The Impact of Disruption of the Care Delivery System by Commercial Laboratory Testing in a Children's Health Care System

Context.— Disruption of outpatient laboratory services by routing the samples to commercial reference laboratories may seem like a cost-saving measure by the payers, but results in hidden costs in quality and resources to support this paradigm. Objective.— To identify differences when outpatient tests are performed at the Children's Healthcare of Atlanta (Children's) Hospital lab compared to a commercial reference lab, and the financial costs to support the reference laboratory testing. Design.— Outpatient testing was sent to 3 different laboratories specified by the payer. Orders were placed in the Children's electronic health record, blood samples were drawn by the Children's phlebotomists, samples were sent to the testing laboratory, and results appeared in the electronic health record. Data comparing the time to result, cancelled samples, and cost to sustain the system of ordering and reporting were drawn from multiple sources, both electronic and manual. Results.— The median time from phlebotomy to result was 0.7 hours for testing at the Children's lab and 20.72 hours for the commercial lab. The median time from result posting to caregiver acknowledgment was 5.4 hours for the Children's lab and 18 hours for the commercial lab. The commercial lab cancelled 2.7% of the tests; the Children's lab cancelled 0.8%. The financial cost to support online ordering and reporting for testing performed at commercial labs was approximately $640,000 per year. Conclusions.— Tangible monetary costs, plus intangible costs related to delayed results, occur when the laboratory testing system is disrupted.

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Visualization of Electronic Health Record Data for Decision-Making in Diabetes and Congestive Heart Failure

ACI Open ◽

10.1055/s-0040-1702213 ◽

2020 ◽

Vol 04 (01) ◽

pp. e35-e43

Author(s):

Shira H. Fischer ◽

Charles Safran ◽

Krzysztof Z. Gajos ◽

Adam Wright

Keyword(s):

Decision Making ◽

Electronic Health Record ◽

Primary Care Providers ◽

Difficulty Level ◽

Health Record ◽

Care Providers ◽

Electronic Health Record Data ◽

Record Data ◽

Electronic Health ◽

The Impact

Abstract Objective The aim of this study is to study the impact of graphical representation of health record data on physician decision-making to inform the design of health information technology. Materials and Methods We conducted a within participants crossover design study using a simulated electronic health record (EHR) in which we presented cases with and without visualized data designed to highlight important clinical trends or relationships, followed by assessment of the impact on decision-making about next steps for patients with chronic diseases. We then asked whether trends were observed and about usability and satisfaction using validated usability questions and asked open-ended questions as well. Time to answer questions was also collected. Results Twenty-one primary care providers participated in the study, including five for testing only and sixteen for the full study. Questions about clinical assessment or next actions were answered correctly 55% of the time. Regarding objective trends in the data, participants described noticing the trends 85% of the time. Differences in noticing trends or difficulty level of questions were not statistically significant. Satisfaction with the tool was high and participants agreed strongly that it helped them make better decisions without adding to the time it took. Discussion The simulation allowed us to test the impact of a visualization on clinician practice in a realistic setting. Designers of EHRs should consider the ways information presentation can affect decision-making. Conclusion Testing visualization tools can be done in a clinically realistic context. Providers desire visualizations and believe that they help them make better and faster decisions.

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Abstract P112: Impact Of Look Back Period Length On Associations When Using Electronic Health Record Data For Epidemiologic Research

Circulation ◽

10.1161/circ.143.suppl_1.p112 ◽

2021 ◽

Vol 143 (Suppl_1) ◽

Author(s):

Nrupen A Bhavsar ◽

John Pura ◽

Ann Marie Navar ◽

Anne Hellkamp ◽

Paul Muntner ◽

...

Keyword(s):

Electronic Health Record ◽

Period Length ◽

Health Record ◽

Electronic Health Record Data ◽

Glucose Lowering ◽

Medicare Claims ◽

Look Back ◽

Electronic Health ◽

The Impact

Introduction: Studies using electronic health record (EHR) data often have a limited number of years available for analysis. There is a trade off between the look back period length used to define baseline characteristics and follow up duration used to define outcomes. Objective: Quantify the impact of 6, 12, and 24 month look back periods on the association between diabetes (DM) and subsequent cardiovascular (CV) events using EHR data alone and in combination with Medicare claims. Methods: EHR data from an academic health system and a federally qualified health center from 2009-2014 were linked to Medicare claims data. Eligibility criteria were age ≥65 years, Durham County address, 24 months of continuous enrollment after first claim, EHR encounter in the 2011 index year, and no history of cardiovascular disease (CVD) in the 24 months prior to the index date (i.e., look back period). DM was defined using EHR ICD-9 codes, HbA1c ≥6.5%, or glucose lowering medication, and using claims based diagnosis codes or glucose lowering medication. The outcome was a major CV event (myocardial infarction, stroke, or cardiac procedure) defined by diagnosis or procedure codes. Hazard ratios (HR) compared time to the outcome between patients with and without DM. Results: In 5473 patients, mean age was 77 years, 67% were female and 28% were Black. The prevalence of DM using EHR data only increased with a longer look back period (6 months [19%]; 12 months [21%]; 24 months [23%]) but was less than the prevalence using all available data from EHRs and claims together (28%) (Table 1A). Shorter look back periods resulted in higher HRs (6 month HR=1.64) as compared to HRs from longer look back periods (24 month HR=1.41) using EHR data alone or all available data from the EHR and claims together (HR=1.43) (Table 1B). Conclusions: To avoid over estimating associations, studies of CVD using EHR data to identify baseline conditions may want to use 12-24 month look back periods in the absence of additional administrative data. This may also lead to a shorter follow-up period.

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Clinical Research Informatics and Electronic Health Record Data

Yearbook of Medical Informatics ◽

10.15265/iy-2014-0009 ◽

2014 ◽

Vol 23 (01) ◽

pp. 215-223 ◽

Cited By ~ 11

Author(s):

M. M. Horvath ◽

S. A. Rusincovitch ◽

R. L. Richesson

Keyword(s):

Clinical Research ◽

Electronic Health Record ◽

Clinical Data ◽

Health Record ◽

Healthcare Operations ◽

Electronic Health Record Data ◽

Pragmatic Clinical Trials ◽

Clinical Research Informatics ◽

Electronic Health ◽

The Impact

Summary Objectives: The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics. Results: Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data. Conclusions: The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI’s key role in the infrastructure of a learning healthcare system.

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Use of Electronic Health Record Data to Evaluate the Impact of Race on 30-Day Mortality in Patients Admitted to the Intensive Care Unit

Journal of Racial and Ethnic Health Disparities ◽

10.1007/s40615-016-0256-6 ◽

2016 ◽

Vol 4 (4) ◽

pp. 539-548 ◽

Cited By ~ 1

Author(s):

Mallika L. Mundkur ◽

Fiona M. Callaghan ◽

Swapna Abhyankar ◽

Clement J. McDonald

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Electronic Health Record ◽

Health Record ◽

Electronic Health Record Data ◽

Record Data ◽

Electronic Health ◽

The Impact

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The electronic health record as a clinical trials tool: Opportunities and challenges

Clinical Trials ◽

10.1177/1740774520913819 ◽

2020 ◽

Vol 17 (3) ◽

pp. 237-242 ◽

Cited By ~ 4

Author(s):

Monica M Bertagnolli ◽

Brian Anderson ◽

Andre Quina ◽

Steven Piantadosi

Keyword(s):

Clinical Trials ◽

Clinical Research ◽

Electronic Health Record ◽

Care Delivery ◽

Clinical Care ◽

Health Record ◽

Electronic Health Record Data ◽

Treatment Protocols ◽

Record Data ◽

Electronic Health

Clinical trials provide evidence essential for progress in health care, and as the complexity of medical care has increased, the demand for such data has dramatically expanded. Conducting clinical trials has also become more complicated, evolving to meet increasing challenges in delivering clinical care and meeting regulatory requirements. Despite this, the general approach to data collection remains the same, requiring that researchers submit clinical data in response to study treatment protocols, using precisely defined data structures made available in study-specific case report forms. Currently, research data management is not integrated within the patient’s clinical care record, creating added burden for clinical staff and opportunities for error. During the past decade, the electronic health record has become standard across the US healthcare system and is increasingly used to collect and analyze data reporting quality metrics for clinical care delivery. Recently, electronic health record data have also been used to address clinical research questions; however, this approach has significant drawbacks due to the unstructured and incomplete nature of current electronic health record data. This report describes steps necessary to use the electronic health record as a tool for conducting high-quality clinical research.

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Automated chronic disease surveillance and visualization using electronic health record data

Emerging Health Threats Journal ◽

10.3402/ehtj.v4i0.11102 ◽

2011 ◽

Vol 4 (0) ◽

Author(s):

Michael Klompas ◽

Chaim Kirby ◽

Jason McVetta ◽

Paul Oppedisano ◽

John Brownstein ◽

...

Keyword(s):

Chronic Disease ◽

Electronic Health Record ◽

Disease Surveillance ◽

Health Record ◽

Electronic Health Record Data ◽

Record Data ◽

Electronic Health

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Faculty Opinions recommendation of Evaluating delivery of low tidal volume ventilation in six icus using electronic health record data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.734212415.793572533 ◽

2020 ◽

Author(s):

Jeremy Beitler

Keyword(s):

Electronic Health Record ◽

Tidal Volume ◽

Health Record ◽

Electronic Health Record Data ◽

Low Tidal Volume ◽

Low Tidal Volume Ventilation ◽

Volume Ventilation ◽

Record Data ◽

Electronic Health

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Management of a parenteral opioid shortage using ASHP guidelines

American Journal of Health-System Pharmacy ◽

10.1093/ajhp/zxaa425 ◽

2021 ◽

Vol 78 (5) ◽

pp. 426-435

Author(s):

Peter Vo ◽

Daniel A Sylvia ◽

Loay Milibari ◽

John Ryan Stackhouse ◽

Paul Szumita ◽

...

Keyword(s):

Electronic Health Record ◽

Drug Product ◽

Health Record ◽

Large Hospital ◽

Hospital Administrators ◽

Utilization Data ◽

Electronic Health ◽

Opioid Medications ◽

American Society ◽

The Impact

Abstract Purpose Management of an acute shortage of parenteral opioid products at a large hospital through prescribing interventions and other guideline-recommended actions is described. Summary In early 2018, many hospitals were faced with a shortage of parenteral opioids that was predicted to last an entire year. The American Society of Health-System Pharmacists (ASHP) has published guidelines on managing drug product shortages. This article describes the application of these guidelines to manage the parenteral opioid shortage and the impact on opioid dispensing that occurred in 2018. Our approach paralleled that recommended in the ASHP guidelines. Daily dispensing reports generated from automated dispensing cabinets and from the electronic health record were used to capture dispenses of opioid medications. Opioid prescribing and utilization data were converted to morphine milligram equivalents (MME) to allow clinical leaders and hospital administrators to quickly evaluate opioid inventories and consumption. Action steps included utilization of substitute opioid therapies and conversion of opioid patient-controlled analgesia (PCA) and opioid infusions to intravenous bolus dose administration. Parenteral opioid supplies were successfully rationed so that surgical and elective procedures were not canceled or delayed. During the shortage, opioid dispensing decreased in the inpatient care areas from approximately 2.0 million MME to 1.4 million MME and in the operating rooms from 0.56 MME to 0.29 million MME. The combination of electronic health record alerts, increased utilization of intravenous acetaminophen and liposomal bupivacaine, and pharmacist interventions resulted in a 67% decline in PCA use and a 65% decline in opioid infusions. Conclusion A multidisciplinary response is necessary for effective management of drug shortages through implementation of strategies and practices for notifying clinicians of shortages and identifying optimal alternative therapies.

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