Barriers to Hospital Electronic Public Health Reporting and Implications for the COVID-19 Pandemic

A Jay  Holmgren; Nate  Apathy; Julia  Adler-Milstein

Article | Journal of the American Medical Informatics Association | Forthcoming

Barriers to Hospital Electronic Public Health Reporting and Implications for the COVID-19 Pandemic

by A Jay Holmgren, Nate Apathy and Julia Adler-Milstein

Abstract

We sought to identify barriers to hospital reporting of electronic surveillance data to local, state, and federal public health agencies and the impact on areas projected to be overwhelmed by the COVID-19 pandemic. Using 2018 American Hospital Association data, we identified barriers to surveillance data reporting, and combined this with data on the projected impact of the COVID-19 pandemic on hospital capacity at the hospital referral region (HRR) level. Our results find the most common barrier was public health agencies lacked the capacity to electronically receive data, with 41.2% of all hospitals reporting it. We also identified 31 HRRs in the top quartile of projected bed capacity needed for COVID-19 patients in which over half of hospitals in the area reported that the relevant public health agency was unable to receive electronic data. Public health agencies’ inability to receive electronic data is the most prominent hospital-reported barrier to effective syndromic surveillance. This reflects the policy commitment of investing in information technology for hospitals without a concomitant investment in IT infrastructure for state and local public health agencies.

Keywords: electronic health records; public health; syndromic surveillance; pandemic; COVID-19; Health Pandemics;

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Citation:

Holmgren, A Jay, Nate Apathy, and Julia Adler-Milstein. "Barriers to Hospital Electronic Public Health Reporting and Implications for the COVID-19 Pandemic." Journal of the American Medical Informatics Association (forthcoming). (Pre-published online June 1, 2020.)

Prescription drug monitoring programs (PDMPs) have become a widely embraced policy to address the US opioid crisis. Despite mixed scientific evidence on their effectiveness at improving health and reducing overdose deaths, 49 states and Washington, DC have adopted PDMPs, and they have received strong bipartisan legislative support. This article explores the history of PDMPs, tracking their evolution from paper-based administrative databases in the early 1900s to modern-day electronic systems that intervene at the point of care. We focus on two questions: how did PDMPs become so widely adopted in the United States, and how did they gain popularity as an intervention in the contemporary opioid crisis? Through this historical approach, we evaluate what PDMPs reflect about national drug policy and broader cultural understandings of substance use disorder in the United States today.

Article | JAMA Network Open | June 2020

Evaluation of Prescription Drug Monitoring Program Integration with Hospital Electronic Health Records by US County-Level Opioid Prescribing Rates

A Jay Holmgren and Nate Apathy

Prescription drug monitoring programs (PDMPs) have become a widely embraced policy solution to the opioid epidemic in the US. PDMPs offer prescribers a comprehensive view of patients’ controlled substance prescription history and can be used to monitor and reduce inappropriate opioid prescribing. However, poor usability and lack of integration with electronic health records (EHRs) have limited their effectiveness. Furthermore, without PDMP integration in EHRs, prescribers are forced to manage multiple disconnected software systems that interrupt clinical workflow, which may exacerbate technology-driven physician burnout and result in prescribers neglecting to check the PDMP before writing opioid prescriptions. Efforts to integrate PDMPs with EHRs may be associated with reduced prescriber burden and improved PDMP effectiveness; however, the extent to which this adoption has occurred remains unknown on a national scale in the US. Extant literature has focused on usability of PDMP systems rather than adoption and integration. PDMP integration rates are particularly important among hospitals, which are major sources of ambulatory care and thus potential opioid prescribing. Furthermore, hospitals in areas with high opioid prescribing rates may gain the most from PDMP integration efforts because physicians are more likely to provide treatment to patients with current or past opioid prescriptions in these regions. In this study, we assessed the level of EHR and PDMP integration in hospitals, comparing hospitals located in US counties with vs without high opioid prescribing rates.

Keywords: prescription drug monitoring programs; PDMPs; electronic health records; Hospitals; Health Care and Treatment; Technology; Integration; Performance Evaluation;

Citation:

Holmgren, A Jay, and Nate Apathy. "Evaluation of Prescription Drug Monitoring Program Integration with Hospital Electronic Health Records by US County-Level Opioid Prescribing Rates." JAMA Network Open 3, no. 6 (June 2020). View Details

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Mobile health applications (“apps”) have rapidly proliferated, yet their ability to improve outcomes for patients remains unclear. A validated tool that addresses apps’ potentially important dimensions has not been available to patients and clinicians. The objective of this study was to develop and preliminarily assess a usable, valid, and open-source rating tool to objectively measure the risks and benefits of health apps. We accomplished this by using a Delphi process, where we constructed an app rating tool called THESIS that could promote informed app selection. We used a systematic process to select chronic disease apps with ≥4 stars and <4-stars and then rated them with THESIS to examine the tool’s interrater reliability and internal consistency. We rated 211 apps, finding they performed fair overall (3.02 out of 5 [95% CI, 2.96–3.09]), but especially poorly for privacy/security (2.21 out of 5 [95% CI, 2.11–2.32]), interoperability (1.75 [95% CI, 1.59–1.91]), and availability in multiple languages (1.43 out of 5 [95% CI, 1.30–1.56]). Ratings using THESIS had fair interrater reliability (κ = 0.3–0.6) and excellent scale reliability (ɑ = 0.85). Correlation with traditional star ratings was low (r = 0.24), suggesting THESIS captures issues beyond general user acceptance. Preliminary testing of THESIS suggests apps that serve patients with chronic disease could perform much better, particularly in privacy/security and interoperability. THESIS warrants further testing and may guide software and policymakers to further improve app performance, so apps can more consistently improve patient outcomes.

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Barriers to Hospital Electronic Public Health Reporting and Implications for the COVID-19 Pandemic

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