a good choice in many circumstances.

Designation, diligence and drift: understanding laboratory expenditure increases in British Columbia, 1996/97 to 2005/06.

BACKGROUND: Laboratory testing is one of the fastest growing areas of health services spending in Canada. We examine the extent to which increases in laboratory expenditures might be explained by testing that is consistent with guidelines for the management of chronic conditions, by analyzing fee-for-service physician payment data in British Columbia from 1996/97 and 2005/06. METHOD: We used direct standardization to quantify the effect on laboratory expenditures from changes in: fee levels; population growth; population aging; treatment prevalence; expenditure on recommended tests for those conditions; and expenditure on other tests. The chronic conditions selected were those with guidelines containing laboratory recommendations developed by the BC Guidelines and Protocol Advisory Committee: diabetes, hypertension, congestive heart failure, renal failure, liver disease, rheumatoid arthritis, osteoarthritis and dementia. RESULT: Laboratory service expenditures increased by $98 million in 2005/06 compared to 1996/97, or 3.6% per year after controlling for population growth and aging. Testing consistent with guideline-recommended care for chronic conditions explained one-third (1.2% per year) of this growth. Changes in treatment prevalence were just as important, contributing 1.5% per year. Hypertension was the most common condition, but renal failure and dementia showed the largest changes in prevalence over time. Changes in other laboratory expenditure including for those without chronic conditions accounted for the remaining 0.9% growth per year. CONCLUSION: Increases in treatment prevalence were the largest driver of laboratory cost increases between 1996/97 and 2005/06. There are several possible contributors to increasing treatment prevalence, all of which can be expected to continue to put pressure on health care expenditures.