Human Resource Costs of Implementing a Tiered Team Care Model for Chronically Ill Patients According to Lean Management Principles
Bibliographic record
Abstract
Background/Aims: The published literature on managing chronic conditions includes mostly randomized controlled trials, but is silent on implementation in real-world practice. We documented the Palo Alto Medical Foundation’s (PAMF) human resource costs of developing and implementing a new chronic care model called Champion. Champion includes proactive outreach to patients with uncontrolled diabetes and hypertension, a redesigned primary care visit and health coaching. Champion aimed to improve patient and provider experience and outcomes, as well as cover costs, through expanding the role of nonlicensed personnel. Methods: One clinic with 38 physicians serving 29,000 adult patients implemented Champion. We used an “activity-based costing” approach to investigate the organizational process. Activity-based costing divides indirect human resource costs, which dominate health system transformation, into specific activities. We searched the Microsoft Outlook calendars of key informants to determine time spent on Champion activities. Activity cost was calculated as the sum of hours spent by employees multiplied by their actual wages. The time period analyzed covered development, rollout and stabilization (July 2012 through May 2014). Results: Implementation involved 162 employees –– 30 managers, 46 physicians, 54 staff and 32 consultants –– and cost $1,309,883 (17,102 hours). Activities followed lean management principles: management guidance meetings, rapid process improvement workshops, value stream mapping, and final performance review. The 32 consultants were the most expensive employee-type: $402,190, 6,343 hours. The 54 staff were the least expensive: $262,210, 6,183 hours. The most expensive activity was the 15 employees who worked full time on implementation (3 managers, 5 consultants, 4 physician piloteers, 3 health coaching program staff). The health coaching staff worked 5,328 hours and cost $210,348. Frontline provider trainings, which included all physicians and medical assistants, extended over 3 days (2,352 hours total) and cost $225,307. Conclusion: PAMF invested significant resources ($1,309,883) to Champion. The standard work developed is fundamental for future spread. PAMF may recover some of the implementation costs from additional revenues as physicians address unmet needs of chronic care patients. Champion’s long-term financial sustainability should be evaluated as PAMF moves further into the era of accountable care, in which value of services brings more reward than volume of services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".