Why use computerized dss

Manag Sci. User acceptance of information technology: toward a unified view. Timmermans S, Berg M. The practice of medical technology. Sociol Health Illn. ISS — Why is it difficult to implement e-health initiatives? A qualitative study. Nicolini D. The work to make telemedicine work: a social and articulative view. Soc Sci Med. Adoption of clinical decision support systems in a developing country: antecedents and outcomes of physician's threat to perceived professional autonomy.

Int J Med Inform. Ten key considerations for the successful implementation and adoption of large-scale health information technology. What may help or hinder the implementation of computerized decision support systems CDSSs : a focus group study with physicians. Berg M. Rationalizing medical work: decision-support techniques and medical practices.

Cambridge: MIT press; Pope C. Resisting evidence: the study of evidence-based medicine as a contemporary social movement. Acad Med. Barriers, facilitators and views about next steps to implementing supports for evidence-informed decision-making in health systems: a qualitative study.

Grol R. Successes and failures in the implementation of evidence-based guidelines for clinical practice. Med Care. Charmaz K. Constructing grounded theory: a practical guide through qualitative analysis.

London: Sage; Implementing an evidence-based computerized decision support system to improve patient care in a general hospital: the CODES study protocol for a randomized controlled trial. Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients: the ONCO-CODES study protocol for a randomized controlled trial. Glaser B, Strauss A. The discovery of grounded theory. Hawthorne: Aldine Publishing Company; Implementation of multiple-domain covering computerized decision support systems in primary care: a focus group study on perceived barriers.

Wenger E. Communities of practice: Learning, meaning, and identity. Cambridge: Cambridge University Press; Book Google Scholar. Nilsen P. Making sense of implementation theories, models and frameworks. Understanding the implementation of complex interventions in health care: the normalization process model. Nielsen J. Usability engineering.

Boston: Academic Press; Triberti S, Riva G. Engaging users to design positive technologies for patient engagement: the perfect interaction model. Berlin: De Gruyter Open; Task-technology fit and individual performance. The GOMS family of user interface analysis techniques: comparison and contrast. Greenberg J. Organizational justice: yesterday, today, and tomorrow. J Manag. Freedman AM. The Wiley-Blackwell handbook of the psychology of leadership, change and organizational development.

Examining trust in information technology artifacts: the effects of system quality and culture. J Manag Inf Syst. Development of a theory of implementation and integration: Normalization Process Theory. Weick KE. Technology as equivoque: sensemaking in new technologies. Technology and Organizations. San Francisco: Jossey-Bass; Technological frames: making sense of information technology in organizations. Carayon P. Sociotechnical systems approach to healthcare quality and patient safety.

Work Reading, Mass. Greenhalgh T, Stones R. Theorising big IT programmes in healthcare: strong structuration theory meets actor-network theory. Soc Sci Med Providing doctors with high-quality information: an updated evaluation of web-based point-of-care information summaries. A review of online evidence-based practice point-of-care information summary providers. Download references. Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Box , , Beersheva, Israel. You can also search for this author in PubMed Google Scholar. All authors contributed to the conception and design of the study. All authors contributed to the editing and final approval of the protocol. Correspondence to Elisa G.

Four local ethical committees names omitted to preserve anonymity have approved the study and consent to participate was obtained by all participants. Lorenzo Moja provides consultancies to knowledge providers in relation to point-of-care information services and computer decision support systems.

This is done on a pro-bono basis; it does not include consultancy fees nor any form of remuneration. The other authors declare that they have no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Reprints and Permissions. Liberati, E. What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implementation Sci 12, OR letter. OR editorial. Article Google Scholar. A critical appraisal of research. Ann Intern Med. Fleiss J: Statistical methods for rates and proportions. Google Scholar. Control Clin Trials.

Download references. Garg lhsc. Nieuwlaat phri. You can also search for this author in PubMed Google Scholar. Correspondence to R Brian Haynes. This paper is based on the protocol submitted for peer review funding. All authors read and approved the final manuscript. This article is published under license to BioMed Central Ltd. Reprints and Permissions. Haynes, R. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: Methods of a decision-maker-researcher partnership systematic review.

Implementation Sci 5, 12 Download citation. Received : 04 December Accepted : 05 February Published : 05 February Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative.

Skip to main content. Search all BMC articles Search. Download PDF. Abstract Background Computerized clinical decision support systems are information technology-based systems designed to improve clinical decision-making. Results Data will be summarized using descriptive summary measures, including proportions for categorical variables and means for continuous variables. Conclusion A decision-maker-researcher partnership provides a model for systematic reviews that may foster knowledge translation and uptake.

Background Computerized clinical decision support systems CCDSSs are information technology-based systems designed to improve clinical decision-making. Methods Steps involved in conducting this update are shown in Figure 1. Figure 1. Flow diagram of steps involved in conducting this review. Full size image.

Table 1 Name and position of decision-makers providing overall direction Full size table. Table 2 Name and position of decisions makers for each of the six clinical application areas Full size table.

Figure 2. Conclusion A decision-maker-researcher partnership provides a model for systematic reviews that may foster KT and uptake. Support for CDSS continues to mount in the age of the electronic medical record, and there are still more advances to be made including interoperability, speed and ease of deployment, and affordability. At the same time, we must stay vigilant for potential downfalls of CDSS, which range from simply not working and wasting resources, to fatiguing providers and compromising quality of patient care.

Extra precautions and conscientious design must be taken when building, implementing, and maintaining CDSS. A portion of these considerations were covered in this review, but further review will be required in practice, especially as CDSS continue to evolve in complexity through advances in AI, interoperability, and new sources of data. Osheroff, J. Sim, I. Clinical decision support systems for the practice of evidence-based medicine.

Med Inf. De Dombal, F. Computers, diagnoses and patients with acute abdominal pain. Shortliffe, E. A model of inexact resoning in medicine. Google Scholar. Middleton, B. Clinical decision support: a 25 year retrospective and a 25 year vision. Article Google Scholar. Dias, D. Wearable health devices—vital sign monitoring, systems and technologies. Berner, E. HIMS, Deo, R. Machine learning in medicine. Circulation , — Electronic medical record adoption model requirements.

Chang, F. Canadian Family Physician. Nationwide citizen access to their health data: analysing and comparing experiences in Denmark, Estonia and Australia. Omididan, Z. The role of clinical decision support systems in healthcare : a systematic review study. Jentashapir Sci.

Kabane, S. Medical Information Science Reference Vonbach, P. Prevalence of drug-drug interactions at hospital entry and during hospital stay of patients in internal medicine. PubMed Article Google Scholar. Helmons, P. Drug-drug interaction checking assisted by clinical decision support: a return on investment analysis. Koutkias, V. Contributions from the Literature on Clinical Decision Support.

Phansalkar, S. High-priority drug — drug interactions for use in electronic health records. Cornu, P. 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. McEvoy, D. Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. Cho, I. National rules for drug-drug interactions: are they appropriate for tertiary hospitals?.

Korean Med. Mahoney, C. Effects of an integrated clinical information system on medication safety in a multi-hospital setting. Health Syst. Peris-Lopez, P. A comprehensive RFID solution to enhance inpatient medication safety.

Levtzion-korach, O. Effect of bar-code technology on the safety of medication administration. Association between workarounds and medication administration errors in bar-code-assisted medication administration in hospitals. Eslami, S. Effects of two different levels of computerized decision support on blood glucose regulation in critically ill patients.

Int J. Jia, P. The effects of clinical decision support systems on medication safety: an overview. Kwok, R. Improving adherence to asthma clinical guidelines and discharge documentation from emergency departments: Implementation of a dynamic and integrated electronic decision support system.

PubMed Google Scholar. Davis, D. Translating guidelines into practice: a systematic review of theoretic concepts, practical experience and research evidence in the adoption of clinical practice guidelines. CAS Google Scholar. Michael, C. Shortliffe, T. Medical thinking: what should we do? Lipton, J. 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. Salem, H. A multicentre integration of a computer led follow up in surgical oncology is valid and safe. BJU Int. Embi, P. Development of an electronic health record-based Clinical Trial Alert system to enhance recruitment at the point of care. AMIA Annu. Calloway, S. Impact of a clinical decision support system on pharmacy clinical interventions, documentation efforts, and costs. McMullin, S. Impact of an evidence-based computerized decision support system on primary care prescription costs.

Algaze, C. Use of a checklist and clinical decision support tool reduces laboratory use and improves cost. Pediatrics , e Pruszydlo, M. Development and evaluation of a computerised clinical decision support system for switching drugs at the interface between primary and tertiary care. BMC Med. Bell, C. A decision support tool for using an ICD anatomographer to address admission coding inaccuracies: a commentary. Online J. Public Health Inform. Haberman, S. Effect of clinical-decision support on documentation compliance in an electronic medical record.

Turchin, A. NLP for patient safety: splenectomy and pneumovax. In Proc. Enhancing problem list documentation in electronic health records using two methods: the example of prior splenectomy. BMJ Qual Saf. Berner E. Segal, M. Experience with integrating diagnostic decision support software with electronic health records: benefits versus risks of information sharing.

Since a DSS is essentially an application, it can be loaded on most computer systems, whether on desktops or laptops. Certain DSS applications are also available through mobile devices. The flexibility of the DSS is extremely beneficial for users who travel frequently.

This gives them the opportunity to be well-informed at all times, providing the ability to make the best decisions for their company and customers on the go or even on the spot. In organizations, a decision support system DSS analyzes and synthesizes vast amounts of data to assist in decision-making. With this information, it produces reports that may project revenue, sales, or manage inventory. Many different industries, from medicine to agriculture, use decision support systems.

To help diagnose a patient, a medical clinician may use a computerized decision support system for diagnostics and prescription. Combining clinician inputs and previous electronic health records, a decision support system may assist a doctor in diagnosing a patient.

Broadly speaking, decision support systems help in making more informed decisions. Often used by upper and mid-level management, decision support systems are used to make actionable decisions, or produce multiple possible outcomes based on current and historical company data. At the same time, decision support systems can be used to produce reports for customers that are easily digestible and can be adjusted based on user specifications.

Small Business. Company Profiles. Business Essentials. Financial Analysis. Your Privacy Rights. To change or withdraw your consent choices for Investopedia. At any time, you can update your settings through the "EU Privacy" link at the bottom of any page.