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The American Journal of Accountable Care September 2017
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Population Health in Primary Care: Cost, Quality, and Experience Impact
Tracy L. Johnson, PhD, MA; Mary van der Heijde, FSA, MAAA; Stoddard Davenport, BA; Carlos Irwin Oronce, MD, MPH; Daniel Brewer, BA; Rachel Everhart, PhD, MS; Patricia Gabow, MD; Simon J. Hambidge, MD; Adam Atherly, PhD; and Holly Batal, MD, MBA
To Err is Avoidable: The Automation of Knowledge and the Clinical Decision Support Revolution
Ezra Mehlman, MBA
Building Partnerships Between a College of Pharmacy and ACOs: Development of the ACORN SEED
Stephanie A. Gernant, PharmD, MS; Genevieve M. Hale, PharmD, BCPS; Tina Joseph, PharmD, BCACP; Renee S. Jones, PharmD, CPh; Sarah Alameddine, PharmD; Stacey Maravent, PharmD; Sara M. Eltaki, PharmD, B
Postacute Care Partnerships and Patient Progression
Penny Gilbert, MSN, MBA, RN, NE-BC, CPHQ; Nancy J. Maggard, MSN, RN, NE-BC; Cheryl Talbert, MSW, LCSW; and Donna Vela, MSN, RN, NE-BC, CPM
Patient Participation in Care Management: Are They Aware?
Jodi Summers Holtrop, PhD, MCHES; Qiaoling Chen, MS; Gretchen Piatt, PhD, MPH; Zhehui Luo, PhD; Jean Malouin, MD; Ruth Clark, RN, BSN, MPA; and Cecilia Sauter, MS, RD, CDE, FAADE

Population Health in Primary Care: Cost, Quality, and Experience Impact

Tracy L. Johnson, PhD, MA; Mary van der Heijde, FSA, MAAA; Stoddard Davenport, BA; Carlos Irwin Oronce, MD, MPH; Daniel Brewer, BA; Rachel Everhart, PhD, MS; Patricia Gabow, MD; Simon J. Hambidge, MD; Adam Atherly, PhD; and Holly Batal, MD, MBA
An evaluation of the use of predictive modeling for primary care resource allocation demonstrated reduced spending and improved quality and patient experience for publicly insured adults.
To establish the historical cost benchmark, against which the intervention period costs can be compared, we quantified payer spending for the baseline population, adjusted by a medical inflation factor to account for secular cost trends. This adjustment is necessary to enable direct comparison of costs during the baseline and intervention periods. The Medicaid trend factor was 3.7%, consistent with the trend developed during Colorado state Medicaid agency annual capitation rate setting for DH’s health plan. The 2.3% Medicare trend factor was derived from the National Health Expenditures Projections report. Baseline costs for the Medicaid expansion population were estimated according to insurance rate-setting methods for a new population (eAppendix). We stratified cost outcomes by payer to account for payer-specific reimbursement levels.


Population Characteristics

Table 1 compares baseline and intervention populations according to tier, gender, and age. Member months are post adjustment, reflecting the above-described tier-mix adjustment.

Program Reach Results

As intended, a greater proportion of higher-tier patients were reached by DH interventions than were lower-tier patients. More than half of adult tier 4 patients were reached by either primary care–based enhanced-care team members or visited a high-intensity clinic during the ramp-up (51.0%) and mature program periods (56.4%). Program reach was lower for lower-tier patients: for the ramp-up and mature program periods, respectively, the percentages were 24.6% and 25.6% for tier 3, 15.7% and 12.9% for tier 2, and 1.5% and 1.4% for tier 1. 

Quality and Patient Experience Results

The DH composite quality metric measured 77% at baseline. It increased to 82% during the intervention ramp-up, declined to 72% in 2014, and rebounded to 81% by mid-2015. Four of 6 patient experience metrics improved or remained constant during intervention ramp-up, and improved relative to baseline in 2014 and 2015 (Table 2).

Total Cost of Care Results

Reductions in total costs were observed for the majority of periods and payer populations, except for Medicaid managed care during the ramp-up period and Medicare managed care in the mature program period (Table 3). DH achieved a cumulative $10.9 million reduction in the total cost of care for its Medicaid and Medicare FFS populations across the 2 program implementation periods. These savings largely accrued to the state and federal governments. The largest share of this FFS cost avoidance ($8.2 million) was attributable to Medicare FFS. An additional $5.0 million reduction in the total cost of care was estimated for DH’s capitated managed care during this same timeframe. The annualized personnel cost of the adult program totaled $1.8 million. 

During both the ramp-up and the mature program periods, reductions in the total cost of care for tier 4 adults were observed across all managed care and FFS payers, ranging from –$40.88 PMPM to –$737.20 PMPM. Reduced tier 4 spending was concentrated in inpatient reductions. For lower-risk populations (tiers 1-3), changes in claims costs varied by payer and by year. In aggregate, the analysis shows small cost reductions for tiers 1 and 3 (totaling $1 million across years and payers). 

Tier 2 costs consistently exceeded the benchmark, totaling $4.8 million for Medicaid and $2.6 million for Medicare. For both Medicare and Medicaid, this higher-than-expected tier 2 spending offset, but did not eliminate, overall cost reductions. 

We conducted a subanalysis on DH’s Medicaid and Medicare managed care populations to further explore the changes in tier 4 inpatient costs. We chose these populations due to the more complete data capture. Table 4 reveals that reductions in inpatient costs are not consistently observed in lower-risk tiers, suggesting that tier 4 inpatient cost reductions are not due to a broader secular trend affecting all DH populations. Consistent with prior research, Table 4 reveals that tier 4 inpatient savings are offset by increased spending in other areas, possibly reflecting program-driven referrals that resulted in additional service provisions.12,24,20


This study provides an important contribution to the literature as one of the first cost analyses of a major CMMI/HCIA initiative. Compared with an inflation-adjusted baseline period, net reductions in PMPM spending were observed in 5 out of 6 payers during 2 subsequent intervention periods. Patient satisfaction measures also improved during this same timeframe, suggesting that cost reductions were not achieved at the expense of patient experience. Overall quality improved in 2013 and 2015 over baseline performance. Reduced performance during 2014 may be partially explained by pent-up demand during the Medicaid expansion. 

As hypothesized, reduced inpatient spending among high-risk adults drove the overall reduction in the total cost of care. High-risk (tier 4) adults with multiple chronic conditions and repeated hospitalizations were targeted for multiple interventions, and more than half were reached. The program reached fewer lower-tier patients and concomitant changes in associated payer spending were less pronounced, and even increased in some cases. This latter finding suggests that team-based care for lower-risk patients may need to be more targeted or evaluated over a longer time horizon. 

Although we observed year-to-year fluctuations in performance, average cost reductions for Medicare exceeded Medicaid performance and FFS reductions were generally larger than those for managed care members. These results are consistent with literature that finds that short-term return on investment requires carefully targeting patients at risk of hospitalization, reducing Medicare inpatient costs may be easier than for Medicaid inpatient costs, and opportunities may be greater among unmanaged FFS populations. Differences in cost-avoidance by payer and by year underscore the importance of all-payer approaches to overall financial sustainability. 

Gross cost reductions of nearly $16 million over the entire 26-month intervention period are substantially larger than the approximately $3.9 million in staffing expenses, supporting the self-sustaining potential of risk-stratified primary care delivery models. However, neither FFS nor experience-based capitation permits ongoing reinvestment of savings into program costs. Cost reductions accrue to at least 3 separate payers: the federal and state government for FFS Medicare and Medicaid patients, and the DH health plan for its managed care members. Since DH owns and operates its own health plan—an unusual arrangement among safety net institutions—there is a direct means to “capture” a portion of the payer savings. Combined Medicaid and Medicare managed care cost avoidance was estimated at $2.3 million (annualized) compared with the $1.8 million in annual program costs. However, because capitation rates are based on historical claims costs, funds for program reinvestment will decline over time as capitation payments are rebased to reflect lower levels of medical expenditures. 

Thus, current payment models—even capitated managed care—do not align incentives. Although both Medicare and Medicaid have implemented new FFS care coordination, care transitions, and integrated care reimbursement opportunities, requirements are often highly prescriptive, process-oriented, and not consistent across payers. As a result, staffing models and clinical work flows that meet Medicare rules do not necessarily satisfy those of Medicaid, and vice versa. However, multipayer approaches are necessary to ensure sufficient funds and to smooth out year-to-year variations, especially for high-volume Medicaid providers. This underlines the need to accelerate implementation of advance payment models that better align financial incentives. 

Recognizing that current payment models do not adequately incentivize population health approaches, CMS has set a goal to have 90% of Medicare non-FFS arrangements by 2018.39 This evaluation offers both programmatic and financing insights relevant to alternative payment model development, such as those being developed under the Medicare Access and CHIP Replacement Act. 


First, although we applied a tier-mix adjustment and stratified by payer to ensure equivalence between the baseline and intervention populations, some differences in population characteristics remain that could partially account for the findings.  Second, incomplete data capture is a concern. Although we estimated service use at non-DH facilities among FFS populations, we did not attempt to estimate cross-sector impacts to the criminal justice or social service systems. Third, there is no widely accepted best practice for trend assumptions to establish the cost benchmark. Because DH accounts for approximately 57% of the Denver Medicaid primary care market, we cannot use actual trend. We therefore selected trend assumptions that were based either on state rate-setting practices (3.7% for Medicaid) or national health spending trends (2.3% for Medicare). These trends are equivalent or lower than a recent CMS actuaries’ analysis that found that US health spending growth was “historically low” between 2009 and 2013, averaging 3.7%.3 Our trends are also lower than a touted Oregon Medicaid program analysis, assuming a 5.4% trend on a 2011 baseline.31,40 Although our approach does not exclude the possibility that savings result from a broader, secular phenomenon of lower inpatient spending, reductions were concentrated in tier 4 where program reach was greatest. Generalized inpatient reductions across all tiers were not observed. Finally, the program was implemented at a single institution; findings may not be generalizable.


Recent leveling-off of costs is often attributed to the aggregate effect of readmission reduction programs nationally, most of which have been launched without formal research designs. Although at-scale implementation complicates efforts to identify unexposed, concurrent comparison groups, from an institutional perspective, improvements over past performance are relevant even while the broader sector also improves.34 This analysis demonstrates that a large, multifaceted program may be evaluated by benchmarking against historical costs, using well-accepted actuarial methods to address regression to the mean.41 We conclude that risk-stratified, enhanced primary care delivery models hold Triple Aim promise, assuming supportive payment models.


The authors would like to acknowledge the project team responsible for designing and implementing the intervention, including the core management team, clinical teams, information technology team, and evaluation team, as well as Ambulatory Care Services and Executive Leadership (past and present).

This study was conducted for quality improvement/quality assurance purposes. Colorado Multiple Institutional Review Board (COMIRB) determined this project to be “not human subjects” research. Results are not generalizable.

Author Affiliations: Denver Health and Hospital Authority (TLJ, CIO, DB, RE, PG, SJH, HB), Denver, CO; Milliman (MvdH, SD), Denver, CO; Colorado School of Public Health (AA), Denver, CO.

Source of Funding: The intervention described herein was supported by Grant Number 1C1CMS331064 from the Department of Health and Human Services, Centers for Medicare & Medicaid Services. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the US Department of Health and Human Services or any of its agencies. Findings might or might not be consistent with or confirmed by the findings of the independent evaluation contractor. 

Author Disclosures: Several of the authors are or were employed by Denver Health and Hospital Authority (TLJ, CIO, DB, RE, PG, SJH, HB). Dr Johnson was co-principal investigator, Director of Evaluation, and reports that earlier versions of this analysis were presented at 2 academic conferences. Dr Hambidge has presented data from this project at 4 national academic and policy conferences. The remaining authors report no other relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (TLJ, PG, SJH, AA, HB); acquisition of data (TLJ, DB, RE, SJH, AA); analysis and interpretation of data (TLJ, MvdH, SD, CIO, DB, RE, PG, SJH, AA, HB); drafting of the manuscript (TLJ, CIO, PG, AA); critical revision of the manuscript for important intellectual content (TLJ, MvdH, SD, CIO, RE, PG, SJH, AA, HB); statistical analysis (MvdH, SD, RE); provision of study materials or patients (SJH); obtaining funding (TLJ, PG); administrative, technical, or logistic support (TLJ, DB, RE, SJH, HB); and supervision (TLJ, RE, SJH). 

Send Correspondence to: Tracy L. Johnson, PhD, MA, Denver Health, Ambulatory Care Services, 777 Bannock St, MC 6551, Denver, CO 80204. E-mail:

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