Osheroff, J. et al. Improving Outcomes with Clinical Decision Support: An Implementer’s Guide. (HIMSS Publishing, 2012).
Sim, I. et al. Clinical decision support systems for the practice of evidence-based medicine. J. Am. Med Inf. Assoc. Jamia. 8, 527–534 (2001).
Google Scholar
De Dombal, F. Computers, diagnoses and patients with acute abdominal pain. Arch. Emerg. Med. 9, 267–270 (1992).
Shortliffe, E. H. & Buchanan, B. G. A model of inexact resoning in medicine. Math. Biosci. 379, 233–262 (1975).
Middleton, B., Sittig, D. F. & Wright, A. Clinical decision support: a 25 year retrospective and a 25 year vision. Yearb. Med. Inform. 25(S 01), S103–S116 (2016).
Google Scholar
Dias, D. Wearable health devices—vital sign monitoring, systems and technologies. (2018).
Berner, E. S. (Ed.). Clinical Decision Support Systems (Springer, New York, NY, 2007).
Osheroff, J., Pifer, E., Teigh, J., Sittig, D. & Jenders, R. Improving Outcomes with Clinical Decision Support: An Implementer’s Guide. (HIMS, 2005).
Deo, R. C. Machine learning in medicine. Circulation 132, 1920–1930 (2015).
Google Scholar
HITECH Act Enforcement Interim Final Rule. https://www.hhs.gov/hipaa/for-professionals/special-topics/HITECH-act-enforcement-interim-final-rule/index.html.
Electronic medical record adoption model requirements. Accessed 25 Aug 2019 (2017).
Chang, F. & Gupta, N. Progress in electronic medical record adoption in Canada Recherche Les progrès dans l’ adoption du dossier médical électronique au Canada. Canadian Family Physician. 61, 1076–1084 (2015).
Healthcare Information and Management Systems Society (HIMSS). Electronic Health Records: A Global Perspective, 2nd edn. (2010).
Nøhr, C. et al. Nationwide citizen access to their health data: analysing and comparing experiences in Denmark, Estonia and Australia. 1–11. (2017).
Omididan, Z. & Hadianfar, A. The role of clinical decision support systems in healthcare (1980-2010): a systematic review study. Jentashapir Sci.-Res Q. 2, 125–134 (2011).
Kabane, S. M. Healthcare and the Effect of Technology: Developments, Challenges and Advancements: Developments, Challenges and Advancements. Medical Information Science Reference (2010).
Vonbach, P., Dubied, A., Krähenbühl, S. & Beer, J. H. Prevalence of drug-drug interactions at hospital entry and during hospital stay of patients in internal medicine. Eur. J. Intern. Med. 19, 413–420 (2008).
Google Scholar
Helmons, P. J., Suijkerbuijk, B. O., Nannan Panday, P. V. & Kosterink, J. G. W. Drug-drug interaction checking assisted by clinical decision support: a return on investment analysis. J. Am. Med. Inf. Assoc. Jamia. 22, 764–772 (2015).
Google Scholar
Koutkias, V. & Bouaud, J. Contributions from the 2017 Literature on Clinical Decision Support. Yearb. Med. Inf. 27, 122–128 (2018).
Google Scholar
Phansalkar, S. et al. High-priority drug – drug interactions for use in electronic health records. J. Am. Med. Inform. Assoc. 19, 735–743 (2012).
Cornu, P., Phansalkar, S., Seger, D. L., Cho, I. & Pontefract, S. International Journal of Medical Informatics High-priority and low-priority drug – drug interactions in di ff erent international electronic health record systems: a comparative study. Int. J. Med. Inf. 111, 165–171 (2018).
Google Scholar
McEvoy, D. S. et al. Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. J. Am. Med. Inf. Assoc. 24, 331–338 (2017).
Google Scholar
Cho, I., Lee, J., Choi, J., Hwang, H. & Bates, D. W. National rules for drug-drug interactions: are they appropriate for tertiary hospitals?. J. Korean Med. Sci. 31, 1887–1896 (2016).
Google Scholar
Mahoney, C. D., Berard-Collins, C. M., Coleman, R., Amaral, J. F. & Cotter, C. M. Effects of an integrated clinical information system on medication safety in a multi-hospital setting. Am. J. Health Syst. Pharm. 64, 1969–1977 (2007).
Google Scholar
Peris-Lopez, P., Orfila, A., Mitrokotsa, A. & van der Lubbe, J. C. A. A comprehensive RFID solution to enhance inpatient medication safety. Int. J. Med. Inf. 80, 13–24 (2011).
Google Scholar
Levtzion-korach, O. et al. Effect of bar-code technology on the safety of medication administration. N. Engl. J. Med. 362, 1698–1707 (2010).
Google Scholar
van der Veen, W. et al. Association between workarounds and medication administration errors in bar-code-assisted medication administration in hospitals. J. Am. Med. Inf. Assoc. 25, 385–392 (2018).
Google Scholar
Eslami, S. et al. Effects of two different levels of computerized decision support on blood glucose regulation in critically ill patients. Int J. Med. Inf. 81, 53–60 (2012).
Google Scholar
Jia, P., Zhang, L., Chen, J., Zhao, P. & Zhang, M. The effects of clinical decision support systems on medication safety: an overview. PLoS ONE 11, 1–17 (2016).
Kwok, R., Dinh, M., Dinh, D. & Chu, M. Improving adherence to asthma clinical guidelines and discharge documentation from emergency departments: Implementation of a dynamic and integrated electronic decision support system. Emerg. Med. Australas. 21, 31–37 (2009).
Google Scholar
Davis, D. A. & Taylor-Vaisey, A. Translating guidelines into practice: a systematic review of theoretic concepts, practical experience and research evidence in the adoption of clinical practice guidelines. Can. Med. Assoc. J. 157, 408–416 (1997).
Google Scholar
Michael, C., Rand, C. S., Powe, N. R., Wu, A. W. & Wilson, M. H. Why don’ t physicians follow clinical practice guidelines? a framework for improvement. Jama,. 282, 1458–1465 (1999).
Google Scholar
Shortliffe, T. Medical thinking: what should we do? (2006).
Lipton, J. A. et al. Impact of an alerting clinical decision support system for glucose control on protocol compliance and glycemic control in the intensive cardiac care unit. Diabetes Technol. Ther. 13, 343–349 (2011).
Google Scholar
Salem, H. et al. A multicentre integration of a computer led follow up in surgical oncology is valid and safe. BJU Int. (2018).
Health Information Technology Foundations Module 28: Clinical Decision Support Basics. Carnegie Mellon University Open Learning Initiative. https://oli.cmu.edu/jcourse/workbook/activity/page?context=e6f7c0b180020ca600c0f4e5957d6f8c.
Embi, P. J., Jain, A., Clark, J. & Harris, C. M. Development of an electronic health record-based Clinical Trial Alert system to enhance recruitment at the point of care. AMIA Annu. Symp. Proc. 2005, 231–235 (2005).
Calloway, S., Akilo, H. & Bierman, K. Impact of a clinical decision support system on pharmacy clinical interventions, documentation efforts, and costs. Hosp. Pharm. 48, 744–752 (2013).
Google Scholar
McMullin, S. T. et al. Impact of an evidence-based computerized decision support system on primary care prescription costs. Ann. Fam. Med. 2, 494–498 (2004).
Google Scholar
Algaze, C. A. et al. Use of a checklist and clinical decision support tool reduces laboratory use and improves cost. Pediatrics 137, e20143019 (2016).
Google Scholar
Pruszydlo, M. G., Walk-Fritz, S. U., Hoppe-Tichy, T., Kaltschmidt, J. & Haefeli, W. E. Development and evaluation of a computerised clinical decision support system for switching drugs at the interface between primary and tertiary care. BMC Med. Inf. Decis. Mak. 12, 1 (2012).
Google Scholar
Bell, C. M., Jalali, A. & Mensah, E. A decision support tool for using an ICD-10 anatomographer to address admission coding inaccuracies: a commentary. Online J. Public Health Inform. 5, 222 (2013).
Google Scholar
Haberman, S. et al. Effect of clinical-decision support on documentation compliance in an electronic medical record. Obstet. Gynecol. 114, 311–317 (2009).
Google Scholar
Turchin, A., Shubina, M. & Gandhi, T. NLP for patient safety: splenectomy and pneumovax. In Proc. AMIA 2010 Annual Symposium (2010).
McEvoy, D., Gandhi, T. K., Turchin, A. & Wright, A. Enhancing problem list documentation in electronic health records using two methods: the example of prior splenectomy. BMJ Qual Saf. (2017).
Berner E. Clinical Decision Support Systems: Theory and Practice 3rd edn. (2016).
Berner, E. S. Diagnostic decision support systems: why aren’t they used more and what can we do about it? AMIA Annu. Symp. Proc. 2006, 1167–1168 (2006).
Segal, M. M. et al. Experience with integrating diagnostic decision support software with electronic health records: benefits versus risks of information sharing. EGEMs Gener. Evid. Methods Improv. Patient Outcomes 5, 23 (2017).
Google Scholar
Kunhimangalam, R., Ovallath, S. & Joseph, P. K. A clinical decision support system with an integrated EMR for diagnosis of peripheral neuropathy. J. Med. Syst. 38, 38 (2014).
Google Scholar
Martinez-Franco, A. I. et al. Diagnostic accuracy in Family Medicine residents using a clinical decision support system (DXplain): a randomized-controlled trial. Diagn. Berl. Ger. 5, 71–76 (2018).
Google Scholar
Singh, H., Schiff, G. D., Graber, M. L., Onakpoya, I. & Thompson, M. J. The global burden of diagnostic errors in primary care. BMJ Qual. Saf. 26, 484–494 (2017).
Google Scholar
Singh, H., Meyer, A. N. D. & Thomas, E. J. The frequency of diagnostic errors in outpatient care: Estimations from three large observational studies involving US adult populations. BMJ Qual. Saf. 23, 727–731 (2014).
Google Scholar
Razzaki, S. et al. A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis. arXiv preprint arXiv:1806.10698 (2018).
Fraser, H., Coiera, E. & Wong, D. Safety of patient-facing digital symptom checkers. Lancet 392, 2263–2264 (2018).
Google Scholar
Georgiou, A., Prgomet, M., Markewycz, A., Adams, E. & Westbrook, J. I. The impact of computerized provider order entry systems on medical-imaging services: a systematic review. J. Am. Med. Inf. Assoc. 18, 335–340 (2011).
Google Scholar
Blackmore, C. C., Mecklenburg, R. S. & Kaplan, G. S. Effectiveness of clinical decision support in controlling inappropriate imaging. JACR 8, 19–25 (2019).
DSS Inc. Radiology Decision Support (RadWise®). https://www.dssinc.com/products/integrated-clinical-products/radwise-radiology-decision-support/.
Giardino, A. et al. Role of imaging in the era of precision medicine. Acad. Radiol. 24, 639–649 (2017).
Google Scholar
Oakden-rayner, L. et al. Precision Radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework. Sci. Rep. (2017).
From Invisible to Visible: IBM Demos AI to Radiologists. Accessed Aug 2019 (2016).
Greenspan, H., Ginneken van, B. & Summers, R. M. Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35, 1153–1159 (2016).
Google Scholar
Suzuki, K. & Chen, Y. Artificial intelligence in decision support systems for diagnosis in medical imaging. (2018).
IBM Watson Health – IBM Watson for Oncology. Accessed 25 Aug 2019 (2018).
Lunit Inc. Accessed Aug 2019 (2018).
Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA – J. Am. Med. Assoc. 316, 2402–2410 (2016).
Google Scholar
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115 (2017).
Google Scholar
Rajpurkar, P. et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017).
Hannun, A. Y. et al. FOCUS | Letters FOCUS | Letters Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network letters | FOCUS letters | FOCUS. Nat. Med. (2019).
Erickson, B. J. Machine Intelligence in Medical Imaging (Society for Imaging Informatics, SIIM, 2016).
Keltch, B., Lin, Y. & Bayrak, C. Comparison of AI techniques for prediction of liver fibrosis in hepatitis patients patient facing systems. J. Med. Syst. (2014).
Mørkrid, L. et al. Continuous age- and sex-adjusted reference intervals of urinary markers for cerebral creatine deficiency syndromes: a novel approach to the definition of reference intervals. Clin. Chem. 61, 760–768 (2015).
Google Scholar
Spyridonos, P., Cavouras, D., Ravazoula, P. & Nikiforidis, G. A computer-based diagnostic and prognostic system for assessing urinary bladder tumour grade and predicting cancer recurrence. Med. Inf. Internet Med. 27, 111–122 (2002).
Google Scholar
Tsolaki, E. et al. Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data. Int. J. Comput. Assist Radio. Surg. 10, 1149–1166 (2015).
Google Scholar
Davis, S., Roudsari, A., Raworth, R., Courtney, K. L. & Mackay, L. Shared decision-making using personal health record technology: a scoping review at the crossroads. J. Am. Med. Inf. Assoc. 24, 857–866 (2017).
Google Scholar
Fuji, K. T. et al. Standalone personal health records in the United States: meeting patient desires. Health Technol. 2, 197–205 (2012).
Google Scholar
Tang, P. C., Ash, J. S., Bates, D. W., Overhage, J. M. & Sands, D. Z. Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption. J. Am. Med. Inf. Assoc. 13, 121–126 (2006).
Google Scholar
Wald, J. S. et al. A patient-controlled journal for an electronic medical record: issues and challenges. Stud. Health Technol. Inform. 107(Pt 2), 1166–1170 (2004).
Google Scholar
Hanauer, D. A., Preib, R., Zheng, K. & Choi, S. W. Patient-initiated electronic health record amendment requests. J. Am. Med. Inf. Assoc. 21, 992–1000 (2014).
Google Scholar
Rosenbloom, S. T. et al. Triaging patients at risk of influenza using a patient portal. J. Am. Med. Inf. Assoc. 19, 549–554 (2012).
Google Scholar
Roehrs, A., Da Costa, C. A., Da Rosa Righi, R. & De Oliveira, K. S. F. Personal health records: A systematic literature review. J. Med. Internet Res. (2017).
Benhamou, P. Y. Improving diabetes management with electronic health records and patients’ health records. Diabetes Metab. 37(Suppl. 4), S53–S56 (2011).
Google Scholar
Kumar, R. B., Goren, N. D., Stark, D. E., Wall, D. P. & Longhurst, C. A. Automated integration of continuous glucose monitor data in the electronic health record using consumer technology. J. Am. Med. Inf. Assoc. 23, 532–537 (2016).
Google Scholar
Kilsdonk, E., Peute, L. W., Riezebos, R. J., Kremer, L. C. & Jaspers, M. W. M. Uncovering healthcare practitioners’ information processing using the think-aloud method: From paper-based guideline to clinical decision support system. Int. J. Med. Inf. 86, 10–19 (2016).
Google Scholar
Dowding, D. et al. Nurses’ use of computerised clinical decision support systems: A case site analysis. J. Clin. Nurs. 18, 1159–1167 (2009).
Google Scholar
Ash, J. S., Sittig, D. F., Campbell, E. M., Guappone, K. P. & Dykstra, R. H. Some unintended consequences of clinical decision support systems. AMIA Annu Symp. Proc. AMIA Symp. AMIA Symp. 2007, 26–30 (2007).
Khalifa, M. & Zabani, I. Improving utilization of clinical decision support systems by reducing alert fatigue: Strategies and recommendations. Stud. Health Technol. Inform. 226, 51–54 (2016).
Google Scholar
Van Laere, S. et al. Clinical decision support systems for drug allergy checking: systematic review. (2018).
Wyatt, J. & Spiegelhalter, D. Field trials of medical decision-aids: potential problems and solutions. American Medical Informatics Association. 3–7. (1991).
Goddard, K., Roudsari, A. & Wyatt, J. Automation bias – A hidden issue for clinical decision support system use. Stud. Health Technol. Inform. 164, 17–22 (2011).
Google Scholar
Devaraj, S., Sharma, S. K., Fausto, D. J., Viernes, S. & Kharrazi, H. Barriers and facilitators to clinical decision support systems adoption: a systematic review. J. Bus Adm. Res. (2014).
Leslie, S. J. et al. Clinical decision support software for management of chronic heart failure: Development and evaluation. Comput Biol. Med. 36, 495–506 (2006).
Google Scholar
Murray, E. et al. Why is it difficult to implement e-health initiatives? A qualitative study. Implement Sci. 6, 6 (2011).
Google Scholar
Lai, F., Macmillan, J., Daudelin, D. H. & Kent, D. M. The potential of training to increase acceptance and use of computerized decision support systems for medical diagnosis. Hum. Factors J. Hum. Factors Erg. Soc. 48, 95–108 (2006).
Google Scholar
Ojeleye, L. Ensuring effective computerised clinical decision support. Prescriber 27, 54–56 (2016).
Google Scholar
Cook, D. A., Teixeira, M. T., Heale, B. S. E., Cimino, J. J. & Del Fiol, G. Context-sensitive decision support (infobuttons) in electronic health records: a systematic review. J. Am. Med Inf. Assoc. 24, 460–468 (2017).
Google Scholar
Sujansky, W. Heterogeneous database integration in biomedicine. J. Biomed. Inform. 34, 285–298 (2001).
Google Scholar
Index – FHIR v3.0.1. (2018). Accessed July 2019.
Katehakis, D. G. Towards the Development of a National eHealth Interoperability Framework to Address Public Health Challenges in Greece. Proceedings of the First International Workshop on Semantic Web Technologies for Health Data Management, SWH@ISWC. 2164, 1–9 (2018).
EHRIntelligence. 5 Ways States Mandate Health Information Exchange. Accessed Aug 2019 (2015).
European Commission Report. Commission Recommendation on a European Electronic Health Record Exchange Format (C(2019)800) of 6 February 2019. (2019).
Bresnick, J. HealthITAnalytics. Interoperability, Low Costs Make Cloud-Based EHRs a Favorite. Accessed July 2019 (2015).
Fernández-Cardeñosa, G., De La Torre-Díez, I., López-Coronado, M. & Rodrigues, J. J. P. C. Analysis of cloud-based solutions on EHRs systems in different scenarios. J. Med. Syst. 36, 3777–3782 (2012).
Google Scholar
Rodrigues, J. J. P. C., de la Torre, I., Fernández, G. & López-Coronado, M. Analysis of the security and privacy requirements of cloud-based electronic health records systems. J. Med. Internet Res. 15, e186 (2013).
Kabachinski, J. A look at clinical decision support systems. Biomed. Instrum. Technol. 47, 432–434 (2013).
Google Scholar
O’Reilly, D., Tarride, J.-E., Goeree, R., Lokker, C. & McKibbon, K. A. The economics of health information technology in medication management: a systematic review of economic evaluations. J. Am. Med. Inf. Assoc. 19, 423–438 (2012).
Google Scholar
Bright, T. J. et al. Effect of clinical decision-support systems: a systematic review. Ann. Intern Med. 157, 29–43 (2012).
Google Scholar
Jacob, V. et al. Cost and economic benefit of clinical decision support systems (CDSS) for cardiovascular disease prevention: a community guide systematic review. J. Am. Med. Inf. Assoc. Jamia. 24, 669–676 (2017).
Google Scholar
Main, C. et al. Computerised decision support systems in order communication for diagnostic, screening or monitoring test ordering: systematic reviews of the effects and cost-effectiveness of systems. Health Technol. Assess. 14, 1–227 (2010).
Google Scholar
Scheepers-Hoeks, A. M., Grouls, R. J., Neef, C. & Korsten, H. H. Strategy for implementation and first results of advanced clinical decision support in hospital pharmacy practice. Stud. Health Technol. Inform. 148, 142–148 (2009).
Edlin, R., McCabe, C., Hulme, C., Hall, P. & Wright, J. Cost effectiveness modelling for health technology assessment. (2015).
Vermeulen, K. M. et al. Cost-effectiveness of an electronic medication ordering system (CPOE/CDSS) in hospitalized patients. Int J. Med, Inf. 83, 572–580 (2014).
Google Scholar
Okumura, L. M., Veroneze, I., Burgardt, C. I. & Fragoso, M. F. Effects of a computerized provider order entry and a clinical decision support system to improve cefazolin use in surgical prophylaxis: a cost saving analysis. Pharm. Pract. 14, 1–7 (2016).
Osheroff, J. A. et al. A roadmap for national action on clinical decision support. J. Am. Med. Inf. Assoc. 14, 141–145 (2007).
Google Scholar
Greenes, R. A. Clinical Decision Support 2nd edn. The Road to Broad Adoption (2014).
Bonney, W. Impacts and risks of adopting clinical decision support systems. In: Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain. IntechOpen (2011).
Bates, D. W. et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J. Am. Med. Inf. Assoc. 10, 523–530 (2003).
Google Scholar
Kawamoto, K. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. Bmj 330, 765–0 (2005).
Google Scholar
Health Level Seven International – Homepage. Accessed 29 Aug 2019 (2018).
IHTSDO. History of SNOMED CT. Ihtsdo. (2015).
Marco-Ruiz, L. & Bellika, J. G. Semantic Interoperability in Clinical Decision Support Systems: A Systematic Review. Stud. Health Technol. Inf. 216, 958 (2015).
Angraal, S., Krumholz, H. M. & Schulz, W. L. Blockchain technology: applications in health care. Circ. Cardiovasc. Qual. Outcomes (2017).
Ivan, D. Moving toward a blockchain-based method for the secure storage of patient records. ONC/NIST Use of Blockchain for Healthcare and Research Workshop. Gaithersburg, Maryland, United States: ONC/NIST (2016).
Eichner, J. & Das, M. Challenges and Barriers to Clinical Decision Support (CDS) Design and Implementation Experienced in the Agency for Healthcare Research and Quality CDS Demonstrations. Agency Healthc Res. Qual. Website. 29. (2010).
Khalifa, M. Clinical decision support: strategies for success. Procedia Comput. Sci. 37, 422–427 (2014).
Google Scholar
Sittig, D. F. Electronic Health Records: Challenges in Design and Implementation. (CRC Press, 2014).
Harper, B. D. & Norman, K. L. Improving User Satisfaction: The Questionnaire for User Interaction Satisfaction Version 5.5. Proceedings of the 1st Annual Mid-Atlantic Human Factors Conference, (pp. 224–228), Virginia Beach, VA. (1993).
Lewis, J. R. The system usability scale: past, present, and future. Int J. Hum.-Comput. Interact. 34, 577–590 (2018).
Google Scholar
Lewis, J. R. IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. Int J. Hum.-Comput. Interact. 7, 57–78 (1995).
Google Scholar
Lewis, J. R. Measuring perceived usability: the CSUQ, SUS, and UMUX. Int J. Hum.-Comput Interact. 34, 1148–1156 (2018).
Google Scholar
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