Introducing SNOMED-CT* Coding into an Electronic Health Record: Impact on Clinicians, Data Sharing and Research potential *Systemised Nomenclature of Medicine Clinical Terminology (Preprint)

2021 ◽  
Author(s):  
Tanya Pankhurst ◽  
Felicity Evison ◽  
Jolene Atia ◽  
Suzy Gallier ◽  
Jamie Coleman ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Data Sharing ◽  
Clinical Decision ◽  
Health Record ◽  
Coding System ◽  
Snomed Ct ◽  
Healthcare Settings ◽  
Electronic Health ◽  
Systematized Nomenclature Of Medicine

BACKGROUND This study describes the conversion within an existing Electronic Health Record (EHR) from the coding system International Classification of Diseases version 10 (ICD-10) to the Systematized Nomenclature Of MEDicine - Clinical Terms (SNOMED-CT), for collection of patients’ history and diagnoses. The setting is a large acute hospital, designing and building its own EHR. Well-designed EHRs create opportunities for continuous data collection which can be utilised in Clinical Decision Support rules to drive patient safety. Collected data can be exchanged across healthcare systems to support patients in all healthcare settings. Data can be used for research to prevent disease and protect future populations. OBJECTIVE To migrate a current electronic health record, with all relevant patient data, to the coding system, Systematized Nomenclature of Medicine - Clinical Terms, to optimise clinical utilisation and clinical decision support, and facilitate data sharing across organisational boundaries for national programmes, and remodelling of medical pathways. METHODS The study used qualitative and quantitative data to understand the successes and gaps in the project, clinician attitudes to the new tool, and future use. RESULTS The new coding system (“tool”) was well received and immediately widely used in all specialities. It resulted in increased, accurate and clinically relevant data collection. Clinicians appreciated the increased depth and detail of the new coding, welcomed the potential for both data sharing and research, and gave extensive feedback for further development. CONCLUSIONS Successful implementation aligned the Trust with national strategy and can be used as a Blueprint for similar projects in other healthcare settings. CLINICALTRIAL NA

scholarly journals Introduction of Systematized Nomenclature of Medicine–Clinical Terms Coding Into an Electronic Health Record and Evaluation of its Impact: Qualitative and Quantitative Study

10.2196/29532 ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. e29532
Author(s):  
Tanya Pankhurst ◽  
Felicity Evison ◽  
Jolene Atia ◽  
Suzy Gallier ◽  
Jamie Coleman ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Data Collection ◽  
Clinical Decision ◽  
Health Record ◽  
Coding System ◽  
Snomed Ct ◽  
Health Care Settings ◽  
Qualitative And Quantitative ◽  
Systematized Nomenclature Of Medicine

Background This study describes the conversion within an existing electronic health record (EHR) from the International Classification of Diseases, Tenth Revision coding system to the SNOMED-CT (Systematized Nomenclature of Medicine–Clinical Terms) for the collection of patient histories and diagnoses. The setting is a large acute hospital that is designing and building its own EHR. Well-designed EHRs create opportunities for continuous data collection, which can be used in clinical decision support rules to drive patient safety. Collected data can be exchanged across health care systems to support patients in all health care settings. Data can be used for research to prevent diseases and protect future populations. Objective The aim of this study was to migrate a current EHR, with all relevant patient data, to the SNOMED-CT coding system to optimize clinical use and clinical decision support, facilitate data sharing across organizational boundaries for national programs, and enable remodeling of medical pathways. Methods The study used qualitative and quantitative data to understand the successes and gaps in the project, clinician attitudes toward the new tool, and the future use of the tool. Results The new coding system (tool) was well received and immediately widely used in all specialties. This resulted in increased, accurate, and clinically relevant data collection. Clinicians appreciated the increased depth and detail of the new coding, welcomed the potential for both data sharing and research, and provided extensive feedback for further development. Conclusions Successful implementation of the new system aligned the University Hospitals Birmingham NHS Foundation Trust with national strategy and can be used as a blueprint for similar projects in other health care settings.

scholarly journals Dissemination of child abuse clinical decision support: Moving beyond a single electronic health record

2021 ◽  
Vol 147 ◽  
pp. 104349
Author(s):  
Thomas McGinn ◽  
David A. Feldstein ◽  
Isabel Barata ◽  
Emily Heineman ◽  
Joshua Ross ◽  
...  
Keyword(s):  
Child Abuse ◽  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Electronic Health

scholarly journals Developing Clinical Decision Support within a Commercial Electronic Health Record System to Improve Antimicrobial Prescribing in the Neonatal ICU

2014 ◽  
Vol 05 (02) ◽  
pp. 368-387 ◽  
Author(s):  
K. Cato ◽  
B. Sheehan ◽  
S. Patel ◽  
J. Duchon ◽  
P. DeLaMora ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Electronic Health Record System ◽  
Health Record ◽  
Record System ◽  
Antimicrobial Prescribing ◽  
Neonatal Icu ◽  
Electronic Health

SummaryObjective: To develop and implement a clinical decision support (CDS) tool to improve antibiotic prescribing in neonatal intensive care units (NICUs) and to evaluate user acceptance of the CDS tool.Methods: Following sociotechnical analysis of NICU prescribing processes, a CDS tool for empiric and targeted antimicrobial therapy for healthcare-associated infections (HAIs) was developed and incorporated into a commercial electronic health record (EHR) in two NICUs. User logs were reviewed and NICU prescribers were surveyed for their perceptions of the CDS tool.Results: The CDS tool aggregated selected laboratory results, including culture results, to make treatment recommendations for common clinical scenarios. From July 2010 to May 2012, 1,303 CDS activations for 452 patients occurred representing 22% of patients prescribed antibiotics during this period. While NICU clinicians viewed two culture results per tool activation, prescribing recommendations were viewed during only 15% of activations. Most (63%) survey respondents were aware of the CDS tool, but fewer (37%) used it during their most recent NICU rotation. Respondents considered the most useful features to be summarized culture results (43%) and antibiotic recommendations (48%).Discussion: During the study period, the CDS tool functionality was hindered by EHR upgrades, implementation of a new laboratory information system, and changes to antimicrobial testing methodologies. Loss of functionality may have reduced viewing antibiotic recommendations. In contrast, viewing culture results was frequently performed, likely because this feature was perceived as useful and functionality was preserved.Conclusion: To improve CDS tool visibility and usefulness, we recommend early user and information technology team involvement which would facilitate use and mitigate implementation challenges.Citation: Hum RS, Cato K, Sheehan B, Patel S, Duchon J, DeLaMora P, Ferng YH, Graham P, Vawdrey DK, Perlman J, Larson E, Saiman L. Developing clinical decision support within a commercial electronic health record system to improve antimicrobial prescribing in the neonatal ICU. Appl Clin Inf 2014; 5: 368–387 http://dx.doi.org/10.4338/ACI-2013-09-RA-0069

scholarly journals Integrating pharmacogenetic information and clinical decision support into the electronic health record

2014 ◽  
Vol 21 (3) ◽  
pp. 522-528 ◽  
Author(s):  
Barry R Goldspiel ◽  
Willy A Flegel ◽  
Gary DiPatrizio ◽  
Tristan Sissung ◽  
Sharon D Adams ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Electronic Health

scholarly journals Clinical decision support alert malfunctions: analysis and empirically derived taxonomy

2017 ◽  
Vol 25 (5) ◽  
pp. 496-506 ◽  
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.

Monitoring clinical decision support in the electronic health record

2017 ◽  
Vol 74 (15) ◽  
pp. 1130-1133 ◽  
Author(s):  
Jason H. Lam ◽  
Olivia Ng
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Electronic Health

scholarly journals Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation

2019 ◽  
Vol 28 (9) ◽  
pp. 762-768 ◽  
Author(s):  
Norman Lance Downing ◽  
Joshua Rolnick ◽  
Sarah F Poole ◽  
Evan Hall ◽  
Alexander J Wessels ◽  
...  
Keyword(s):  
Severe Sepsis ◽  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Tertiary Care ◽  
Clinical Decision ◽  
Health Record ◽  
Hospitalised Patients ◽  
Significant Difference ◽  
Electronic Health

BackgroundSepsis remains the top cause of morbidity and mortality of hospitalised patients despite concerted efforts. Clinical decision support for sepsis has shown mixed results reflecting heterogeneous populations, methodologies and interventions.ObjectivesTo determine whether the addition of a real-time electronic health record (EHR)-based clinical decision support alert improves adherence to treatment guidelines and clinical outcomes in hospitalised patients with suspected severe sepsis.DesignPatient-level randomisation, single blinded.SettingMedical and surgical inpatient units of an academic, tertiary care medical centre.Patients1123 adults over the age of 18 admitted to inpatient wards (intensive care units (ICU) excluded) at an academic teaching hospital between November 2014 and March 2015.InterventionsPatients were randomised to either usual care or the addition of an EHR-generated alert in response to a set of modified severe sepsis criteria that included vital signs, laboratory values and physician orders.Measurements and main resultsThere was no significant difference between the intervention and control groups in primary outcome of the percentage of patients with new antibiotic orders at 3 hours after the alert (35% vs 37%, p=0.53). There was no difference in secondary outcomes of in-hospital mortality at 30 days, length of stay greater than 72 hours, rate of transfer to ICU within 48 hours of alert, or proportion of patients receiving at least 30 mL/kg of intravenous fluids.ConclusionsAn EHR-based severe sepsis alert did not result in a statistically significant improvement in several sepsis treatment performance measures.

Implementing Electronic Health Record–Based Clinical Decision Support for Head CT Ordering in Minor Pediatric Head Trauma

2020 ◽  
Vol 17 (12) ◽  
pp. 1571-1574
Author(s):  
Joel Y. Sun ◽  
Ryan K. Lee ◽  
Neena S. David ◽  
Bryan J. Kang ◽  
Terence Matalon
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Head Trauma ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Head Ct ◽  
Pediatric Head Trauma ◽  
Electronic Health

scholarly journals The Use of Evidence-Based, Problem-Oriented Templates as a Clinical Decision Support in an Inpatient Electronic Health Record System

2016 ◽  
Vol 07 (03) ◽  
pp. 790-802 ◽  
Author(s):  
Nila Radhakrishnan ◽  
Carrie Warring ◽  
Ankur Jain ◽  
Jorge Fuentes ◽  
Angela Dolganiuc ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Outcome Measurement ◽  
Clinical Decision ◽  
Electronic Health Record System ◽  
Evidence Based ◽  
Health Record ◽  
Record System ◽  
Electronic Health

SummaryThe integration of clinical decision support (CDS) in documentation practices remains limited due to obstacles in provider workflows and design restrictions in electronic health records (EHRs). The use of electronic problem-oriented templates (POTs) as a CDS has been previously discussed but not widely studied.We evaluated the voluntary use of evidence-based POTs as a CDS on documentation practices.This was a randomized cohort (before and after) study of Hospitalist Attendings in an Academic Medical Center using EPIC EHRs. Primary Outcome measurement was note quality, assessed by the 9-item Physician Documentation Quality Instrument (PDQI-9). Secondary Outcome measurement was physician efficiency, assessed by the total charting time per note.Use of POTs increased the quality of note documentation [score 37.5 vs. 39.0, P = 0.0020]. The benefits of POTs scaled with use; the greatest improvement in note quality was found in notes using three or more POTs [score 40.2, P = 0.0262]. There was no significant difference in total charting time [30 minutes vs. 27 minutes, P = 0.42].Use of evidence-based and problem-oriented templates is associated with improved note quality without significant change in total charting time. It can be used as an effective CDS during note documentation. Citation: Mehta R, Radhakrishnan NS, Warring CD, Jain A, Fuentes J, Dolganiuc A, Lourdes LS, Busigin J, Leverence RR. The use of evidence-based, problemoriented templates as a clinical decision support in an inpatient electronic health record system.

Sign in / Sign up

Export Citation Format

Share Document