Electronic Health Record Integration of Predictive Analytics to Select High-Risk Stable Patients With Non–ST-Segment–Elevation Myocardial Infarction for Intensive Care Unit Admission

HomeCirculation: Cardiovascular Quality and OutcomesVol. 14, No. 4Electronic Health Record Integration of Predictive Analytics to Select High-Risk Stable Patients With Non–ST-Segment–Elevation Myocardial Infarction for Intensive Care Unit Admission Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toSupplementary MaterialsFree AccessResearch ArticlePDF/EPUBElectronic Health Record Integration of Predictive Analytics to Select High-Risk Stable Patients With Non–ST-Segment–Elevation Myocardial Infarction for Intensive Care Unit Admission Aman Kansal, MD, Cynthia L. Green, PhD, Eric D. Peterson, MD, MPH, L. Kristin Newby, MD, Tracy Y. Wang, MD, MHS, MSc, Mark Sendak, MD, MPP, Suresh Balu, MS, MBA, Manesh R. Patel, MD and Alexander C. Fanaroff, MD, MHS Aman KansalAman Kansal https://orcid.org/0000-0002-6031-463X Department of Medicine, Duke University, Durham, NC (A.K., E.D.P., L.K.N., T.Y.W., M.R.P.). , Cynthia L. GreenCynthia L. Green Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC (C.L.G.). Duke Clinical Research Institute, Durham, NC (C.L.G., E.D.P., L.K.N., T.Y.W., M.R.P.). , Eric D. PetersonEric D. Peterson https://orcid.org/0000-0002-5415-4721 Department of Medicine, Duke University, Durham, NC (A.K., E.D.P., L.K.N., T.Y.W., M.R.P.). Duke Clinical Research Institute, Durham, NC (C.L.G., E.D.P., L.K.N., T.Y.W., M.R.P.). , L. Kristin NewbyL. Kristin Newby https://orcid.org/0000-0002-6394-8187 Department of Medicine, Duke University, Durham, NC (A.K., E.D.P., L.K.N., T.Y.W., M.R.P.). Duke Clinical Research Institute, Durham, NC (C.L.G., E.D.P., L.K.N., T.Y.W., M.R.P.). , Tracy Y. WangTracy Y. Wang Department of Medicine, Duke University, Durham, NC (A.K., E.D.P., L.K.N., T.Y.W., M.R.P.). Duke Clinical Research Institute, Durham, NC (C.L.G., E.D.P., L.K.N., T.Y.W., M.R.P.). , Mark SendakMark Sendak https://orcid.org/0000-0001-5828-4497 Duke Institute for Health Innovation, Durham, NC (M.S., S.B.). , Suresh BaluSuresh Balu https://orcid.org/0000-0003-4929-9130 Duke Institute for Health Innovation, Durham, NC (M.S., S.B.). , Manesh R. PatelManesh R. Patel Department of Medicine, Duke University, Durham, NC (A.K., E.D.P., L.K.N., T.Y.W., M.R.P.). Duke Clinical Research Institute, Durham, NC (C.L.G., E.D.P., L.K.N., T.Y.W., M.R.P.). and Alexander C. FanaroffAlexander C. Fanaroff Alexander C. Fanaroff, MD, MHS, Perelman Center for Advanced Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104. Email E-mail Address: [email protected] https://orcid.org/0000-0002-9060-5307 Cardiovascular Medicine Division, Penn Cardiovascular Outcomes, Quality and Evaluative Research Center, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia (A.C.F.). Originally published24 Mar 2021https://doi.org/10.1161/CIRCOUTCOMES.120.007602Circulation: Cardiovascular Quality and Outcomes. 2021;14:e007602This article is commented on by the following:Old Habits Die HardOther version(s) of this articleYou are viewing the most recent version of this article. Previous versions: March 24, 2021: Ahead of Print See Editorial by Thompson and ValleyGoals and Vision of the ProgramIn the United States, ≈40% of patients with non–ST-segment–elevation myocardial infarction (NSTEMI) who initially present without cardiogenic shock or cardiac arrest are admitted to the intensive care unit (ICU).1,2 Importantly, ICU utilization for these initially stable NSTEMI patients varies substantially between hospitals, and severity of illness on presentation is similar for NSTEMI patients treated and not treated in the ICU, suggesting that ICU admission decisions are largely based on hospital policies and local clinician preferences.1Risk-based ICU utilization—admitting those at the highest risk of developing complications requiring ICU care to the ICU and admitting those at lower risk to a non-ICU setting—has the potential to better align resource use with patient needs. For patients with initially stable NSTEMI, the Acute Coronary Treatment and Intervention Outcomes Network (ACTION) ICU risk score uses data available at the time of hospital presentation to predict risk of clinical deterioration requiring ICU care—in-hospital death, cardiac arrest, shock, respiratory failure, heart block requiring pacemaker placement, and stroke.3 Risk scores are infrequently used in clinical practice,4 but directly embedding a risk score calculator with associated decision support into the electronic health record (EHR) could increase uptake. The goal of this innovation was to achieve risk-based ICU utilization for patients with NSTEMI presenting to the emergency department (ED) by embedding the ACTION ICU risk score into the EHR as a modified best practice advisory (BPA).Local Challenges in ImplementationMajor challenges in implementation included lack of clinician familiarity with the BPA interface, placement of the risk calculator within ED clinicians' existing workflow, and finding consensus on the appropriate threshold for ICU admission.Although NSTEMI is a relatively common diagnosis, a large number of clinicians see patients in the ED at tertiary academic medical centers, including attending physicians, advanced practice providers, fellows, and residents from multiple specialties. Any one clinician, therefore, sees patients with NSTEMI infrequently and is likely to be unfamiliar with the interface of an NSTEMI risk prediction calculator. Successful deployment of predictive analytics at the point of care requires that the tool overcome clinicians' unfamiliarity by integrating into existing workflows. Since the risk calculator and decision support tool was intended for use by ED clinicians, we designed the tool in consultation with a group of ED clinicians, with a goal of understanding how a new tool could be best incorporated into their workflow. ED clinicians said that they often opened patients' charts before seeing them, so a BPA that forced them to answer questions about the patient before closing the chart would not fit into their workflow. The BPA was thus designed such that when it triggered, clinicians were given a chance to hide the BPA and have it reappear the next time they opened the patient's chart (if they had not yet seen the patient), cancel it (if the patient was not hemodynamically stable or did not have an NSTEMI), or calculate the ACTION ICU risk score. Before BPA rollout, ED clinicians were given a brief presentation and 1-page handout about the rationale for risk-based ICU admission, the ACTION ICU risk score, and how the BPA was designed to work.We also sought consensus on the threshold ACTION ICU risk score value for ICU admission through a series of conversations with clinical leadership in the division of cardiology, ultimately selecting a risk score of 5. In the ACTION ICU risk score development cohort, ≈50% of patients had a score ≤5. These patients had a <10% risk of developing complications requiring ICU care (defined as death, respiratory failure, cardiac arrest, shock, heart block requiring pacemaker, or stroke).3 Because the ACTION ICU risk score may be ≤5 in a patient with an alternate indication for ICU care, such as hypoxia or ongoing chest pain, cardiologists were concerned that strict adherence to risk-based ICU admission could lead to harm. As such, the decision support tool specifically recommended non-ICU care for patients with a score ≤5 and no other compelling indication for ICU care, if congruent with clinical assessment; other compelling indications for ICU care were listed alongside the score. Patients with a score >5 were recommended for ICU admission.Design and Implementation of the InitiativeWorking with information technology specialists at our institution, we determined that a modified BPA was the most feasible mechanism for embedding a risk score calculator with integrated decision support into the EHR (Figure I in the Data Supplement). To maximize sensitivity, we chose a single positive troponin above the upper limit of normal for a patient currently housed in the ED as the trigger for the modified BPA to appear. When the BPA was triggered and clinicians selected the option to calculate the risk score, the risk score calculator appeared on the right side of the screen with access to the remainder of the chart preserved to enable the clinician to search for other relevant information. Of the 9 elements comprising the ACTION ICU score, 5 were captured as discrete data elements within the EHR (age, systolic blood pressure on presentation, heart rate on presentation, initial serum creatinine, and initial serum troponin) and were automatically entered into the risk score calculator and 4 were not (ST-segment depression or transient elevation on ECG, signs or symptoms of heart failure on presentation, prior revascularization, and chronic lung disease). The 4 elements not captured as discrete data elements had to be entered by clinicians using yes/no radio buttons. Once clinicians answered the 4 required yes/no questions, the score was calculated and displayed to the clinician, along with a recommendation for location of admission (ICU versus non-ICU).The BPA was integrated into the EHR on August 14, 2017. From implementation until 12 months later (August 13, 2018), it triggered on 972 patients in the ED with a serum troponin value above the upper limit of normal, an average of 81 patients per month, or 2.7 patients per day. Possible ED clinician responses to the BPA included repeatedly hiding the BPA until the patient left the ED and the BPA stopped appearing, canceling the BPA as inappropriate (if the patient was hemodynamically stable or did not have an NSTEMI), selecting the option to calculate a risk score but not actually completing the prompts to calculate the score (in which case the BPA did not reappear), and calculating a risk score. Among the 972 patients, the BPA was hidden until the patient left the ED in 561 (57.7%), canceled as inappropriate in 230 (23.7%), and accepted without a score calculated in 35 (3.6%). The ACTION ICU score was calculated in 146 patients (15.0%). Among patients with a final hospital diagnosis of NSTEMI, the BPA triggered 135 times (13.9% of all triggers). In this cohort, the BPA was hidden until the patient left the ED (to go home or to an inpatient bed) 67 times (45.9%), canceled as inappropriate 16 times (11.9%), and accepted without a score calculated 4 times (3.0%). A risk score was calculated 53 times (39.3%). Low use of the risk calculator, even in patients with a final hospital diagnosis of NSTEMI, was the largest barrier to uptake, with clinicians frequently taking the path of least resistance and hiding the BPA until the patient left the ED.Success of the InitiativeThis initiative was not successful, reflecting the difficulty in implementing risk assessment and decision support tools within the EHR. In the year before BPA rollout (Before cohort: August 14, 2016, to August 13, 2017), 253 patients initially presenting to the ED had a final hospital diagnosis of NSTEMI; in the year after BPA rollout (After cohort: August 14, 2017, August 14, 2018), there were 190 such patients. For each month of the study period, we describe the proportion of patients directly admitted from the ED to the ICU, the proportion initially admitted elsewhere and transferred to the ICU, the proportion that died while hospitalized, as well as the median (25th to 75th percentiles) ICU length of stay and hospital length of stay. Using a χ2 test, we compared the proportion of patients directly admitted to the ICU, the proportion transferred to the ICU, and the proportion that died while hospitalized in the Before and After cohorts. We compared hospital length of stay and ICU length of stay in the Before and After cohorts using the Wilcoxon rank-sum test.Overall, there were no differences between the Before and After cohorts in the proportion of patients admitted directly from the ED to the ICU, the proportion transferred to the ICU, the proportion that died while hospitalized, or in median hospital or ICU length of stay (Figure). In the Before cohort, 95 NSTEMI patients (37.5%) were admitted directly from the ED to the ICU, compared with 62 (32.6%) in the After cohort (P=0.32). Among patients initially admitted to a non-ICU setting, 33 of 158 (20.9%) in the Before cohort were eventually transferred to the ICU, compared with 27 of 128 (21.1%) in the After cohort (P=0.78). In-hospital mortality was 8.3% in the Before cohort and 4.7% in the After cohort (P=0.18). Median (25th to 75th percentiles) ICU length of stay was 0 days (0–2) in both the Before and After cohorts (P=0.96); median (25th to 75th percentiles) hospital length of stay was 5 days (4–10) in the Before cohort and 5 days (3–9) in the After cohort (P=0.27).Download figureDownload PowerPointFigure. Association between rollout of the Acute Coronary Treatment and Intervention Outcomes Network intensive care unit (ICU) risk calculator and clinical and utilization outcomes. Comparing the time period before best practice advisory (BPA) rollout to the time period after, there was no change in health care utilization outcomes, including the proportion of patients with non–ST-segment–elevation myocardial infarction admitted to the ICU from the emergency department, the proportion of patients transferred to the ICU after admission, median ICU or hospital length or stay, or the proportion of patients who died while hospitalized. ED indicates emergency department; and LOS, length of stay.In the year before BPA rollout, 120 patients with NSTEMI could be linked to local National Cardiovascular Data Registry records for the purposes of calculating ACTION ICU scores; in the year after, 99 patients could be linked. Among 41 patients admitted to the ICU in the Before cohort, median ACTION ICU score was 7 (25th to 75th percentiles, 4–9); among 28 admitted to the ICU in the After cohort, median ACTION ICU score was 7 (4–10; P=0.69). Among 79 patients admitted to a non-ICU setting in the Before cohort, median ACTION ICU score was 4 (25th to 75th percentiles, 3–6); median ACTION ICU score was 5 (3–7) among 71 patients admitted to a non-ICU setting in the After cohort (P=0.42).Translation to Other SettingsThe growing number of patients with advanced heart failure, pulmonary hypertension, and adult congenital heart disease has changed the face of the cardiac ICU. Rational use of limited cardiac ICU beds requires that patients with NSTEMI at high risk of clinical deterioration be treated in the ICU while those at low risk not occupy beds needed for patients with critical illness. Core components of the intervention reported here include development with input from ED and cardiology clinicians and the recognition that a risk score should be part of the decision-making process and not supplement clinician judgment or obvious indications for ICU care. The major challenge in translating this intervention to other settings is limited interoperability between EHRs at different medical centers,5 which complicates broad dissemination of a strategy that integrates predictive analytics and decision support into the EHR. In the absence of a plug-and-play design, individual institutions will need to build their own modified BPAs and map the location of key data elements within the EHR.Summary of the Experience, Future Directions, and ChallengesOur study highlights the difficulty of integrating predictive analytics into the EHR using existing processes like BPAs. We had hypothesized that clearly linking the results of a 1-time risk score calculation to a single clinical decision would encourage effective utilization of the risk score calculator; however, this was not the case. Successful integration of predictive analytics into the EHR for a large user pool requires that the process is as streamlined, user-friendly, and compatible with existing workflows as possible.In our discussions with ED clinicians, it was clear that forcing clinicians to respond to the BPA upon first opening patients' charts did not fit into their workflow, as clinicians often opened the chart before seeing the patient. Within the BPA framework, there was no way to deliver predictive analytics at the time when it was most likely to be useful for decision-making. Furthermore, the need for clinicians to manually answer 4 questions, rather than having the ACTION ICU risk score calculated automatically, may have reduced utilization, highlighting the importance of high-fidelity, real-time data mapping within the EHR to facilitate implementation of predictive analytics. High-fidelity data mapping may also have enabled us to narrow the trigger for the risk score calculator. A risk score was calculated in <15% of encounters in which the calculator was triggered, and alarm fatigue may have led clinicians to inappropriately ignore the BPA.Though integrating the ACTION ICU score into the EHR did not affect ICU utilization or admission patterns in this study, standardization of ICU admission practices for patients with MI remains a worthy goal, with the potential to reduce costs of health care delivery and preserve ICU beds for patients with other critical cardiovascular illnesses.Sources of FundingThis study was supported by grants from the Duke Institute for Health Innovation to Drs Fanaroff and Kansal. Dr Fanaroff is supported by a career development grant from the American Heart Association.Supplemental MaterialsFigure IDisclosures None.FootnotesThe Data Supplement is available at https://www.ahajournals.org/doi/suppl/10.1161/CIRCOUTCOMES.120.007602.Alexander C. Fanaroff, MD, MHS, Perelman Center for Advanced Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104. Email alexander.[email protected]upenn.eduReferences1. Fanaroff AC, Peterson ED, Chen AY, Thomas L, Doll JA, Fordyce CB, Newby LK, Amsterdam EA, Kosiborod MN, de Lemos JA, et al.. Intensive care unit utilization and mortality among medicare patients hospitalized with non–ST-segment elevation myocardial infarction.JAMA Cardiol. 2017; 2:36–44. doi: 10.1001/jamacardio.2016.3855CrossrefMedlineGoogle Scholar2. Chen R, Strait KM, Dharmarajan K, Li SX, Ranasinghe I, Martin J, Fazel R, Masoudi FA, Cooke CR, Nallamothu BK, et al.. Hospital variation in admission to intensive care units for patients with acute myocardial infarction.Am Heart J. 2015; 170:1161–1169. doi: 10.1016/j.ahj.2015.09.003CrossrefMedlineGoogle Scholar3. Fanaroff AC, Chen AY, Thomas LE, Pieper KS, Garratt KN, Peterson ED, Newby LK, de Lemos JA, Kosiborod MN, Amsterdam EA, et al.. A risk score to predict need for intensive care in initially hemodynamically stable adults with non-ST segment elevation myocardial infarction.J Am Heart Assoc. 2018; 7:e008894. doi: 10.1161/JAHA.118.008894LinkGoogle Scholar4. Bedoya AD, Clement ME, Phelan M, Steorts RC, O'Brien C, Goldstein BA. Minimal impact of implemented early warning score and best practice alert for patient deterioration.Crit Care Med. 2019; 47:49–55. doi: 10.1097/CCM.0000000000003439CrossrefMedlineGoogle Scholar5. Reisman M. EHRs: the challenge of making electronic data usable and interoperable.PT. 2017; 42:572–575.MedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsRelated articlesOld Habits Die HardAndrea D. Thompson, et al. Circulation: Cardiovascular Quality and Outcomes. 2021;14 April 2021Vol 14, Issue 4Article InformationMetrics Download: 641 © 2021 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.120.007602PMID: 33757310 Manuscript receivedOctober 22, 2020Manuscript acceptedJanuary 22, 2021Originally publishedMarch 24, 2021 Keywordsheart arrestoutcome assessment, health carehospitalsmyocardial infarctionpolicyPDF download Advertisement SubjectsHealth ServicesMortality/SurvivalMyocardial InfarctionQuality and Outcomes