High-cost, high-need patients: the impact of reported penicillin allergy.

OBJECTIVES: More than 90% of patients who report a penicillin allergy have the allergy disproved when tested. Unnecessary use of alternative (non-beta-lactam) antibiotics can result in more treatment failures and adverse reactions. We described the prevalence and impact of a reported penicillin allergy in high-cost, high-need (HCHN) patients. STUDY DESIGN: Retrospective cohort. METHODS: We identified HCHN patients in a care management program of an urban academic medical center (January 1, 2014, to December 31, 2016). We used multivariable logistic regression models to determine the association between a reported penicillin allergy and antibiotic use. We used multivariable Poisson regression models to determine the association between a reported penicillin allergy, with or without multiple drug intolerance syndrome (MDIS; ≥3 reported drug allergies), and healthcare resource utilization (HRU). RESULTS: Of 1870 HCHN patients, 383 (20%) reported penicillin allergy, 835 (45%) had MDIS, and 290 (16%) had both. HCHN patients reporting penicillin allergy had an increased odds of beta-lactam alternative antibiotic use (adjusted odds ratio, 3.84; 95% CI, 2.17-6.80). HRU was significantly higher for patients reporting a penicillin allergy alone (adjusted relative risk [aRR], 1.13; 95% CI, 1.03-1.25) and with concurrent MDIS (aRR, 1.20; 95% CI, 1.08-1.34). CONCLUSIONS: HCHN patients had a high burden of reported drug allergy. A reported penicillin allergy conferred a 4-fold increased odds of beta-lactam alternative antibiotic use. Reporting penicillin allergy, with and without MDIS, was associated with significantly more HRU. HCHN care management programs should consider systematic drug allergy evaluations to optimize antibiotic use in these fragile patients.

Optimizing the Use of Electronic Health Records to Identify High-Risk Psychosocial Determinants of Health.

BACKGROUND: Care coordination programs have traditionally focused on medically complex patients, identifying patients that qualify by analyzing formatted clinical data and claims data. However, not all clinically relevant data reside in claims and formatted data. Recently, there has been increasing interest in including patients with complex psychosocial determinants of health in care coordination programs. Psychosocial risk factors, including social determinants of health, mental health disorders, and substance abuse disorders, are less amenable to rapid and systematic data analyses, as these data are often not collected or stored as formatted data, and due to US Health Insurance Portability and Accountability Act (HIPAA) regulations are often not available as claims data. OBJECTIVE: The objective of our study was to develop a systematic approach using word recognition software to identifying psychosocial risk factors within any part of a patient's electronic health record (EHR). METHODS: We used QPID (Queriable Patient Inference Dossier), an ontology-driven word recognition software, to scan adult patients' EHRs to identify terms predicting a high-risk patient suitable to be followed in a care coordination program in Massachusetts, USA. Search terms identified high-risk conditions in patients known to be enrolled in a care coordination program, and were then tested against control patients. We calculated precision, recall, and balanced F-measure for the search terms. RESULTS: We identified 22 EHR-available search terms to define psychosocial high-risk status; the presence of 9 or more of these terms predicted that a patient would meet inclusion criteria for a care coordination program. Precision was .80, recall .98, and balanced F-measure .88 for the identified terms. For adult patients insured by Medicaid and enrolled in the program, a mean of 14 terms (interquartile range [IQR] 11-18) were present as identified by the search tool, ranging from 2 to 22 terms. For patients enrolled in the program but not insured by Medicaid, a mean of 6 terms (IQR 3-8) were present as identified by the search tool, ranging from 1 to 21. CONCLUSIONS: Selected informatics tools such as word recognition software can be leveraged to improve health care delivery, such as an EHR-based protocol that identifies psychosocially complex patients eligible for enrollment in a care coordination program.

Community health workers' support for cancer clinical trials: description and explanation.

Ethnic differences in participation in cancer clinical trials slow advances in medical knowledge that can reduce health care disparities. Community health workers (CHWs) are an increasingly important bridge between the health care system and underserved communities and could play an important role in increasing rates of clinical trial participation. We investigated community health workers' orientations to medical research and cancer clinical trials with a mixed methods design: two focus groups, 11 intensive interviews, and a structured survey of 76 CHW training workshop participants. CHWs demonstrated high levels of commitment to improving the health of community members but considerable distrust of researchers' motives, low levels of knowledge about cancer clinical trials, and frequent perceptions of bias in the health care system. Support for research is associated with more research experience, self-assessed knowledge, and Hispanic ethnicity, but with less seniority as a CHW. Neither actual knowledge of cancer clinical trials nor perceptions of bias in the health care system were related to degree of support for medical research. Community health workers perceive bias in the health care system but recognize the importance of medical research and are interested in learning more about cancer clinical trials. Research experience increases support for medical research; education increases perceptions of health care system bias.