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.

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 SNOMED CT Concept Hierarchies for Sharing Definitions of Clinical Conditions Using Electronic Health Record Data

2018 ◽  
Vol 09 (03) ◽  
pp. 667-682 ◽  
Author(s):  
Duwayne Willett ◽  
Vaishnavi Kannan ◽  
Ling Chu ◽  
Joel Buchanan ◽  
Ferdinand Velasco ◽  
...  
Keyword(s):  
Decision Support ◽  
Population Health ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Snomed Ct ◽  
Patient Registries ◽  
Value Sets ◽  
Clinical Conditions ◽  
Concept Hierarchies

Background Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or “grouper.” For constructing value sets, Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems. Objective This article demonstrates a practical approach to defining conditions with combinations of SNOMED CT concept hierarchies, and evaluates sharing of definitions for clinical and analytic uses. Methods We constructed diagnosis value sets for EHR patient registries using SNOMED CT concept hierarchies combined with Boolean logic, and shared them for clinical decision support, reporting, and analytic purposes. Results A total of 125 condition-defining “standard” SNOMED CT diagnosis value sets were created within our EHR. The median number of SNOMED CT concept hierarchies needed was only 2 (25th–75th percentiles: 1–5). Each value set, when compiled as an EHR diagnosis grouper, was associated with a median of 22 International Classification of Diseases (ICD)-9 and ICD-10 codes (25th–75th percentiles: 8–85) and yielded a median of 155 clinical terms available for selection by clinicians in the EHR (25th–75th percentiles: 63–976). Sharing of standard groupers for population health, clinical decision support, and analytic uses was high, including 57 patient registries (with 362 uses of standard groupers), 132 clinical decision support records, 190 rules, 124 EHR reports, 125 diagnosis dimension slicers for self-service analytics, and 111 clinical quality measure calculations. Identical SNOMED CT definitions were created in an EHR-agnostic tool enabling application across disparate organizations and EHRs. Conclusion SNOMED CT-based diagnosis value sets are simple to develop, concise, understandable to clinicians, useful in the EHR and for analytics, and shareable. Developing curated SNOMED CT hierarchy-based condition definitions for public use could accelerate cross-organizational population health efforts, “smarter” EHR feature configuration, and clinical–translational research employing EHR-derived data.

scholarly journals Differences, Opportunities, and Strategies in Drug Alert Optimization—Experiences of Two Different Integrated Health Care Systems

2019 ◽  
Vol 10 (05) ◽  
pp. 777-782 ◽  
Author(s):  
Salim M. Saiyed ◽  
Katherine R. Davis ◽  
David C. Kaelber
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Systematic Approach ◽  
Drug Information ◽  
Health Care Systems ◽  
Clinical Decision ◽  
Health Record ◽  
Alarm Fatigue ◽  
Care Systems ◽  
Guidelines And Recommendations

Abstract Background Concerns about the number of automated medication alerts issued within the electronic health record (EHR), and the subsequent potential for alarm fatigue, led us to examine strategies and methods to optimize the configuration of our drug alerts. Objectives This article reports on comprehensive drug alerting rates and develops strategies across two different health care systems to reduce the number of drug alerts. Methods Standardized reports compared drug alert rates between the two systems, among 13 categories of drug alerts. Both health care systems made modifications to the out-of-box alerts available from their EHR and drug information vendors, focusing on system-wide approaches, when relevant, while performing more drug-specific changes when necessary. Results Drug alerting rates even after initial optimization were 38 alerts and 51 alerts per 100 drug orders, respectively. Eight principles were identified and developed to reflect the themes in the implementation and optimization of drug alerting. Conclusion A team-based, systematic approach to optimizing drug-alerting strategies can reduce the number of drug alerts, but alert rates still remain high. In addition to strategic principles, additional tactical guidelines and recommendations need to be developed to enhance out-of-the-box clinical decision support for drug alerts.

scholarly journals Measuring implementation feasibility of clinical decision support alerts for clinical practice recommendations

2020 ◽  
Vol 27 (4) ◽  
pp. 514-521 ◽  
Author(s):  
Rachel L Richesson ◽  
Catherine J Staes ◽  
Brian J Douthit ◽  
Traci Thoureen ◽  
Daniel J Hatch ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Data Collection ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Additional Data ◽  
Clinical Decision ◽  
Health Record ◽  
Practice Recommendations ◽  
Additional Data Collection

Abstract Objective The study sought to describe key features of clinical concepts and data required to implement clinical practice recommendations as clinical decision support (CDS) tools in electronic health record systems and to identify recommendation features that predict feasibility of implementation. Materials and Methods Using semistructured interviews, CDS implementers and clinician subject matter experts from 7 academic medical centers rated the feasibility of implementing 10 American College of Emergency Physicians Choosing Wisely Recommendations as electronic health record–embedded CDS and estimated the need for additional data collection. Ratings were combined with objective features of the guidelines to develop a predictive model for technical implementation feasibility. Results A linear mixed model showed that the need for new data collection was predictive of lower implementation feasibility. The number of clinical concepts in each recommendation, need for historical data, and ambiguity of clinical concepts were not predictive of implementation feasibility. Conclusions The availability of data and need for additional data collection are essential to assess the feasibility of CDS implementation. Authors of practice recommendations and guidelines can enable organizations to more rapidly assess data availability and feasibility of implementation by including operational definitions for required data.

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 Facilitating Genetics Aware Clinical Decision Support: Putting the eMERGE Infrastructure into Practice

ACI Open ◽  
2021 ◽  
Vol 05 (02) ◽  
pp. e54-e58
Author(s):  
Casey Overby Taylor ◽  
Luke V. Rasmussen ◽  
Laura J. Rasmussen-Torvik ◽  
Cynthia A. Prows ◽  
David A. Dorr ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Medical Records ◽  
Clinical Decision Support ◽  
Medical Records ◽  
Case Reports ◽  
Clinical Decision ◽  
Phase Iii ◽  
Health Record ◽  
It Infrastructure ◽  
Emerge Network

AbstractThis editorial provides context for a series of published case reports in ACI Open by summarizing activities and outputs of joint electronic health record integration and pharmacogenomics workgroups in the NIH-funded electronic Medical Records and Genomics (eMERGE) Network. A case report is a useful tool to describe the range of capabilities that an IT infrastructure or a particular technology must support. The activities we describe have informed infrastructure requirements used during eMERGE phase III, provided a venue to share experiences and ask questions among other eMERGE sites, summarized potential hazards that might be encountered for specific clinical decision support (CDS) implementation scenarios, and provided a simple framework that captured progress toward implementing CDS at eMERGE sites in a consistent format.

scholarly journals Reduced Length of Stay Using Clinical Decision Support Tool (ASAP) for Empiric Antibiotic Selection

2020 ◽  
Vol 41 (S1) ◽  
pp. s368-s368
Author(s):  
Mary Acree ◽  
Kamaljit Singh ◽  
Urmila Ravichandran ◽  
Jennifer Grant ◽  
Gary Fleming ◽  
...  
Keyword(s):  
Decision Support ◽  
Length Of Stay ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Health Record ◽  
Antibiotic Selection ◽  
Support Tool ◽  
Empiric Antibiotic

Background: Empiric antibiotic selection is challenging and requires knowledge of the local antibiogram, national guidelines and patient-specific factors, such as drug allergy and recent antibiotic exposure. Clinical decision support for empiric antibiotic selection has the potential to improve adherence to guidelines and improve patient outcomes. Methods: At NorthShore University HealthSystem, a 4-hospital, 789 bed system, an automated point-of-care decision support tool referred to as Antimicrobial Stewardship Assistance Program (ASAP) was created for empiric antibiotic selection for 4 infectious syndromes: pneumonia, skin and soft-tissue infections, urinary tract infection, and intra-abdominal infection. The tool input data from the electronic health record, which can be modified by any user. Using an algorithm created with electronic health record data, antibiogram data, and national guidelines, the tool produces an antibiotic recommendation that can be ordered via a link to order entry. If the tool identifies a patient with a high likelihood for a multidrug-resistant infection, a consultation by an infectious diseases specialist is recommended. Utilization of the tool and associated outcomes were evaluated from July 2018 to May 2019. Results: The ASAP tool was executed by 140 unique, noninfectious diseases providers 790 times. The tool was utilized most often for pneumonia (194 tool uses), followed by urinary tract infection (166 tool uses). The most common provider type to use the tool was an internal medicine hospitalist. The tool increased adherence to the recommended antibiotic regimen for each condition. Antibiotic appropriateness was assessed by an infectious diseases physician. Antibiotics were considered appropriate when they were similar to the antibiotic regimen recommended by the ASAP. Inappropriate antibiotics were classified as broad or narrow. When antibiotic coverage was appropriate, hospital length of stay was statistically significantly shorter (4.8 days vs 6.8 days for broad antibiotics vs 7.4 days for narrow antibiotics; P < .01). No significant differences were identified in mortality or readmission. Conclusions: A clinical decision support tool in the electronic health record can improve adherence to recommended empiric antibiotic therapy. Use of appropriate antibiotics recommended by such a tool can reduce hospital length of stay.Funding: NoneDisclosures: None

Integrating Qualitative and Quantitative Survey Techniques

1986 ◽  
Vol 7 (3) ◽  
pp. 181-200 ◽  
Author(s):  
Deborah C. Glik ◽  
Kathleen Parker ◽  
Gabriel Muligande ◽  
Bona Hategikamana
Keyword(s):  
Health Care ◽  
Data Collection ◽  
Small Samples ◽  
Care Utilization ◽  
Medication Usage ◽  
Qualitative And Quantitative ◽  
Survey Techniques ◽  
Degree Of Confidence ◽  
Qualitative Technique ◽  
Group Interviewing

An “educational diagnosis,” which targetted the health behaviors of mothers with regards to malaria and diarrhea in their young children, was carried out over a three-week period in November 1985 in Rwanda, East Africa. The study used the methodological technique of triangulation, in which both quantitative and qualititative data collection efforts are used to find answers to the same or similar sets of questions. Findings include estimates of health care utilization and medication usage and illustrate how multiple methods of data collection can maximize both the amount of usable data and the degree of confidence in the validity of data obtained, even with relatively small samples. In particular, the qualitative technique of focus group interviewing, which provided information on the sociocultural, economic, and historical contexts of the Rwandese health care system, provided an explanation of why there is relatively little self-care or home treatment for these diseases in Rwandese society.

scholarly journals Association Patterns of Ontological Features Signify Electronic Health Records in Liver Cancer

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Lawrence W. C. Chan ◽  
S. C. Cesar Wong ◽  
Choo Chiap Chiau ◽  
Tak-Ming Chan ◽  
Liang Tao ◽  
...  
Keyword(s):  
Decision Support ◽  
Liver Cancer ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Computed Tomography Imaging ◽  
Health Records ◽  
Association Pattern ◽  
Electronic Health ◽  
Systematized Nomenclature Of Medicine ◽  
Primary Predictor

Electronic Health Record (EHR) system enables clinical decision support. In this study, a set of 112 abdominal computed tomography imaging examination reports, consisting of 59 cases of hepatocellular carcinoma (HCC) or liver metastases (so-called HCC group for simplicity) and 53 cases with no abnormality detected (NAD group), were collected from four hospitals in Hong Kong. We extracted terms related to liver cancer from the reports and mapped them to ontological features using Systematized Nomenclature of Medicine (SNOMED) Clinical Terms (CT). The primary predictor panel was formed by these ontological features. Association levels between every two features in the HCC and NAD groups were quantified using Pearson’s correlation coefficient. The HCC group reveals a distinct association pattern that signifies liver cancer and provides clinical decision support for suspected cases, motivating the inclusion of new features to form the augmented predictor panel. Logistic regression analysis with stepwise forward procedure was applied to the primary and augmented predictor sets, respectively. The obtained model with the new features attained 84.7% sensitivity and 88.4% overall accuracy in distinguishing HCC from NAD cases, which were significantly improved when compared with that without the new features.

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

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