Clinical decision support for drug related events: Moving towards better prevention

Kane‐Gill, Sandra L.; Achanta, Archita; Kellum, John A.; Handler, Steven M.

doi:10.5492/wjccm.v5.i4.204

Cited by 16 publications

(13 citation statements)

References 64 publications

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“…The panelists also agreed that features need to include prompts and abilities to make entries related to patient assessment like presenting symptoms, vital signs, laboratory and imaging reports, co-morbidities, prescription and non-prescription medications, allergies, and adverse drug reactions. Such features might improve diagnostic accuracy [35, 42, 48, 51], effectiveness of medication review and patient care [36–38, 46], improve screening for allergies to medications [34], reduced adverse drug reactions [33], drug-drug, and drug-food interactions [4, 49, 50]. The panelists also agreed that the system should provide prompts to specifying doses of the medications prescribed, frequencies, routes of administration, dosage forms, dosing units, alternative medications, and duration of therapy.…”

Section: Discussionmentioning

confidence: 99%

Merits, features, and desiderata to be considered when developing electronic health records with embedded clinical decision support systems in Palestinian hospitals: a consensus study

Shawahna

2019

BMC Med Inform Decis Mak

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BackgroundElectronic health records (EHRs) with embedded clinical decision support systems (CDSSs) have the potential to improve healthcare delivery. This study was conducted to explore merits, features, and desiderata to be considered when planning for, designing, developing, implementing, piloting, evaluating, maintaining, upgrading, and/or using EHRs with CDSSs.MethodsA mixed-method combining the Delphi technique and Analytic Hierarchy Process was used. Potentially important items were collected after a thorough search of the literature and from interviews with key contact experts (n = 19). Opinions and views of the 76 panelists on the use of EHRs were also explored. Iterative Delphi rounds were conducted to achieve consensus on 122 potentially important items by a panel of 76 participants. Items on which consensus was achieved were ranked in the order of their importance using the Analytic Hierarchy Process.ResultsOf the 122 potentially important items presented to the panelists in the Delphi rounds, consensus was achieved on 110 (90.2%) items. Of these, 16 (14.5%) items were related to the demographic characteristics of the patient, 16 (14.5%) were related to prescribing medications, 16 (14.5%) were related to checking prescriptions and alerts, 14 (12.7%) items were related to the patient’s identity, 13 (11.8%) items were related to patient assessment, 12 (10.9%) items were related to the quality of alerts, 11 (10%) items were related to admission and discharge of the patient, 9 (8.2%) items were general features, and 3 (2.7%) items were related to diseases and making diagnosis.ConclusionsIn this study, merits, features, and desiderata to be considered when planning for, designing, developing, implementing, piloting, evaluating, maintaining, upgrading, and/or using EHRs with CDSSs were explored. Considering items on which consensus was achieved might promote congruence and safe use of EHRs. Further studies are still needed to determine if these recommendations can improve patient safety and outcomes in Palestinian hospitals.

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Section: Discussionmentioning

confidence: 99%

Merits, features, and desiderata to be considered when developing electronic health records with embedded clinical decision support systems in Palestinian hospitals: a consensus study

Shawahna

2019

BMC Med Inform Decis Mak

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“…This flows in part from work designed to detect abnormal patient states or events and to communicate the need for action to decision-makers. For example, work to detect and even predict adverse drug reactions can help to improve quality by reducing the negative consequences of clinical care [36]. Automated techniques for knowledge discovery that can inform CDS work to improve medication safety also emphasize quality improvement [37].…”

Section: Quality Improvementmentioning

confidence: 99%

Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016

Jenders¹

2017

Yearb Med Inform

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SummaryIntroduction: Advances in clinical decision support (CDS) continue to evolve to support the goals of clinicians, policymakers, patients and professional organizations to improve clinical practice, patient safety, and the quality of care. Objectives: Identify key thematic areas or foci in research and practice involving clinical decision support during the 2015-2016 time period. Methods: Thematic analysis consistent with a grounded theory approach was applied in a targeted review of journal publications, the proceedings of key scientific conferences as well as activities in standards development organizations in order to identify the key themes underlying work related to CDS. Results: Ten key thematic areas were identified, including: 1) an emphasis on knowledge representation, with a focus on clinical practice guidelines; 2) various aspects of precision medicine, including the use of sensor and genomic data as well as big data; 3) efforts in quality improvement; 4) innovative uses of computer-based provider order entry (CPOE) systems, including relevant data displays; 5) expansion of CDS in various clinical settings; 6) patient-directed CDS; 7) understanding the potential negative impact of CDS; 8) obtaining structured data to drive CDS interventions; 9) the use of diagnostic decision support; and 10) the development and use of standards for CDS. Conclusions: Active research and practice in 2015-2016 continue to underscore the importance and broad utility of CDS for effecting change and improving the quality and outcome of clinical care. KeywordsClinical decision support, quality improvement, medical order entry systems Yearb Med Inform 2017:125-32 http://dx

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“…Past literature posited that PASS could provide high-quality alerts for users [6] and is an effective approach to improve prescribing behavior [7]. Compared with other HISs (eg, electronic medical record [EMR] and HIS), PASS could directly help in the avoidance of adverse drug reactions (ADRs) and adverse drug events (ADEs) and in the reduction of high medical treatment costs caused by inappropriate drug usage [1,8]. Considering the benefits of PASS, many hospitals have already adopted this system.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of the Prescription Automatic Screening System in Health Care Services: Quasi-Experiment

Guo

Hsu

et al. 2019

JMIR Med Inform

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Background Hospitals have deployed various types of technologies to alleviate the problem of high medical costs. The cost of pharmaceuticals is one of the main drivers of medical costs. The Prescription Automatic Screening System (PASS) aims to monitor physicians’ prescribing behavior, which has the potential to decrease prescription errors and medical treatment costs. However, a substantial number of cases with unsatisfactory results related to the effects of PASS have been noted. Objective The objectives of this study were to systematically explore the imperative role of PASS on hospitals’ prescription errors and medical treatment costs and examine its contingency factors to clarify the various factors associated with the effective use of PASS. Methods To systematically examine the various effects of PASS, we adopted a quasi-experiment methodology by using a 2-year observation dataset from 2 hospitals in China. We then analyzed the data related to physicians’ prescriptions both before and after the deployment of PASS and eliminated influences from a variety of perplexing factors by utilizing a control hospital that did not use a PASS system. In total, 754 physicians were included in this experiment comprising 11,054 patients: 400 physicians in the treatment group and 354 physicians in the control group. This study was also preceded by a series of interviews, which were employed to identify moderators. Thereafter, we adopted propensity score matching integrated with difference-in-differences to isolate the effects of PASS. Results The effects of PASS on prescription errors and medical treatment costs were all significant (error: 95% CI –0.40 to –0.11, P =.001; costs: 95% CI –0.75 to –0.12, P =.007). Pressure from organizational rules and workload decreased the effect of PASS on prescription errors (95% CI 0.18-0.39; P <.001) and medical treatment costs (95% CI 0.07-0.55; P =.01), respectively. We also suspected that other pressures (eg, clinical title and risk categories of illness) also impaired physicians’ attention to alerts from PASS. However, the effects of PASS did not change among physicians with a higher clinical title or when treating diseases demonstrating high risk. This may be attributed to the fact that these physicians will focus more on their patients in these situations, regardless of having access to an intelligent system. Conclusions Although implementation of PASS decreases prescription errors and medical treatment costs, workload and organizational rules remain problematic, as they tend to impair the positive effects of auxiliary diagnosis systems on performance. This again highlights the importance of considering both technical and organizational issues to obtain the highest level of effectiveness when deploying information technology in hospitals.

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Clinical decision support for drug related events: Moving towards better prevention

Cited by 16 publications

References 64 publications

Merits, features, and desiderata to be considered when developing electronic health records with embedded clinical decision support systems in Palestinian hospitals: a consensus study

Merits, features, and desiderata to be considered when developing electronic health records with embedded clinical decision support systems in Palestinian hospitals: a consensus study

Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016

Exploring the Impact of the Prescription Automatic Screening System in Health Care Services: Quasi-Experiment

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