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CC BY 4.0 · ACI open 2021; 05(02): e84-e93
DOI: 10.1055/s-0041-1736470
Original Article

A Clinical Decision Support System Design Framework for Nursing Practice

Sheng-Chieh Lu
1   Department of Symptom Research, University of Texas MD Anderson Cancer Center, Houston, Texas, United States
,
Rebecca J. Brown
2   School of Nursing, University of Minnesota, Minneapolis, Minnesota, United States
,
Martin Michalowski
2   School of Nursing, University of Minnesota, Minneapolis, Minnesota, United States
› Author Affiliations Funding The authors also acknowledge the financial support from the Office of the Vice President for Research, University of Minnesota Grant-in-Aid. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agency.
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Abstract

Background As nurses increasingly engage in decision-making for patients, a unique opportunity exists to translate research into practice using clinical decision support systems (CDSSs). While research has shown that CDSS has led to improvements in patient outcomes and nursing workflow, the success rate of CDSS implementation in nursing is low. Further, the majority of CDSS for nursing are not designed to support the care of patients with comorbidity.

Objectives The aim of the study is to conceptualize an evidence-based CDSS supporting complex patient care for nursing.

Methods We conceptualized the CDSS through extracting scientific findings of CDSS design and development. To describe the CDSS, we developed a conceptual framework comprising the key components of the CDSS and the relationships between the components. We instantiated the framework in the context of a hypothetical clinical case.

Results We present the conceptualized CDSS with a framework comprising six interrelated components and demonstrate how each component is implemented via a hypothetical clinical case.

Conclusion The proposed framework provides a common architecture for CDSS development and bridges CDSS research findings and development. Next research steps include (1) working with clinical nurses to identify their knowledge resources for a particular disease to better articulate the knowledge base needed by a CDSS, (2) develop and deploy a CDSS in practice using the framework, and (3) evaluate the CDSS in the context of nursing care.

Keywords

clinical decision support - evidence-based practice - knowledge repository - analysis

Protection of Human and Animal Subjects

This study was deemed exempt by the University Institutional Review Board.


Authors' Contribution

M.M. and S.-C.L. contributed toward conception and design; M.M. provided financial support. All the authors collected and analyzed the data, drafted and approved the manuscript.




Publication History

Received: 10 December 2020

Accepted: 02 September 2021

Article published online:
05 November 2021

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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  • References

  • 1 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330 (7494): 765-768
    CrossrefPubMedGoogle Scholar
  • 2 Fillmore CL, Bray BE, Kawamoto K. Systematic review of clinical decision support interventions with potential for inpatient cost reduction. BMC Med Inform Decis Mak 2013; 13 (01) 135
    CrossrefPubMedGoogle Scholar
  • 3 Goldzweig CL, Orshansky G, Paige NM. et al. Electronic health record-based interventions for improving appropriate diagnostic imaging: a systematic review and meta-analysis. Ann Intern Med 2015; 162 (08) 557-565
    CrossrefPubMedGoogle Scholar
  • 4 Lee S. Features of computerized clinical decision support systems supportive of nursing practice: a literature review. Comput Inform Nurs 2013; 31 (10) 477-495
    CrossrefPubMedGoogle Scholar
  • 5 Nibbelink CW, Carrington JM. Nurse decision making in acute care. Comput Inform Nurs 2019; 37 (03) 151-160
    CrossrefPubMedGoogle Scholar
  • 6 Nibbelink CW, Brewer BB. Decision-making in nursing practice: an integrative literature review. J Clin Nurs 2018; 27 (5-6): 917-928
    CrossrefPubMedGoogle Scholar
  • 7 Oliveira EM, Secco LMD, Figueiredo WB, Padilha KG, Secoli SR. Nursing Activities Score and the cost of nursing care required and available. Rev Bras Enferm 2019; 72 (Suppl. 01) 137-142
    CrossrefPubMedGoogle Scholar
  • 8 Welton JM, Harper EM. Measuring nursing care value. Nurs Econ 2016; 34 (01) 7-14
    PubMedGoogle Scholar
  • 9 Dunn Lopez K, Gephart SM, Raszewski R, Sousa V, Shehorn LE, Abraham J. Integrative review of clinical decision support for registered nurses in acute care settings. J Am Med Inform Assoc 2017; 24 (02) 441-450
    CrossrefPubMedGoogle Scholar
  • 10 Borum C. Barriers for hospital-based nurse practitioners utilizing clinical decision support systems: a systematic review. Comput Inform Nurs 2018; 36 (04) 177-182
    CrossrefPubMedGoogle Scholar
  • 11 Toma SDE, Para DED, Revisión E. Decision-making system for nursing: integrative review. J Nurs UFPE Line 2017; 11 (Suppl. 10) 4263-4272
    PubMedGoogle Scholar
  • 12 Khong PCB, Holroyd E, Wang W. A critical review of the theoretical frameworks and the conceptual factors in the adoption of clinical decision support systems. Comput Inform Nurs 2015; 33 (12) 555-570
    CrossrefPubMedGoogle Scholar
  • 13 Greenhalgh T, Wherton J, Papoutsi C. et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res 2017; 19 (11) e367
    CrossrefPubMedGoogle Scholar
  • 14 Banning M. A review of clinical decision making: models and current research. J Clin Nurs 2008; 17 (02) 187-195
    CrossrefPubMedGoogle Scholar
  • 15 Bakken S, Currie LM, Lee N-J, Roberts WD, Collins SA, Cimino JJ. Integrating evidence into clinical information systems for nursing decision support. Int J Med Inform 2008; 77 (06) 413-420
    CrossrefPubMedGoogle Scholar
  • 16 Pelaccia T, Tardif J, Triby E, Charlin B. An analysis of clinical reasoning through a recent and comprehensive approach: the dual-process theory. Med Educ Online 2011; 16 (01) DOI: 10.3402/meo.v16i0.5890.
    CrossrefPubMedGoogle Scholar
  • 17 Paley J, Cheyne H, Dalgleish L, Duncan EAS, Niven CA. Nursing's ways of knowing and dual process theories of cognition. J Adv Nurs 2007; 60 (06) 692-701
    CrossrefPubMedGoogle Scholar
  • 18 Bjørk IT, Hamilton GA. Clinical decision making of nurses working in hospital settings. Nurs Res Pract 2011; 2011: 524918
    PubMedGoogle Scholar
  • 19 Carper B. Fundamental patterns of knowing in nursing. ANS Adv Nurs Sci 1978; 1 (01) 13-23
    CrossrefPubMedGoogle Scholar
  • 20 Price A, Zulkosky K, White K, Pretz J. Accuracy of intuition in clinical decision-making among novice clinicians. J Adv Nurs 2017; 73 (05) 1147-1157
    CrossrefPubMedGoogle Scholar
  • 21 Hicks EP, Kluemper GT. Heuristic reasoning and cognitive biases: are they hindrances to judgments and decision making in orthodontics?. Am J Orthod Dentofacial Orthop 2011; 139 (03) 297-304
    CrossrefPubMedGoogle Scholar
  • 22 Featherston R, Downie LE, Vogel AP, Galvin KL. Decision making biases in the allied health professions: a systematic scoping review. PLoS One 2020; 15 (10) e0240716
    CrossrefPubMedGoogle Scholar
  • 23 Thompson C, Aitken L, Doran D, Dowding D. An agenda for clinical decision making and judgement in nursing research and education. Int J Nurs Stud 2013; 50 (12) 1720-1726
    CrossrefPubMedGoogle Scholar
  • 24 Thompson C. A conceptual treadmill: the need for ‘middle ground’ in clinical decision making theory in nursing. J Adv Nurs 1999; 30 (05) 1222-1229
    CrossrefPubMedGoogle Scholar
  • 25 Pretz JE, Folse VN. Nursing experience and preference for intuition in decision making. J Clin Nurs 2011; 20 (19-20): 2878-2889
    CrossrefPubMedGoogle Scholar
  • 26 Chilcote DR. Intuition: a concept analysis. Nurs Forum 2017; 52 (01) 62-67
    CrossrefPubMedGoogle Scholar
  • 27 Kawamoto K, Del Fiol G. Clinical decision support systems in healthcare. In: Nelson R, Staggers N. eds. Health Informatics: An Interprofessional Approach. 2nd ed.. Elsevier; 2016: 170-183
    Google Scholar
  • 28 Osheroff JA, Teich JM, Levick D. et al. Improving Outcomes with Clinical Decision Support: An Implementer's Guide. 2nd ed.. HIMSS; 2012
    CrossrefGoogle Scholar
  • 29 Clarke MA, Belden JL, Koopman RJ. et al. Information needs and information-seeking behaviour analysis of primary care physicians and nurses: a literature review. Health Info Libr J 2013; 30 (03) 178-190
    CrossrefPubMedGoogle Scholar
  • 30 Kilsdonk E, Peute LW, Jaspers MWM. Factors influencing implementation success of guideline-based clinical decision support systems: a systematic review and gaps analysis. Int J Med Inform 2017; 98: 56-64
    CrossrefPubMedGoogle Scholar
  • 31 Farokhzadian J, Khajouei R, Ahmadian L. Information seeking and retrieval skills of nurses: nurses readiness for evidence based practice in hospitals of a medical university in Iran. Int J Med Inform 2015; 84 (08) 570-577
    CrossrefPubMedGoogle Scholar
  • 32 O'leary DF, Mhaolrúnaigh SN. Information-seeking behaviour of nurses: where is information sought and what processes are followed?. J Adv Nurs 2012; 68 (02) 379-390
    CrossrefPubMedGoogle Scholar
  • 33 Camargo FC, Iwamoto HH, Galvão CM, Pereira GA, Andrade RB, Masso GC. Competences and barriers for the evidence-based practice in nursing: an integrative review. Rev Bras Enferm 2018; 71 (04) 2030-2038
    CrossrefPubMedGoogle Scholar
  • 34 Han Y, Zeng H, Jiang H. et al. CSC expert consensus on principles of clinical management of patients with severe emergent cardiovascular diseases during the COVID-19 epidemic. Circulation 2020; 141 (20) e810-e816
    PubMedGoogle Scholar
  • 35 Alford D, Alexander S, Barr R. Optimization of clinical decision support tools for the care of older adults with diabetes mellitus type 2. Comput Inform Nurs 2018; 36 (06) 259-264
    CrossrefPubMedGoogle Scholar
  • 36 Jeffery AD, Novak LL, Kennedy B, Dietrich MS, Mion LC. Participatory design of probability-based decision support tools for in-hospital nurses. J Am Med Inform Assoc 2017; 24 (06) 1102-1110
    CrossrefPubMedGoogle Scholar
  • 37 Brunner J, Chuang E, Goldzweig C, Cain CL, Sugar C, Yano EM. User-centered design to improve clinical decision support in primary care. Int J Med Inform 2017; 104 (March): 56-64
    CrossrefPubMedGoogle Scholar
  • 38 Cho I, Staggers N, Park I. Nurses' responses to differing amounts and information content in a diagnostic computer-based decision support application. Comput Inform Nurs 2010; 28 (02) 95-102
    CrossrefPubMedGoogle Scholar
  • 39 Cvach M. Monitor alarm fatigue: an integrative review. Biomed Instrum Technol 2012; 46 (04) 268-277
    CrossrefPubMedGoogle Scholar
  • 40 Greenes RA, Bates DW, Kawamoto K, Middleton B, Osheroff J, Shahar Y. Clinical decision support models and frameworks: Seeking to address research issues underlying implementation successes and failures. J Biomed Inform 2018; 78 (78) 134-143
    CrossrefPubMedGoogle Scholar
  • 41 Freimuth RR, Formea CM, Hoffman JM, Matey E, Peterson JF, Boyce RD. Implementing genomic clinical decision support for drug-based precision medicine. CPT Pharmacometrics Syst Pharmacol 2017; 6 (03) 153-155
    CrossrefPubMedGoogle Scholar
  • 42 World Health Organization. Multimorbidity: Technical Series on Safer Primary Care. 2016 Accessed October 30, 2019 at: http://apps.who.int/bookorders
    PubMedGoogle Scholar
  • 43 Fiset VJ, Graham ID, Davies BL. Evidence-based practice in clinical nursing education: a scoping review. J Nurs Educ 2017; 56 (09) 534-541
    CrossrefPubMedGoogle Scholar
  • 44 Carey S, Hogan S. Failure in systems and culture: barriers that prevent implementation of evidence-based fasting times for patients in the acute care setting. J Parenter Enter Nutr 2021; 45 (05) 933-940
    CrossrefPubMedGoogle Scholar
  • 45 Michalowski M, Wilk S, Michalowski W, Carrier M. Mitplan: a planning approach to mitigating concurrently applied clinical practice guidelines. In: Riaño D, Wilk S, ten Teije A. eds. Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science. Vol. 11526. Springer; 2019: 93-103
    Google Scholar
  • 46 Wilk S, Michalowski M, Michalowski W, Rosu D, Carrier M, Kezadri-Hamiaz M. Comprehensive mitigation framework for concurrent application of multiple clinical practice guidelines. J Biomed Inform 2017; 66: 52-71
    CrossrefPubMedGoogle Scholar
  • 47 Prentzas J, Hatzilygeroudis I. Categorizing approaches combining rule-based and case-based reasoning. Expert Syst 2007; 24 (02) 97-122
    CrossrefPubMedGoogle Scholar
  • 48 Yu PP. Knowledge bases, clinical decision support systems, and rapid learning in oncology. J Oncol Pract 2015; 11 (02) e206-e211
    CrossrefPubMedGoogle Scholar
  • 49 Battisto D, Pak R, Vander Wood MA, Pilcher JJ. Using a task analysis to describe nursing work in acute care patient environments. J Nurs Adm 2009; 39 (12) 537-547
    CrossrefPubMedGoogle Scholar
  • 50 Douglas S, Cartmill R, Brown R. et al. The work of adult and pediatric intensive care unit nurses. Nurs Res 2013; 62 (01) 50-58
    CrossrefPubMedGoogle Scholar
  • 51 Whelton PK, Carey RM, Aronow WS. et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults a report of the American College of Cardiology/American Heart Association Task Force on clinical pr. Hypertension 2018; 71 (06) e13-e115
    PubMedGoogle Scholar
  • 52 Tsutsui H, Isobe M, Ito H. et al; Japanese Circulation Society and the Japanese Heart Failure Society Joint Working Group. JCS 2017/JHFS 2017 guideline on diagnosis and treatment of acute and chronic heart failure. Circ J 2019; 83 (10) 2084-2184
    CrossrefPubMedGoogle Scholar
  • 53 Munshi MN, Florez H, Huang ES. et al. Management of diabetes in long-term care and skilled nursing facilities: a position statement of the American diabetes association. Diabetes Care 2016; 39 (02) 308-318
    CrossrefPubMedGoogle Scholar
  • 54 Herdman TH, Kamitsuru S. eds. NANDA International Nursing Diagnoses: Definitions & Classification 2018–2020. 7th ed.. Noida: Thieme Medical Publishers; 2017
    CrossrefGoogle Scholar
  • 55 Brush Jr JE, Handberg EM, Biga C. et al. 2015 ACC health policy statement on cardiovascular team-based care and the role of advanced practice providers. J Am Coll Cardiol 2015; 65 (19) 2118-2136
    CrossrefPubMedGoogle Scholar
  • 56 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 (01) 17
    CrossrefPubMedGoogle Scholar
  • 57 Westra BL, Christie B, Johnson SG. et al. Modeling flowsheet data to support secondary use. Comput Inform Nurs 2017; 35 (09) 452-458
    CrossrefPubMedGoogle Scholar
  • 58 O'Neill ES, Dluhy NM, Chin E. Modeling novice clinical reasoning for a computerized decision support system. J Adv Nurs 2005; 49 (01) 67-77
    CrossrefPubMedGoogle Scholar
  • 59 Tacia L, Biskupski K, Pheley A, Lehto RH. Identifying barriers to evidence-based practice adoption: a focus group study. Clin Nurs Stud 2015; 3 (02) 90-96
    PubMedGoogle Scholar
  • 60 Harrison RL, Lyerla F. Using nursing clinical decision support systems to achieve meaningful use. Comput Inform Nurs 2012; 30 (07) 380-385
    CrossrefPubMedGoogle Scholar
  • 61 Karlsson LO, Nilsson S, Bång M, Nilsson L, Charitakis E, Janzon M. A clinical decision support tool for improving adherence to guidelines on anticoagulant therapy in patients with atrial fibrillation at risk of stroke: a cluster-randomized trial in a Swedish primary care setting (the CDS-AF study). PLoS Med 2018; 15 (03) e1002528
    CrossrefPubMedGoogle Scholar
  • 62 Stojkovska LA, Loskovska S. Clinical decision support systems: medical knowledge acquisition and representation methods. Paper presented at: 2010 IEEE Int Conf Electro/Information Technol EIT2010. Published online 2010: 0-5
    PubMedGoogle Scholar
  • 63 Vernooij RWM, Sanabria AJ, Solà I, Alonso-Coello P, Martínez García L. Guidance for updating clinical practice guidelines: a systematic review of methodological handbooks. Implement Sci 2014; 9 (01) 3
    CrossrefPubMedGoogle Scholar
  • 64 Castillo RS, Kelemen A. Considerations for a successful clinical decision support system. Comput Inform Nurs 2013; 31 (07) 319-326
    CrossrefPubMedGoogle Scholar
  • 65 Cresswell KM, Lee L, Mozaffar H, Williams R, Sheikh A. NIHR ePrescribing Programme Team. Sustained user engagement in health information technology: the long road from implementation to system optimization of computerized physician order entry and clinical decision support systems for prescribing in hospitals in England. Health Serv Res 2017; 52 (05) 1928-1957
    CrossrefPubMedGoogle Scholar
  • 66 Brunner MC, Sheehan SE, Yanke EM. et al. Joint design with providers of clinical decision support for value-based advanced shoulder imaging. Appl Clin Inform 2020; 11 (01) 142-152
    Article in Thieme ConnectPubMedGoogle Scholar