scholarly journals A systematic review of the use of the electronic health record for patient identification, communication, and clinical support in palliative care

JAMIA Open ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 294-303 ◽  
Author(s):  
Ruth A Bush ◽  
Alexa Pérez ◽  
Tanja Baum ◽  
Caroline Etland ◽  
Cynthia D Connelly
Keyword(s):  
Systematic Review ◽  
Palliative Care ◽  
Electronic Health Record ◽  
Original Research ◽  
Health Record ◽  
Clinical Workflow ◽  
Patient Identification ◽  
Patient Reported ◽  
Electronic Health ◽  
Prospective Longitudinal

Abstract Objectives Globally, healthcare systems are using the electronic health record (EHR) and elements of clinical decision support (CDS) to facilitate palliative care (PC). Examination of published results is needed to determine if the EHR is successfully supporting the multidisciplinary nature and complexity of PC by identifying applications, methodology, outcomes, and barriers of active incorporation of the EHR in PC clinical workflow. Methods A systematic review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources PubMed, CINAL, EBSCOhost, and Academic Search Premier were used to identify literature published 1999–2017 of human subject peer-reviewed articles in English containing original research about the EHR and PC. Results The search returned 433 articles, 30 of which met inclusion criteria. Most studies were feasibility studies or retrospective cohort analyses; one study incorporated prospective longitudinal mixed methods. Twenty-three of 30 (77%) were published after 2014. The review identified five major areas in which the EHR is used to support PC. Studies focused on CDS to: identify individuals who could benefit from PC; electronic advanced care planning (ACP) documentation; patient-reported outcome measures (PROMs) such as rapid, real-time pain feedback; to augment EHR PC data capture capabilities; and to enhance interdisciplinary communication and care. Discussion Beginning in 2015, there was a proliferation of articles about PC and EHRs, suggesting increasing incorporation of and research about the EHR with PC. This review indicates the EHR is underutilized for PC CDS, facilitating PROMs, and capturing ACPs.

scholarly journals Approaches for combining primary care electronic health record data from multiple sources: a systematic review of observational studies

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e037405
Author(s):  
Daniel Dedman ◽  
Melissa Cabecinha ◽  
Rachael Williams ◽  
Stephen J W Evans ◽  
Krishnan Bhaskaran ◽  
...  
Keyword(s):  
Systematic Review ◽  
Primary Care ◽  
Electronic Health Record ◽  
Observational Studies ◽  
Meta Analysis ◽  
Data Sources ◽  
Health Record ◽  
Two Stage ◽  
One Stage ◽  
Electronic Health

ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.DesignA systematic review of published studies.Data sourcesPubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.Study selectionObservational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.Results6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.ConclusionsComparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

What does social isolation have to do with my arthritis? Patient and provider views of the implementation of an electronic Social Determinants of Health risk assessment tool

2020 ◽  
Vol 64 (1) ◽  
pp. 707-710
Keyword(s):  
Electronic Health Record ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Assessment Tool ◽  
Determinants Of Health ◽  
Health Record ◽  
Institute Of Medicine ◽  
Patient Reported ◽  
Item Responses ◽  
Electronic Health

Social Determinants of Health (SDoH) are the conditions in which people are born, live, learn, work, and play that can affect health, functioning, and quality-of-life outcomes. The Institute of Medicine charged healthcare institutions with capturing and measuring patient SDoH risk factors through the electronic health record. Following the implementation of a social determinants of health electronic module across a major health institution, the response to institutional implementation was evaluated. To assess the response, a multidisciplinary team interviewed patients and providers, mapped the workflow, and performed simulated tests to trace the flow of SDoH data from survey item responses to visualization in EHR output for clinicians. Major results of this investigation were: 1) the lack of patient consensus about value of collecting SDOH data, and 2) the disjointed view of patient reported SDoH risks across patients, providers, and the electronic health record due to the way data was collected and visualized.

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

2019 ◽  
Vol 27 (3) ◽  
pp. 480-490 ◽  
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.

scholarly journals How well is the electronic health record supporting the clinical tasks of hospital physicians? A survey of physicians at three Norwegian hospitals

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Thomas Roger Schopf ◽  
Bente Nedrebø ◽  
Karl Ove Hufthammer ◽  
Inderjit Kaur Daphu ◽  
Hallvard Lærum
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Electronic Health Record System ◽  
System Stability ◽  
Medical Decision ◽  
Health Record ◽  
Clinical Workflow ◽  
Online Questionnaire ◽  
Record System ◽  
Electronic Health

Abstract Background The electronic health record is expected to improve the quality and efficiency of health care. Many novel functionalities have been introduced in order to improve medical decision making and communication between health care personnel. There is however limited evidence on whether these new functionalities are useful. The aim of our study was to investigate how well the electronic health record system supports physicians in performing basic clinical tasks. Methods Physicians of three prominent Norwegian hospitals participated in the survey. They were asked, in an online questionnaire, how well the hospital’s electronic health record system DIPS supported 49 clinical tasks as well as how satisfied they were with the system in general, including the technical performance. Two hundred and eight of 402 physicians (52%) submitted a completely answered questionnaire. Results Seventy-two percent of the physicians had their work interrupted or delayed because the electronic health record hangs or crashes at least once a week, while 22% had experienced this problem daily. Fifty-three percent of the physicians indicated that the electronic health record is cumbersome to use and adds to their workload. The majority of physicians were satisfied with managing tests, e.g., requesting laboratory tests, reading test results and managing radiological investigations and electrocardiograms. Physicians were less satisfied with managing referrals. There was high satisfaction with some of the decision support functionalities available for prescribing drugs. This includes drug interaction alerts and drug allergy warnings, which are displayed automatically. However, physicians were less satisfied with other aspects of prescribing drugs, including getting an overview of the ongoing drug therapy. Conclusions In the survey physicians asked for improvements of certain electronic health record functionalities like medication, clinical workflow support including planning and better overviews. In addition, there is apparently a need to focus on system stability, number of logins, reliability and better instructions on available electronic health record features. Considerable development is needed in current electronic health record systems to improve usefulness and satisfaction.

scholarly journals Frequency and Types of Patient-Reported Errors in Electronic Health Record Ambulatory Care Notes

2020 ◽  
Vol 3 (6) ◽  
pp. e205867 ◽  
Author(s):  
Sigall K. Bell ◽  
Tom Delbanco ◽  
Joann G. Elmore ◽  
Patricia S. Fitzgerald ◽  
Alan Fossa ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Ambulatory Care ◽  
Health Record ◽  
Patient Reported ◽  
Electronic Health

scholarly journals Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review

Informatics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 25
Author(s):  
Terrence C. Lee ◽  
Neil U. Shah ◽  
Alyssa Haack ◽  
Sally L. Baxter
Keyword(s):  
Systematic Review ◽  
Electronic Health Record ◽  
Clinical Outcomes ◽  
Predictive Models ◽  
Performance Metrics ◽  
Predictive Analytics ◽  
Health Record ◽  
Clinical Implementation ◽  
Level Of Evidence ◽  
Electronic Health

Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic review of articles describing predictive models integrated into EHR systems and implemented in clinical practice. We conducted an exhaustive database search and extracted data encompassing multiple facets of implementation. We assessed study quality and level of evidence. We obtained an initial 3393 articles for screening, from which a final set of 44 articles was included for data extraction and analysis. The most common clinical domains of implemented predictive models were related to thrombotic disorders/anticoagulation (25%) and sepsis (16%). The majority of studies were conducted in inpatient academic settings. Implementation challenges included alert fatigue, lack of training, and increased work burden on the care team. Of 32 studies that reported effects on clinical outcomes, 22 (69%) demonstrated improvement after model implementation. Overall, EHR-based predictive models offer promising results for improving clinical outcomes, although several gaps in the literature remain, and most study designs were observational. Future studies using randomized controlled trials may help improve the generalizability of findings.

Integrating patient-reported outcomes data into the electronic health record.

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 186-186
Author(s):  
Brandon Bosch ◽  
Scott Hartman ◽  
Lauren Caldarello ◽  
Diane Denny, DBA
Keyword(s):  
Electronic Health Record ◽  
Population Analysis ◽  
Treatment Plan ◽  
Symptom Burden ◽  
Symptom Assessment ◽  
Patient Reported Outcome ◽  
Health Record ◽  
National Network ◽  
Patient Reported ◽  
Electronic Health

186 Background: As a national network of hospitals that specialize in the treatment of patients fighting complex or advanced-stage cancer, the network was an early adopter of using patient reported outcome (PRO) data as part of its routine patient assessment and treatment. Since 2012 an externally validated tool has been used to capture patients’ perceived symptom burden for real-time clinical intervention, from the point of first visit throughout the course of treatment, at intervals of 21 days or greater. Research has demonstrated the use of PRO data as a valuable component of a patient’s treatment plan, promoting improved quality and length of life. Methods: The use of this data across the network was expanded such that results once only accessible on paper and via electronically stored images, has now been fully integrated into the electronic health record (EHR). A multidisciplinary project team formulated the specifications for a successful integration of PRO data into the EHR. Results: The project achieved its goal and went beyond data integration to include implementation of a solution to facilitate documentation of intervention against patients’ symptoms. Provider workflow efficiency is greatly enhanced via single system access and visual notification, with critical values flagged, to focus providers’ attention on severe symptoms. Incorporation of a unified EHR flowsheet provides a paperless, one-stop symptom assessment approach and streamlined mechanism for intervention documentation. The documentation module leverages structured data fields and linkage of PRO data with interventions, such as specialist referrals or medication orders, to support enhanced patient care and quality improvement. Conclusions: The ability to easily view an array of patient reported concerns and document interventions against severe or significantly worsening symptoms provides clinicians an enhanced ability to address quality of life related needs. PRO data is now stored electronically in the enterprise warehouse, thus enabling aggregation with data from which to perform population analysis and eventually, pursue opportunities for predictive modeling.

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