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Implementation of a Fast Healthcare Interoperability Resources–Based Clinical Decision Support Tool for Calculating CHA 2 DS 2 -VASc Scores

2020; Lippincott Williams & Wilkins; Volume: 13; Issue: 2 Linguagem: Inglês

10.1161/circoutcomes.119.006286

1941-7705

Zameer Abedin, Robert M. Hoerner, Joseph Habboushe, Yi Lu, Kensaku Kawamoto, Phillip B. Warner, David Shields, Rashmee U. Shah,

ECG Monitoring and Analysis

HomeCirculation: Cardiovascular Quality and OutcomesVol. 13, No. 2Implementation of a Fast Healthcare Interoperability Resources–Based Clinical Decision Support Tool for Calculating CHA2DS2-VASc Scores Free AccessLetterPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessLetterPDF/EPUBImplementation of a Fast Healthcare Interoperability Resources–Based Clinical Decision Support Tool for Calculating CHA2DS2-VASc Scores Zameer Abedin, MD, MS, Robert Hoerner, MD, Joseph Habboushe, MD MBA, Yi Lu, MS, Kensaku Kawamoto, MD, PhD, MHS, Phillip B. Warner, MS, David E. Shields, BA and Rashmee U. Shah, MD, MS Zameer AbedinZameer Abedin Zameer Abedin, MD, University of Utah School of Medicine, Department of Internal Medicine, 30 N 1900 E, Room 4A100, Salt Lake City, UT 84132. Email E-mail Address: [email protected] Department of Internal Medicine (Z.A., R.H.) , Robert HoernerRobert Hoerner Department of Internal Medicine (Z.A., R.H.) , Joseph HabbousheJoseph Habboushe MDAware LLC (d/b/a MDCalc) (J.H., Y.L.). , Yi LuYi Lu MDAware LLC (d/b/a MDCalc) (J.H., Y.L.). , Kensaku KawamotoKensaku Kawamoto Department of Biomedical Informatics (K.K., P.B.W., D.E.S.), University of Utah. , Phillip B. WarnerPhillip B. Warner Department of Biomedical Informatics (K.K., P.B.W., D.E.S.), University of Utah. , David E. ShieldsDavid E. Shields Department of Biomedical Informatics (K.K., P.B.W., D.E.S.), University of Utah. and Rashmee U. ShahRashmee U. Shah Department of Internal Medicine, Division of Cardiovascular Medicine (R.U.S) Originally published6 Feb 2020https://doi.org/10.1161/CIRCOUTCOMES.119.006286Circulation: Cardiovascular Quality and Outcomes. 2020;13:e006286The CHA2DS2-VASc score guides oral anticoagulant treatment decisions in atrial fibrillation but requires that clinicians gather information on the components of the score, including age, sex, and 5 clinical conditions.1,2 Thorough review of a patient's entire medical history and rote transfer of data to a calculator can be onerous and time-consuming. Here, we discuss the use of Substitutable Medical Applications, Reusable Technologies on the Fast Healthcare Interoperability Resources (SMART on FHIR) to address this workflow bottleneck. SMART on FHIR is a platform that allows secure information exchange between electronic medical records and third-party applications (apps) using a standardized data exchange format. The goal is to encourage innovative, clinically useful, electronic medical record-based tools. Our goal was to validate an implementation of the MDCalc app that uses electronic medical record data to calculate a CHA2DS2-VASc score at the point of care and identify factors that affect app accuracy in clinical practice. This quality improvement project did not fall under the definition of research under 45 Code of Federal Regulations part 46 and therefore did not require Institutional Review Board review.We prospectively compared automated app scores with clinician scores by identifying consecutive, adult outpatients with a primary visit diagnosis of atrial fibrillation seen at the University of Utah cardiology clinics over a 1-month period. Within 24 hours of the clinic visit, one of 2 reviewers used the β version of MDCalc on FHIR (MoF) app to automatically calculate a CHA2DS2-VASc score. The MoF app auto-fills the score as a first step, then allows the clinician to change inputs if needed. We evaluated the first step, automatic calculation before any adjustments. For comparison, the reviewer simultaneously identified whether a patient was prescribed an anticoagulant (suggesting a score ≥2) and captured CHA2DS2-VASc scores documented by clinicians in the visit note.We identified 200 consecutive atrial fibrillation patients seen between November 5, 2018 and December 7, 2018 111 of whom had a documented CHA2DS2-VASc score. The mean MoF app score was 3.79 (SD 1.86) compared to a mean clinician score of 3.25 (SD 1.63, P=0.02). If the MoF app were used instead of the documented score, 13.5% (n=27) of patients would move into or out of the high-risk group (defined as CHA2DS2-VASc score ≥2). Ten percent (n=19) of patients were up-classified by the MoF app, meaning they were considered high risk by the app and low risk by the clinician. Four percent (n=8) of patients were down-classified by the app. Upon review of these cases, we accounted for documented clinical decisions which lead to patients either being anticoagulated or not regardless of their stroke risk (eg, patient preference, bleeding, or recent cardioversion). After accounting for these clinical decisions, we found that 3% (n=5) of patients were up-classified by the app, and 2% (n=3) of patients were down-classified by the app, making the adjusted net reclassification index 4% (n=8).Among the 111 patients who had a documented clinician score, the exact scores differed in 61% (n=68) of the cases. We identified condition-specific discrepancies for the 60 patients in whom the clinician documented specific components. Overall, we found 70 condition-specific differences (Table); heart failure (n=25) and hypertension (n=10) were the most common discrepancies. The app captured a condition that was not noted by the clinician in 57 cases; the clinician noted a condition that was not captured by the app in 13 cases. Two themes emerged. First, the MoF app identified conditions because specific medications were incorrectly attributed to conditions. For example, a prescription for losartan would lead the app to classify the patient as hypertension present when that medication may have been used solely for heart failure. Second, the problem list (which is used to generate the MoF score) included conditions that the clinician did not count, or the clinician noted a condition that was not in the problem list (eg, new patients whose charts had not yet been populated problem list). Notably, the autofill design of the app was intentionally made to be more sensitive than specific, so it would present all potential information to the clinician for consideration.Table. Condition-Specific Discrepancies in the CHA2DS2-VASc Score for 60 Patients Using an MDCalc on FHIR App and Clinician DocumentationConditionCaptured by App, Not Noted by ClinicianCaptured by Clinician, Not Noted by AppHeart failure251Hypertension102Stroke64Vascular115Diabetes mellitus51Total5713App indicates application; and FHIR, Fast Healthcare Interoperability Resources.*Please note, a single patient may have discrepancies for >1 condition. The table includes 70 condition discrepancies for 60 patients.Apps and other forms of clinical decision support are increasingly prevalent in healthcare delivery and regulatory guidance is a work in progress.3 Prior studies have found little impact on treatment rates. Chaturvedi et al4 found that using automated electronic decision support would have theoretically increased anticoagulation rates by 15%, but similar rates of anticoagulation resulted when put into practice. Further, evidence from the implementation of similar apps for other conditions has shown modest time saving and excellent usability.5 The apps could reduce time for data aggregation, which is a measurable end point. Still, the current tools require a human in the loop for validation and clinician judgment. As usage increases, healthcare systems need to be mindful of downstream effects on treatment rates and patient outcomes.Sources of FundingDr Shah is supported by the National Heart, Lung, and Blood Institute (K08 HL136850).DisclosuresY. Lu is an employee of MDAware. Dr Habboushe is cofounder of MDCalc. Dr Kawamoto, P.B. Warner, and D.E. Shields assisted with development of electronic medical record (EMR)-integrated MDCalc and may benefit financially if it is commercially successful. The other authors report no conflicts.FootnotesData availability: Given the small sample size and specified date range, deidentification of this data would be challenging. As such, data will not be made available from this study in the interest of preserving patient privacy and confidentiality.Zameer Abedin, MD, University of Utah School of Medicine, Department of Internal Medicine, 30 N 1900 E, Room 4A100, Salt Lake City, UT 84132. Email zameer.[email protected]utah.eduReferences1. Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation.Chest. 2010; 137:263–272. doi: 10.1378/chest.09-1584CrossrefMedlineGoogle Scholar2. January CT, Wann LS, Alpert JS, Calkins H, Cigarroa JE, Cleveland JC, Conti JB, Ellinor PT, Ezekowitz MD, Field ME, Murray KT, Sacco RL, Stevenson WG, Tchou PJ, Tracy CM, Yancy CW; ACC/AHA Task Force Members. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society.Circulation. 2014; 130:e199–e267. doi: 10.1161/CIR.0000000000000041LinkGoogle Scholar3. Center for Devices, Radiological Health. Clinical Decision Support Software - Draft Guidance. U.S. Food and Drug Administration. 2019. http://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software. Accessed October 2, 2019.Google Scholar4. Chaturvedi S, Kelly AG, Prabhakaran S, Saposnik G, Lee L, Malik A, Boerman C, Serlin G, Mantero AM. Electronic decision support for improvement of contemporary therapy for stroke prevention.J Stroke Cerebrovasc Dis. 2019; 28:569–573. doi: 10.1016/j.jstrokecerebrovasdis.2018.10.041CrossrefMedlineGoogle Scholar5. Kawamoto K, Kukhareva P, Shakib JH, Kramer H, Rodriguez S, Warner PB, Shields D, Weir C, Del Fiol G, Taft T, Stipelman CH. Association of an electronic health record add-on app for neonatal bilirubin management with physician efficiency and care quality.JAMA Netw Open. 2019; 2:e1915343. doi: 10.1001/jamanetworkopen.2019.15343CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Bradshaw R, Kawamoto K, Kaphingst K, Kohlmann W, Hess R, Flynn M, Nanjo C, Warner P, Shi J, Morgan K, Kimball K, Ranade-Kharkar P, Ginsburg O, Goodman M, Chambers R, Mann D, Narus S, Gonzalez J, Loomis S, Chan P, Monahan R, Borsato E, Shields D, Martin D, Kessler C and Del Fiol G (2022) GARDE: a standards-based clinical decision support platform for identifying population health management cohorts, Journal of the American Medical Informatics Association, 10.1093/jamia/ocac028, 29:5, (928-936), Online publication date: 13-Apr-2022. Shah R, Bress A and Vickers A (2022) Do Prediction Models Do More Harm Than Good?, Circulation: Cardiovascular Quality and Outcomes, 15:4, (e008667), Online publication date: 1-Apr-2022. Kukhareva P, Weir C, Del Fiol G, Aarons G, Taft T, Schlechter C, Reese T, Curran R, Nanjo C, Borbolla D, Staes C, Morgan K, Kramer H, Stipelman C, Shakib J, Flynn M and Kawamoto K (2022) Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation, Journal of Biomedical Informatics, 10.1016/j.jbi.2022.104014, 127, (104014), Online publication date: 1-Mar-2022. Braunstein M (2022) Health Informatics in the Real World Health Informatics on FHIR: How HL7's API is Transforming Healthcare, 10.1007/978-3-030-91563-6_3, (33-68), . Taber P, Radloff C, Del Fiol G, Staes C and Kawamoto K (2021) New Standards for Clinical Decision Support: A Survey of The State of Implementation, Yearbook of Medical Informatics, 10.1055/s-0041-1726502, 30:01, (159-171), Online publication date: 1-Aug-2021. Kawamoto K, Kukhareva P, Weir C, Flynn M, Nanjo C, Martin D, Warner P, Shields D, Rodriguez-Loya S, Bradshaw R, Cornia R, Reese T, Kramer H, Taft T, Curran R, Morgan K, Borbolla D, Hightower M, Turnbull W, Strong M, Chapman W, Gregory T, Stipelman C, Shakib J, Hess R, Boltax J, Habboushe J, Sakaguchi F, Turner K, Narus S, Tarumi S, Takeuchi W, Ban H, Wetter D, Lam C, Caverly T, Fagerlin A, Norlin C, Malone D, Kaphingst K, Kohlmann W, Brooke B and Del Fiol G (2021) Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center, JAMIA Open, 10.1093/jamiaopen/ooab041, 4:3, Online publication date: 31-Jul-2021. February 2020Vol 13, Issue 2 Advertisement Article InformationMetrics © 2020 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.119.006286PMID: 32069091 Originally publishedFebruary 6, 2020 Keywordselectronic health recordsatrial fibrillationclinical decision supportPDF download Advertisement SubjectsAtrial FibrillationDigital HealthQuality and OutcomesRisk Factors