Connecting Patients to Prescription Assistance Programs: Effects on Emergency Department and Hospital Utilization
Bibliographic record
Abstract
BACKGROUND: Manufacturer prescription assistance programs (PAPs) have been developed to provide medications at little or no cost to eligible patients. There are over 200 PAPs available from pharmaceutical companies, and each may have different eligibility requirements and assistance guidelines. A formalized community-based patient prescription coordinator can help patients navigate these programs by reviewing an applicant's financial information and medication requirements to identify which PAPs are most appropriate. Little is known, however, about whether providing such guidance is associated with a reduction in acute care utilization. OBJECTIVE: To evaluate changes in emergency department and hospital utilization among patients who received care coordination and financial assistance with prescribed medications. METHODS: This single-cohort interrupted time-series study included participants in eastern Washington state who enrolled in the Spokane Prescription Assistance Network (SPAN) program between March 1, 2009, and August 31, 2012. Referrals to the SPAN patient prescription coordinator were made by a social service agency or medical provider for patients who may have difficulty paying for prescribed medications. Initial patient contact occurred while the patient was still being treated in a clinic or hospital or through a direct visit to the coordinator's community-based office. Participants were contacted 6 months after the initial appointment and then annually thereafter to review current medications and health status. A review of electronic health records provided information on hospitalizations and emergency department visits in the 12 months before and after program entry. RESULTS: Among SPAN participants (n = 310), emergency department and hospital encounters declined from 0.38 per participant in the year before enrollment to 0.20 encounters in the year following program entry. A repeated-measures mixed-effects model indicated SPAN participation was associated with a 51% decline in the rate of emergency department and hospital utilization (incidence rate ratio [IRR] = 0.49; 95% CI = 0.31-0.77; P = 0.002). Observed effects differed by prescription class. Factor interactions revealed significant reductions in utilization for participants with prescribed pulmonary medications (IRR = 0.58; 95% CI = 0.37-0.92; P = 0.019). Assistance with mental health (psychotropic) medications was associated with increased incidence of utilization (IRR = 2.07; 95% CI = 1.32-3.24; P = 0.001). At the time of SPAN enrollment, 60% of participants had prescriptions for psychotropic medications. CONCLUSIONS: A formalized patient prescription coordinator can help patients access prescribed medications at low cost and remain compliant with treatment plans. In a study of a coordination pilot program, reductions in hospital admissions and emergency department visits were observed following program participation. DISCLOSURES: This study was not supported by any outside funding. The authors declare no conflicts of interest. Study design was created by Burley, McPherson, and Daratha. Burley Daratha, Selinger, and Armstrong collected the data, with interpretation performed by Burley, Daratha, and Tuttle, assisted by McPherson. The manuscript was written by Burley, Daratha, and Selinger, with assistance from White, and revised by Burley, White, and Selinger, with assistance from Daratha and Tuttle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".