Nurse workarounds in the electronic health record: An integrative review

Abstract Objective The study sought to synthesize published literature on direct care nurses’ use of workarounds related to the electronic health record. Materials and Methods We conducted an integrative review of qualitative and quantitative peer-reviewed research through a structured search of Academic Search Complete, EBSCO Cumulative Index of Nursing and Allied Health Literature (CINAHL), Embase, Engineering Village, Ovid Medline, Scopus, and Web of Science. We systematically applied exclusion rules at the title, abstract, and full article stages and extracted and synthesized their research methods, workaround classifications, and probable causes from articles meeting inclusion criteria. Results Our search yielded 5221 results. After removing duplicates and applying rules, 33 results met inclusion criteria. A total of 22 articles used qualitative approaches, 10 used mixed methods, and 1 used quantitative methods. While researchers may classify workarounds differently, they generally fit 1 of 3 broad categories: omission of process steps, steps performed out of sequence, and unauthorized process steps. Each study identified probable causes, which included technology, task, organizational, patient, environmental, and usability factors. Conclusions Extensive study of nurse workarounds in acute settings highlights the gap in ambulatory care research. Despite decades of electronic health record development, poor usability remains a key concern for nurses and other members of care team. The widespread use of workarounds by the largest group of healthcare providers subverts quality health care at every level of the healthcare system. Research is needed to explore the gaps in our understanding of and identify strategies to reduce workaround behaviors.

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Clinical decision support alert malfunctions: analysis and empirically derived taxonomy

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocx106 ◽

2017 ◽

Vol 25 (5) ◽

pp. 496-506 ◽

Cited By ~ 21

Author(s):

Adam Wright ◽

Angela Ai ◽

Joan Ash ◽

Jane F Wiesen ◽

Thu-Trang T Hickman ◽

...

Keyword(s):

Decision Support ◽

Electronic Health Record ◽

Medical Informatics ◽

Clinical Decision Support ◽

Quantitative Methods ◽

Clinical Decision ◽

The United States ◽

Health Record ◽

Qualitative And Quantitative Methods ◽

Electronic Health

Abstract Objective To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. Materials and Methods We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. Results We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Discussion Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. Conclusion CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

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Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocz196 ◽

2019 ◽

Vol 27 (3) ◽

pp. 480-490 ◽

Cited By ~ 6

Author(s):

Adam Rule ◽

Michael F Chiang ◽

Michelle R Hribar

Keyword(s):

Systematic Review ◽

Access Control ◽

Electronic Health Record ◽

Chart Review ◽

Observational Research ◽

Clinical Activity ◽

Health Record ◽

Problem List ◽

Inclusion Criteria ◽

Electronic Health

Abstract Objective To systematically review published literature and identify consistency and variation in the aims, measures, and methods of studies using electronic health record (EHR) audit logs to observe clinical activities. Materials and Methods In July 2019, we searched PubMed for articles using EHR audit logs to study clinical activities. We coded and clustered the aims, measures, and methods of each article into recurring categories. We likewise extracted and summarized the methods used to validate measures derived from audit logs and limitations discussed of using audit logs for research. Results Eighty-five articles met inclusion criteria. Study aims included examining EHR use, care team dynamics, and clinical workflows. Studies employed 6 key audit log measures: counts of actions captured by audit logs (eg, problem list viewed), counts of higher-level activities imputed by researchers (eg, chart review), activity durations, activity sequences, activity clusters, and EHR user networks. Methods used to preprocess audit logs varied, including how authors filtered extraneous actions, mapped actions to higher-level activities, and interpreted repeated actions or gaps in activity. Nineteen studies validated results (22%), but only 9 (11%) through direct observation, demonstrating varying levels of measure accuracy. Discussion While originally designed to aid access control, EHR audit logs have been used to observe diverse clinical activities. However, most studies lack sufficient discussion of measure definition, calculation, and validation to support replication, comparison, and cross-study synthesis. Conclusion EHR audit logs have potential to scale observational research but the complexity of audit log measures necessitates greater methodological transparency and validated standards.

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Assessment of Data Quality of and Staff Satisfaction with an Electronic Health Record System in a Developing Country (Uganda)

Methods of Information in Medicine ◽

10.3414/me0511 ◽

2008 ◽

Vol 47 (06) ◽

pp. 489-498 ◽

Cited By ~ 13

Author(s):

S.P. Ndira ◽

K.D. Rosenberger ◽

T. Wetter

Keyword(s):

Electronic Health Record ◽

Quantitative Methods ◽

Electronic System ◽

Hospital Staff ◽

Health Record ◽

Staff Satisfaction ◽

District Health ◽

Record System ◽

Post Test ◽

Electronic Health

Summary Objectives: To assess if electronic health record systems in developing countries can improve on timeliness, availability and accuracy of routine health reports and staff satisfaction after introducing the electronic system, compared to the paper-based alternative. Methods: The research was conducted with hospital staff of Tororo District Hospital in Uganda. A comparative intervention study with qualitative and quantitative methods was used to compare the paper-based (pre-test) to the electronic system (post-test) focusing on accuracy, availability and timeliness of monthly routine reports about mothers visiting the hospital; and staff satisfaction with the electronic system as outcome measures. Results: Timeliness: pre-test 13 of 19 months delivered to the district timely, delivery dates for six months could not be established; post-test 100%. Availability: pretest 79% of reports were present at the district health office; post-test 100%. Accuracy: pre-test 73.2% of selected reports could be independently confirmed as correct; post-test 71.2%. Difficulties were encountered in finding enough mothers through direct follow up to inquire on accuracy of information recorded about them. Staff interviews showed that the electronic system is appreciated by the majority of the hospital staff. Remaining obstacles include staff workload, power shortages, network breakdowns and parallel data entry (paper-based and electronic). Conclusion: While timeliness and availability improved, improvement of accuracy could not be established. Better approaches to ascertaining accuracy have to be devised, e.g. evaluation of intended use. For success, organizational, managerial and social challenges must be addressed beyond technical aspects.

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NURSING EDUCATION AND TRAINING ON ELECTRONIC HEALTH RECORD SYSTEMS: AN INTEGRATIVE REVIEW

Nurse Education in Practice ◽

10.1016/j.nepr.2021.103168 ◽

2021 ◽

pp. 103168

Author(s):

Justine Ting ◽

Anna Garnett ◽

Lorie Donelle

Keyword(s):

Nursing Education ◽

Electronic Health Record ◽

Education And Training ◽

Integrative Review ◽

Health Record ◽

Electronic Health ◽

And Training

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Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: A Retrospective Analysis and Predictive Model (Preprint)

10.2196/preprints.10458 ◽

2018 ◽

Cited By ~ 2

Author(s):

Hadi Kharrazi ◽

Claudia P Gonzalez ◽

Kevin B Lowe ◽

Timothy R Huerta ◽

Eric W Ford

Keyword(s):

Electronic Health Record ◽

Technology Diffusion ◽

Health Care Quality ◽

Care Delivery ◽

Care Quality ◽

Direct Care ◽

Health Record ◽

Bass Model ◽

Ehr Adoption ◽

Electronic Health

BACKGROUND The Meaningful Use (MU) program has promoted electronic health record (EHR) adoption among US hospitals. Studies have shown that EHR adoption has been slower than desired in certain types of hospitals; but generally, the overall adoption rate has increased among hospitals. However, these studies have neither evaluated the adoption of advanced functionalities of EHRs (beyond MU) nor forecasted EHR maturation over an extended period in a holistic fashion. Additional research is needed to prospectively assess US hospitals’ EHR technology adoption and advancement patterns. OBJECTIVE This study forecasts the maturation of EHR functionality adoption among US hospitals through 2035. METHODS The Healthcare Information and Management Systems Society (HIMSS) Analytics’ Electronic Medical Record Adoption Model (EMRAM) dataset was used to track historic uptakes of various EHR functionalities considered critical to improving health care quality and efficiency in hospitals. The Bass model was used to predict the technological diffusion rates for repeated EHR adoptions where upgrades undergo rapid technological improvements. The forecast used EMRAM data from 2006 to 2014 to estimate adoption levels to the year 2035. RESULTS In 2014, over 5400 hospitals completed HIMSS’ annual EMRAM survey (86%+ of total US hospitals). In 2006, the majority of the US hospitals were in EMRAM Stages 0, 1, and 2. By 2014, most hospitals had achieved Stages 3, 4, and 5. The overall technology diffusion model (ie, the Bass model) reached an adjusted R-squared of .91. The final forecast depicted differing trends for each of the EMRAM stages. In 2006, the first year of observation, peaks of Stages 0 and 1 were shown as EHR adoption predates HIMSS’ EMRAM. By 2007, Stage 2 reached its peak. Stage 3 reached its full height by 2011, while Stage 4 peaked by 2014. The first three stages created a graph that exhibits the expected “S-curve” for technology diffusion, with inflection point being the peak diffusion rate. This forecast indicates that Stage 5 should peak by 2019 and Stage 6 by 2026. Although this forecast extends to the year 2035, no peak was readily observed for Stage 7. Overall, most hospitals will achieve Stages 5, 6, or 7 of EMRAM by 2020; however, a considerable number of hospitals will not achieve Stage 7 by 2035. CONCLUSIONS We forecasted the adoption of EHR capabilities from a paper-based environment (Stage 0) to an environment where only electronic information is used to document and direct care delivery (Stage 7). According to our forecasts, the majority of hospitals will not reach Stage 7 until 2035, absent major policy changes or leaps in technological capabilities. These results indicate that US hospitals are decades away from fully implementing sophisticated decision support applications and interoperability functionalities in EHRs as defined by EMRAM’s Stage 7.

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Developing Minimum Dataset For Electronic Health Record In Iran

10.21203/rs.3.rs-1035605/v1 ◽

2021 ◽

Author(s):

Marziye Meraji ◽

Roghaye estaji ◽

Mehrsadat Mahdizadeh ◽

Elaheh Hooshmand

Keyword(s):

Health Care ◽

Electronic Health Record ◽

Health System ◽

Quantitative Methods ◽

Health Information System ◽

Vital Role ◽

Health Record ◽

Middle Income ◽

Electronic Health ◽

Minimum Dataset

Abstract Background: Health information system is an integral part of the health system that has a vital role in increasing the efficiency of the health system, especially in primary health care settings. This study was conducted to determine the minimum dataset required in the electronic health record within the health system of Iran as a lower middle income country.Method: This study combines qualitative and quantitative methods. It includes three main stages: reviewing the theoretical foundations of research, designing the main framework for interview questions, conducting a qualitative study by interviewing 42 managers of the health system across the country to determine the minimum dataset in the electronic health. Interviews were carried out from 2020 to 2021. The validity of data was assessed by Delphi method using SPSS 15 software.Results: After reviewing the minimum dataset in the electronic health records of seven countries, 7 main concepts and 23 sub-concepts were extracted from the interviews with experts across the country. Accordingly, 159 information elements were surveyed and a two-round Delphi provided 145 information elements in seven categories of children's program, mothers' program, mental health, elderly, paraclinical services, drug program, and vaccination.Conclusion: Health systems in different countries determine the minimum dataset required in health care setting based on their demographic and epidemiological needs, which can facilitate access to accurate and unambiguous information.

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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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