Subscribe to RSS
Integration of Risk Scores and Integration Capability in Electronic Patient Records
Background Digital availability of patient data is continuously improving with the increasing implementation of electronic patient records in physician practices. The emergence of digital health data defines new fields of application for data analytics applications, which in turn offer extensive options of using data. Common areas of data analytics applications include decision support, administration, and fraud detection. Risk scores play an important role in compiling algorithms that underlay tools for decision support.
Objectives This study aims to identify the current state of risk score integration and integration capability in electronic patient records for cardiovascular disease and diabetes in German primary care practices.
Methods We developed an evaluation framework to determine the current state of risk score integration and future integration options for four cardiovascular disease risk scores (arriba, Pooled Cohort Equations, QRISK3, and Systematic Coronary Risk Evaluation) and two diabetes risk scores (Finnish Diabetes Risk Score and German Diabetes Risk Score). We then used this framework to evaluate the integration of risk scores in common practice software solutions by examining the software and inquiring the respective software contact person.
Results Our evaluation showed that the most widely integrated risk score is arriba, as recommended by German medical guidelines. Every software version in our sample provided either an interface to arriba or the option to implement one. Our assessment of integration capability revealed a more nuanced picture. Results on data availability were mixed. Each score contains at least one variable, which requires laboratory diagnostics. Our analysis of data standardization showed that only one score documented all variables in a standardized way.
Conclusion Our assessment revealed that the current state of risk score integration in physician practice software is rather low. Integration capability currently faces some obstacles. Future research should develop a comprehensive framework that considers the reasonable integration of risk scores into practice workflows, disease prevention programs, and the awareness of physicians and patients.
The present work was performed in fulfillment of the requirements for obtaining the degree “Dr. rer. biol. hum.” by Ann-Kathrin Heider from the Friedrich-Alexander-Universität Erlangen-Nürnberg.
Protection of Human and Animal Subjects
Human and/or animal subjects were not included in the project.
Received: 22 February 2022
Accepted: 13 July 2022
Article published online:
07 September 2022
© 2022. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
- 1 KBV. PraxisBarometer Digitalisierung. Accessed March 8, 2021 at: https://www.kbv.de/media/sp/PraxisBarometer-Digitalisierung_2018.pdf
- 2 KBV. PraxisBarometer Digitalisierung 2019. Accessed March 8, 2021 at: https://www.kbv.de/media/sp/KBV_Praxisbarometer_Digitalisierung_2019.pdf
- 3 National Center for Health Staistics. . 2019 National Electronic Health Records Survey Public Use File National Weighted Estimates, Accessed May 15, 2022 at: https://www.cdc.gov/nchs/data/nehrs/2019NEHRS-PUF-weighted-estimates-508.pdf
- 4 Pinevich Y, Clark KJ, Harrison AM, Pickering BW, Herasevich V. Interaction time with electronic health records: a systematic review. Appl Clin Inform 2021; 12 (04) 788-799
- 5 Islam MS, Hasan MM, Wang X, Germack HD, Noor-E-Alam M. A systematic review on healthcare analytics: application and theoretical perspective of data mining. Healthcare (Basel) 2018; 6 (02) 54
- 6 Kwan JL, Lo L, Ferguson J. et al. Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials. BMJ 2020; 370: m3216
- 7 Kruse CS, Ehrbar N. Effects of computerized decision support systems on practitioner performance and patient outcomes: systematic review. JMIR Med Inform 2020; 8 (08) e17283
- 8 Wu CX, Suresh E, Phng FWL. et al. Effect of a real-time risk score on 30-day readmission reduction in Singapore. Appl Clin Inform 2021; 12 (02) 372-382
- 9 Walker RL, Shortreed SM, Ziebell RA. et al. Evaluation of electronic health record-based suicide risk prediction models on contemporary data. Appl Clin Inform 2021; 12 (04) 778-787
- 10 Schreier DJ, Lovely JK. Optimizing clinical monitoring tools to enhance patient review by pharmacists. Appl Clin Inform 2021; 12 (03) 621-628
- 11 Sim I, Gorman P, Greenes RA. et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc 2001; 8 (06) 527-534
- 12 Pereira AM, Jácome C, Amaral R, Jacinto T, Fonseca JA. Real-time clinical decision support at the point of care. In Agache I, Hellings P, eds. Implementing Precision Medicine in Best Practices of Chronic Airway Diseases. MA: Academic Press; 2019: 125-133
- 13 Black JA, Campbell JA, Parker S. et al. Absolute risk assessment for guiding cardiovascular risk management in a chest pain clinic. Med J Aust 2021; 214 (06) 266-271
- 14 Vogenberg FR. Predictive and prognostic models: implications for healthcare decision-making in a modern recession. Am Health Drug Benefits 2009; 2 (06) 218-222
- 15 Van de Velde S, Heselmans A, Delvaux N. et al. A systematic review of trials evaluating success factors of interventions with computerised clinical decision support. Implement Sci 2018; 13 (01) 114
- 16 Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med 2020; 3: 17
- 17 Aakre C, Dziadzko M, Keegan MT, Herasevich V. Automating clinical score calculation within the electronic health record: a feasibility assessment. Appl Clin Inform 2017; 8 (02) 369-380
- 18 Perry WM, Hossain R, Taylor RA. Assessment of the Feasibility of automated, real-time clinical decision support in the emergency department using electronic health record data. BMC Emerg Med 2018; 18 (01) 19
- 19 Goff Jr DC, Lloyd-Jones DM, Bennett G. et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014; 129 (25, Suppl 02) S49-S73
- 20 Piepoli MF, Hoes AW, Agewall S. et al; ESC Scientific Document Group. 2016 European guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention and Rehabilitation (EACPR). Eur Heart J 2016; 37 (29) 2315-2381
- 21 Ludt S, Angelow A, Baum E. et al. S3-Leitlinien Hausärztliche Risikoberatung zur kardiovaskulären Prävention, AWMF-Register-Nr. 053 -024 DEGAM-Leitlinie Nr. 19. Hrsg Deutsche Gesellschaft für Allgemeinmedizin und Familienmedizin e.V. 2017. Accessed May 15, 2022 at:. www.awmf.org/leitlinien/detail/ll/053-024.html
- 22 National Institute of Clinical Excellence (NICE). Surveillance report 2018–Cardiovascular disease: risk assessment and reduction, including lipid modification. NICE guideline CG181. 2014. Accessed May 15, 2022 at: https://www.nice.org.uk/guidance/cg181/resources/surveillance-report-2018-cardiovascular-disease-risk-assessment-and-reduction-including-lipid-modification-2014-nice-guideline-cg181-4724759773/chapter/Surveillance-decision?tab=evidence
- 23 American Diabetes Association. Classification and diagnosis of diabetes: standards of medical care in diabetes-2021. Diabetes Care 2021; 44 (Supplement1): 15-33
- 24 NICE. Type 2 diabetes: prevention in people at high risk. Accessed May 15, 2022 at: https://www.nice.org.uk/guidance/ph38
- 25 Bundesärztekammer (BÄK). Kassenärztliche Bundesvereinigung (KBV), Arbeitsgemeinschaft der Wis-senschaftlichen Medizinischen Fachgesellschaften (AWMF). Nationale VersorgungsLeitlinie Therapie des Typ-2-Diabetes – Langfassung, 1. Auflage. Version 4. 2013. Accessed May 15, 2022 at: https://www.leitlinien.de/themen/diabetes/archiv/pdf/therapie-des-typ-2-diabetes/dm-therapie-1aufl-vers4-lang.pdf
- 26 Kassenärztliche Bundesvereinigung. Installationsstatistiken von Softwaresystemen. Accessed February 15, 2021 at: https://www.kbv.de/html/7023.php
- 27 Donner-Banzhoff N, Keller H, Sadowski E-M. et al. arriba Werkstattbericht. Accessed February 15, 2021 at: https://arriba-hausarzt.de/uploads/files/arriba_werkstattbericht.pdf
- 28 Grammer TB, Dressel A, Gergei I. et al. Cardiovascular risk algorithms in primary care: results from the DETECT study. Sci Rep 2019; 9 (01) 1101
- 29 GPZK gGmbH.. Arriba fuer Hausaerzte. Accessed February 12, 2021 at: https://arriba-hausarzt.de/zugang-arriba/arriba-f%C3%BCr-haus%C3%A4rzte
- 30 Martinez-Millana A, Argente-Pla M, Valdivieso Martinez B, Traver Salcedo V, Merino-Torres JF. Driving type 2 diabetes risk scores into clinical practice: performance analysis in hospital settings. J Clin Med 2019; 8 (01) 107
- 31 Public Health England. NHS Health Check Best practice guidance. Accessed May 15, 2022 at: https://www.healthcheck.nhs.uk/seecmsfile/?id=1480
- 32 HIMSS. Interoperability in Healthcare. Accessed May 15, 2022 at: https://www.himss.org/resources/interoperability-healthcare
- 33 Miller DD. The medical AI insurgency: what physicians must know about data to practice with intelligent machines. NPJ Digit Med 2019; 2: 62
- 34 CGM Mobile Services GmbH. CGM LIFE. . Accessed March 14, 2021 at: https://cgmlife.de/
- 35 medatixx GmbH & Co. KG. x.patient. Accessed March 14, 2021 at: https://medatixx.de/praxissoftware/zusatzloesungen/xpatient
- 36 Amarasingham R, Patzer RE, Huesch M, Nguyen NQ, Xie B. Implementing electronic health care predictive analytics: considerations and challenges. Health Aff (Millwood) 2014; 33 (07) 1148-1154
- 37 Ruwanpathirana T, Owen A, Reid CM. Review on cardiovascular risk prediction. Cardiovasc Ther 2015; 33 (02) 62-70
- 38 Glümer C, Vistisen D, Borch-Johnsen K, Colagiuri S. DETECT-2 Collaboration. Risk scores for type 2 diabetes can be applied in some populations but not all. Diabetes Care 2006; 29 (02) 410-414
- 39 Buijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev 2011; 33 (01) 46-62
- 40 Shelov E, Muthu N, Wolfe H. et al. Design and implementation of a pediatric ICU acuity scoring tool as clinical decision support. Appl Clin Inform 2018; 9 (03) 576-587
- 41 Wasylewicz A, van de Burgt B, Weterings A. et al. Identifying adverse drug reactions from free-text electronic hospital health record notes. Br J Clin Pharmacol 2022; 88 (03) 1235-1245
- 42 Lee TC, Shah NU, Haack A, Baxter SL. Clinical implementation of predictive models embedded within electronic health record systems: a systematic review. Informatics (MDPI) 2020; 7 (03) 25
- 43 Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ 2011; 343: d7163