Physician behavior and low-value care

Inappropriate prescribing and overprescribing are examples of low- or no-value care that result in high costs with little to no clinical benefit and patient harms. Suboptimal prescribing is the result of suboptimal physician decision-making. Physician behavior is influenced by intrinsic and extrinsic factors, such as reimbursement models, patient demand, diagnostic uncertainty, and poor numeracy. When faced with numerous diagnostic and treatment decisions per day, physicians rely on mental shortcut, or "heuristics", that unconsciously alter their perception of the risks and benefits of a treatment. While overtreatment or inappropriate prescribing occurs in many conditions, this dissertation concentrates on opioids, COVID-19 treatments, and benzodiazepines. The three aims of this dissertation include: (1) the downstream harms of opioid overprescribing during COVID-19;(2) the impact of setting on physician behavior in treating COVID-19 outpatients and associated outcomes;and (3) the effectiveness of a behavioral economic intervention on inappropriate benzodiazepine prescribing. We primarily address these aims using LA County Department of the Medical Examiner-Coroner autopsy reports, California Controlled Substance Utilization Review and Evaluation System data, and claims data (Optum's de-identified Clinformatics Data Mart Database (2007-2020)). We identify community- and decedent-level characteristics associated with opioid-related deaths following the implementation of stay-at-home orders in Los Angeles County. We estimate if the likelihood of initial provider interventions for COVID-19, including inappropriate prescribing, differs by appointment setting (i.e., urgent care center versus dedicated telehealth company) and if inappropriate prescribing for COVID-19 is associated with adverse outcomes (i.e., hospitalizations and mortality). Lastly, we measure the effect of a behavioral economic intervention in reducing benzodiazepine prescribing in a secondary analysis of a randomized controlled trial. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

A Cost-Utility Analysis of Remote Pulse-Oximetry Monitoring of Patients With COVID-19.

OBJECTIVES: Since 2020, COVID-19 has infected tens of millions and caused hundreds of thousands of fatalities in the United States. Infection waves lead to increased emergency department utilization and critical care admission for patients with respiratory distress. Although many individuals develop symptoms necessitating a ventilator, some patients with COVID-19 can remain at home to mitigate hospital overcrowding. Remote pulse-oximetry (pulse-ox) monitoring of moderately ill patients with COVID-19 can be used to monitor symptom escalation and trigger hospital visits, as needed. METHODS: We analyzed the cost-utility of remote pulse-ox monitoring using a Markov model with a 3-week time horizon and daily cycles from a US health sector perspective. Costs (US dollar 2020) and outcomes were derived from the University Hospitals' real-world evidence and published literature. Costs and quality-adjusted life-years (QALYs) were used to determine the incremental cost-effectiveness ratio at a cost-effectiveness threshold of $100 000 per QALY. We assessed model uncertainty using univariate and probabilistic sensitivity analyses. RESULTS: Model results demonstrated that remote monitoring dominates current standard care, by reducing costs ($11 472 saved) and improving outcomes (0.013 QALYs gained). There were 87% fewer hospitalizations and 77% fewer deaths among patients with access to remote pulse-ox monitoring. The incremental cost-effectiveness ratio was not sensitive to uncertainty ranges in the model. CONCLUSIONS: Patient with COVID-19 remote pulse-ox monitoring increases the specificity of those requiring follow-up care for escalating symptoms. We recommend remote monitoring adoption across health systems to economically manage COVID-19 volume surges, maintain patients' comfort, reduce community infection spread, and carefully monitor needs of multiple individuals from one location by trained experts.

Opioid-related deaths before and after COVID-19 stay-at-home orders in Los Angeles County.

BACKGROUND: Opioid-related morbidity and mortality has increased during the COVID-19 pandemic, yet specific information about the communities most affected remains unknown. Our objective is to evaluate decedent-level associations with an opioid-related death following the implementation of stay-at-home orders in Los Angeles County. METHODS: This retrospective cohort study used data from the L.A. County Medical Examiner-Coroner to identify opioid-related deaths in 2019 and 2020. We used logistic regression to analyze the change in opioid-related deaths following a 30-day washout period after the start of stay-at-home orders. Independent variables included decedent age, gender, race and ethnicity, heroin or fentanyl present at the time of death, census tract-level education, and a scheduled drug prescription in the year before death. RESULTS: Opioid-related deaths in L.A. County are most common in census tracts where a small percentage of the population has a Bachelor's degree. Following stay-at-home orders, Non-Hispanic Caucasian individuals had significantly more opioid-related deaths than Hispanic individuals (risk ratio (RR): 1.82 [95 % CI, 1.10-3.02]; P < 0.05) after adjusting for age, gender, and heroin or fentanyl use. Racial and ethnic differences in mortality were not explained by census tract-level education or recent scheduled drug prescriptions. DISCUSSION: There has been an alarming rise in opioid-related deaths in L.A. County during 2020. The increase in opioid-related overdose deaths following the onset of COVID-19 and related policies occurred most often among Non-Hispanic Caucasian individuals. Further research on this trend's underlying cause is needed to inform policy recommendations during these simultaneous public health crises.