scholarly journals Continuing Patient Care during Electronic Health Record Downtime

2019 ◽  
Vol 10 (03) ◽  
pp. 495-504
Author(s):  
Ethan Larsen ◽  
Daniel Hoffman ◽  
Carlos Rivera ◽  
Brian M. Kleiner ◽  
Christian Wernz ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Electronic Health Record ◽  
Clinical Laboratory ◽  
Normal Operation ◽  
Contingency Planning ◽  
Health Record ◽  
Safety Assurance ◽  
Hospital Operations ◽  
Recorded Data ◽  
Electronic Health

Introduction Electronic health record (EHR) downtime is any period during which the EHR system is fully or partially unavailable. These periods are operationally disruptive and pose risks to patients. EHR downtime has not sufficiently been studied in the literature, and most hospitals are not adequately prepared. Objective The objective of this study was to assess the operational implications of downtime with a focus on the clinical laboratory, and to derive recommendations for improved downtime contingency planning. Methods A hybrid qualitative–quantitative study based on historic performance data and semistructured interviews was performed at two mid-Atlantic hospitals. In the quantitative analysis, paper records from downtime events were analyzed and compared with normal operations. To enrich this quantitative analysis, interviews were conducted with 17 hospital employees, who had experienced several downtime events, including a hospital-wide EHR shutdown. Results During downtime, laboratory testing results were delayed by an average of 62% compared with normal operation. However, the archival data were incomplete due to inconsistencies in the downtime paper records. The qualitative interview data confirmed that delays in laboratory result reporting are significant, and further uncovered that the delays are often due to improper procedural execution, and incomplete or incorrect documentation. Interviewees provided a variety of perspectives on the operational implications of downtime, and how to best address them. Based on these insights, recommendations for improved downtime contingency planning were derived, which provide a foundation to enhance Safety Assurance Factors for EHR Resilience guides. Conclusion This study documents the extent to which downtime events are disruptive to hospital operations. It further highlights the challenge of quantitatively assessing the implication of downtimes events, due to a lack of otherwise EHR-recorded data. Organizations that seek to improve and evaluate their downtime contingency plans need to find more effective methods to collect data during these times.

scholarly journals Analyses of Original and Computationally-Derived Electronic Health Record Data: The National COVID Cohort Collaborative (Preprint)

2021 ◽  
Author(s):  
Randi Foraker ◽  
Aixia Guo ◽  
Jason Thomas ◽  
Noa Zamstein ◽  
Philip R.O. Payne ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Predictive Models ◽  
Clinical Laboratory ◽  
Synthetic Data ◽  
Original Data ◽  
Health Record ◽  
Use Case ◽  
Electronic Health Record Data ◽  
Electronic Health ◽  
Synthetic Derivatives

BACKGROUND Background: Synthetic data can be used by collaborators to generate and share data in support of answering critical research questions to address the COVID-19 pandemic. Computationally-derived (“synthetic”) data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record (EHR) data. OBJECTIVE Objectives: To compare the results of analyses using synthetic derivatives to analyses using the original data downloaded from a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) to assess the strengths and limitations of leveraging computationally-derived data for research purposes. METHODS Methods: We used the National COVID Cohort Collaborative’s (N3C) instance of MDClone, comprising EHR data from 34 N3C institutional partners. We tested three use cases, including (1) exploring the distributions of key features of the COVID-positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-related measures and outcomes, and constructing their respective epidemic curves. We compared the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, temporal and spatial representations of the data. RESULTS Results: For each use case, the results of the synthetic data analyses successfully mimicked those of the original data such that the distributions of the data were similar and the predictive models demonstrated comparable performance. While the synthetic and original data yielded overall nearly the same results, there were exceptions which included an odds ratio on either side of the null in multivariable analyses (0.97 versus 1.01) and epidemic curves constructed for zip codes with low population counts. CONCLUSIONS Discussion & Conclusion: This paper presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in collaborative research for faster insights. CLINICALTRIAL N/A

Investigating Electronic Health Record Downtimes: A Scoping Review of Literature and News Media (Preprint)

2019 ◽  
Author(s):  
Ethan Larsen ◽  
Arjun Rao ◽  
Farzan Sasangohar
Keyword(s):  
Electronic Health Record ◽  
Patient Care ◽  
Scoping Review ◽  
News Media ◽  
Normal Operation ◽  
Health Record ◽  
Cyber Attack ◽  
Top Down ◽  
Bottom Up ◽  
Electronic Health

BACKGROUND Electronic Health Record Systems have become ubiquitous in the delivery of patient care. While the implementation has brought safety and efficiency boosts to the industry, it has also exposed patients and their data to new risks in the form of downtime. Downtimes are any period where the computer systems are unavailable and these periods occur for updates or upgrades, but can also be triggered by deliberate cyber-attack. During an unexpected downtime, healthcare workers are forced to fall back to rarely practiced paper-based methods for healthcare delivery, while at the same time, patient data is potentially exposed to parties seeking to profit from its sale. OBJECTIVE We sought to provide a foundational perspective of the current state of downtime readiness in light of the growing cyber-attack threat on healthcare data and hospital networks. METHODS A search of technical news media related to healthcare informatics and a scoping review of research literature were conducted. Following the ENTEREQ framework, 1,651 records were retrieved, of which 16 were included in the final review. RESULTS 164 US-based hospitals experienced a total of 670 days of downtime in 41 events between 2012 and 2018. Almost half (48.8%) of the published downtime events involved some form of cyber-attack. 1,651 studies matching downtime search strings were found, 16 of which were found to meet inclusion criteria. Few research studies have a downtime emphasis; those that do are predominantly focused on a top-down approach. They were found to have a range of focus from the theoretical exploration of downtime to direct empirical comparison of downtime versus normal operation. CONCLUSIONS Downtime contingency planning is still predominantly considered in abstract or top-down organizational focus. It is proposed that a bottom-up approach to comprehending and addressing downtime will be beneficial due to the complicated nature of patient care and computer downtime events. A bottom-up approach would involve the front-line clinical staff responsible for executing the downtime procedure and directly caring for the patients. EHR downtime events will continue to be a complication to hospital and healthcare operations. Significant new research support for the development of contingency plans will be needed as the cyber-attack threat continues to grow.

scholarly journals Adherence to recommended electronic health record safety practices across eight health care organizations

2018 ◽  
Vol 25 (7) ◽  
pp. 913-918 ◽  
Author(s):  
Dean F Sittig ◽  
Mandana Salimi ◽  
Ranjit Aiyagari ◽  
Colin Banas ◽  
Brian Clay ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health Care Organizations ◽  
National Policy ◽  
Health Record ◽  
Health It ◽  
Clinical Communication ◽  
Safety Assurance ◽  
Ehr Adoption ◽  
Electronic Health ◽  
System Interfaces

Abstract Objective The Safety Assurance Factors for EHR Resilience (SAFER) guides were released in 2014 to help health systems conduct proactive risk assessment of electronic health record (EHR)- safety related policies, processes, procedures, and configurations. The extent to which SAFER recommendations are followed is unknown. Methods We conducted risk assessments of 8 organizations of varying size, complexity, EHR, and EHR adoption maturity. Each organization self-assessed adherence to all 140 unique SAFER recommendations contained within 9 guides (range 10–29 recommendations per guide). In each guide, recommendations were organized into 3 broad domains: “safe health IT” (total 45 recommendations); “using health IT safely” (total 80 recommendations); and “monitoring health IT” (total 15 recommendations). Results The 8 sites fully implemented 25 of 140 (18%) SAFER recommendations. Mean number of “fully implemented” recommendations per guide ranged from 94% (System Interfaces—18 recommendations) to 63% (Clinical Communication—12 recommendations). Adherence was higher for “safe health IT” domain (82.1%) vs “using health IT safely” (72.5%) and “monitoring health IT” (67.3%). Conclusions Despite availability of recommendations on how to improve use of EHRs, most recommendations were not fully implemented. New national policy initiatives are needed to stimulate implementation of these best practices.

Business: The Electronic Health Record: Front Line Experience

2011 ◽  
Vol 21 (1) ◽  
pp. 18-22
Author(s):  
Rosemary Griffin
Keyword(s):  
Health Care ◽  
Information Technology ◽  
Electronic Health Record ◽  
Financial Incentives ◽  
Best Practice ◽  
Health Care Systems ◽  
The United States ◽  
Health Record ◽  
Health Trends ◽  
Electronic Health

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.

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