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Record W2188680388 · doi:10.18553/jmcp.2003.9.6.523

Financial Risk Relationships and Adoption of Management Strategies in Physician Groups for Self-Administered Injectable Drugs

2003· article· en· W2188680388 on OpenAlexaff
Jonathan D. Agnew, Marilyn Stebbins, David E Hickman, Helene Levins Lipton

Bibliographic record

VenueJournal of Managed Care Pharmacy · 2003
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British Columbia
FundersAcademyHealthRobert Wood Johnson Foundation
KeywordsCapitationMedicinePharmacyFamily medicineHealth maintenanceRisk managementFinancial riskFinanceHealth careBusiness

Abstract

fetched live from OpenAlex

OBJECTIVE: To consider the extent, nature, and range of risk arrangements between physician groups and health maintenance organizations (HMOs) for self-administered injectable (SAI) drugs; to examine types and frequencies of SAI drug-use management strategies adopted by physician groups; and to explore the relationship between locus and level of financial risk for SAIs and physician group strategy adoption. METHODS: We used a multiple case-study design to select physician groups and their health maintenance organization (HMO) contractual partners in 4 markets in the United States (Northwest, Northeast, Midwest, Southwest). Physician groups in these markets were chosen based on size (e50 physicians) and experience with drug risk (e1 year). Physician groups were asked to identify their 3 major HMO contractual partners in each market. Telephone interviews were conducted from January 2000 to June 2001, with the resulting purposive sample of 37 individuals representing 20 physician groups. RESULTS: We found that the level and locus of SAI financial risk were related to the adoption of management strategies. Physician groups with higher financial risk for SAIs adopted more strategies than lower-risk groups. Groups with SAI financial risk in the medical services capitation (MSC) adopted 9.2 strategies per group. In contrast, groups with SAI financial risk in the pharmacy-risk budget (PRB) averaged 1.5 strategies per group. Groups with SAI financial risk in both the MSC and PRB fell in-between, averaging 4.5 strategies per group. The most frequently adopted strategy was designing evidenced-based therapeutic guidelines, i.e., protocols based on evidence from the peer-reviewed literature used to guide physicians in the treatment of typically chronic conditions (9 groups, 45% of sample). The second most common strategy involved adapting the existing utilization management system to process SAIs (7 groups, 35%) and the establishment of office procedures for internal authorization (5 groups, 25%). The least frequently used strategies were determining amount paid to out-of-group physician providers (1 group, 5%) and hiring personnel (e.g., pharmacists) in claims or utilization management departments to implement and manage SAI programs (1 group, 5%). We also identified potential factors that increased the likelihood of strategy adoption and that could slow the rate of SAI cost increases. CONCLUSION: Our findings suggest that adoption of SAI drug-use management strategies may be more likely to occur when there is a minimum level of risk for SAI drug costs. Likewise, both the adoption of strategies and the opportunity to slow the rate of SAI cost increases may be more likely to occur when 3 additional factors are present: a contractual environment conducive to controlling SAI drug costs, the ability to implement SAI drug-use management strategies, and power in negotiations with drug manufacturers to reduce SAI prices. A sustainable and affordable SAI financial risk management program maximizing these factors while minimizing the financial burden for patients will require collaboration among all stakeholders, payers, providers, drug manufacturers, and patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.323
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2003
Admission routes1
Has abstractyes

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