Guide for Coordination of Clinical Laboratory Services within the Electronic Health Record Environment and Networked Architectures

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.

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

10.2196/preprints.30697 ◽

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

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Business: The Electronic Health Record: Front Line Experience

Perspectives on Administration and Supervision ◽

10.1044/aas21.1.18 ◽

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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