Development, Testing, and Dissemination of a Public-Facing Tool to Help Consumers Find Higher-Quality Addiction Treatment.

OBJECTIVE: The authors aimed to describe the development and testing of quality measures included in a public-facing addiction treatment facility search engine. METHODS: An addiction treatment facility survey was created that queried providers in six U.S. states about whether they offered the services and used the processes identified by federal agencies and nonprofit organizations as signs of higher-quality addiction treatment. Four insurance claims-based quality measures were created to capture the percentage of a provider's patients with opioid use disorder receiving opioid use disorder medications, who filled prescriptions for such medication for at least 180 days, who received follow-up care after treatment for substance use disorder in inpatient or residential settings, or who had a substance use disorder-related hospitalization or emergency department visit. A patient experience-of-care survey captured patients' perceptions of the quality of the addiction treatment. The project was undertaken from November 2018 through July 2020. RESULTS: The authors tested the measures by using 1,245 facility surveys, 7,970 patients' experience-of-care surveys, and four claims-based measures submitted by 129, 136, 283, and 408 addiction treatment providers. Statistical testing demonstrated that the quality measures were reliable and valid. The quality measure scores varied among providers, capturing a wide performance range. A public website containing quality measures launched in July 2020 in the six states and has been accessed by thousands of consumers. CONCLUSIONS: This study developed valid, reliable, and useful addiction treatment quality measures. Dissemination of these measures may help consumers select among providers and help providers, policy makers, and payers improve quality.

Evaluation of Switching Patterns in FDA's Sentinel System: A New Tool to Assess Generic Drugs.

INTRODUCTION: Nearly 90% of drugs dispensed in the US are generic products. OBJECTIVE: The aim of this study was to develop and implement a tool for analyzing manufacturer-level drug utilization and switching patterns within the US Food and Drug Administration's Sentinel system. METHODS: A descriptive tool was designed to analyze data in the Sentinel common data model and was tested with two case studies-metoprolol extended release (ER) and lamotrigine ER-using claims data from four Sentinel data partners. We plotted initiators of each brand and generic product over time. For metoprolol ER, we evaluated rates of switching from generics around the time of manufacturing issues. For lamotrigine ER, we examined rates of switching back to the brand among those who switched from brand to generic. RESULTS: We identified 1,651,285 initiators of metoprolol ER products between July 2008 and September 2015. We observed a large decrease in monthly metoprolol ER initiators (from 25,465 in December 2008 to 13,128 in February 2009), corresponding to recalls by generic manufacturers. We observed simultaneous increases in utilization of the authorized generic and brand products. We identified 4266 initiators of lamotrigine ER with an epilepsy diagnosis between January 2012 and September 2015. Among those who switched from brand to generic, the cumulative incidence of switching back was close to 20% at 2 years. Switchback rates were higher for the first available generic products. CONCLUSIONS: This developed tool was able to elucidate novel utilization and switching patterns in two case studies. Such information can be used to support surveillance of generic drugs and biosimilars.

Distributed data networks: a blueprint for Big Data sharing and healthcare analytics.

This paper defines the attributes of distributed data networks and outlines the data and analytic infrastructure needed to build and maintain a successful network. We use examples from one successful implementation of a large-scale, multisite, healthcare-related distributed data network, the U.S. Food and Drug Administration-sponsored Sentinel Initiative. Analytic infrastructure-development concepts are discussed from the perspective of promoting six pillars of analytic infrastructure: consistency, reusability, flexibility, scalability, transparency, and reproducibility. This paper also introduces one use case for machine learning algorithm development to fully utilize and advance the portfolio of population health analytics, particularly those using multisite administrative data sources.

Hospitalizations for arthritis and other rheumatic conditions: data from the 1997 National Hospital Discharge Survey.

OBJECTIVE: To describe the impact of arthritis and other rheumatic conditions on hospitals by describing the magnitude and characteristics of these hospitalizations. METHODS: Data from the 1997 National Hospital Discharge Survey were used to examine this impact. Arthritis was defined using International Classification of Diseases, 9th Revision, Clinical Modification, codes specified by the National Arthritis Data Workgroup. Arthritis-related hospitalizations were analyzed by principal diagnosis of arthritis and by any-listed arthritis diagnosis. RESULTS: In 1997, there were an estimated 744,000 hospitalizations with a principal arthritis diagnosis (3% of hospitalizations). Compared with nonarthritis hospitalizations, persons hospitalized with a principal arthritis diagnosis were older, had fewer comorbidities, had shorter hospital stays, were more likely to undergo a procedure, and were more likely to be discharged to short- and long-term care facilities. The most common diagnoses and procedures related to osteoarthritis. This profile was consistent with a healthier-than-average hospital population electively admitted for specific procedures and subsequent rehabilitation. There were an estimated 2.5 million hospitalizations with an any-listed arthritis diagnosis (>9% of hospitalizations). Persons hospitalized with an any-listed arthritis diagnosis were older, had more comorbidities, and had longer hospital stays than those with principal arthritis or nonarthritis hospitalizations. This profile was consistent with a sicker-than-average hospital population nonelectively admitted for reasons other than their arthritis, especially cardiovascular disease. CONCLUSION: Arthritis has a sizable impact on the hospital care system. As our population ages, this impact, in both human and economic terms, is likely to increase.