scholarly journals The role of digital clinical decision support tool in improving quality of intrapartum and postpartum care: experiences from two states of India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Gulnoza Usmanova ◽  
Kamlesh Lalchandani ◽  
Ashish Srivastava ◽  
Chandra Shekhar Joshi ◽  
Deepak Chandra Bhatt ◽  
...  
Keyword(s):  
Time Series ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Interrupted Time Series ◽  
Clinical Decision ◽  
Clinical Practices ◽  
The Impact

Abstract Background Computerized clinical decision support (CDSS) –digital information systems designed to improve clinical decision making by providers – is a promising tool for improving quality of care. This study aims to understand the uptake of ASMAN application (defined as completeness of electronic case sheets), the role of CDSS in improving adherence to key clinical practices and delivery outcomes. Methods We have conducted secondary analysis of program data (government data) collected from 81 public facilities across four districts each in two sates of Madhya Pradesh and Rajasthan. The data collected between August –October 2017 (baseline) and the data collected between December 2019 – March 2020 (latest) was analysed. The data sources included: digitized labour room registers, case sheets, referral and discharge summary forms, observation checklist and complication format. Descriptive, univariate and multivariate and interrupted time series regression analyses were conducted. Results The completeness of electronic case sheets was low at postpartum period (40.5%), and in facilities with more than 300 deliveries a month (20.9%). In multivariate logistic regression analysis, the introduction of technology yielded significant improvement in adherence to key clinical practices. We have observed reduction in fresh still births rates and asphyxia, but these results were not statistically significant in interrupted time series analysis. However, our analysis showed that identification of maternal complications has increased over the period of program implementation and at the same time referral outs decreased. Conclusions Our study indicates CDSS has a potential to improve quality of intrapartum care and delivery outcome. Future studies with rigorous study design is required to understand the impact of technology in improving quality of maternity care.

The Role of Digital Clinical Decision Support Tool in Improving Quality of Intrapartum and Postpartum Care: Experiences From Two States of India.

2020 ◽  
Author(s):  
Gulnoza Usmanova ◽  
Kamlesh Lalchandani ◽  
Ashish Srivast ◽  
Chandra Joshi ◽  
Deepak Bhatt ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Multivariate Logistic Regression Analysis ◽  
Clinical Decision ◽  
Interrupted Time Series Analysis ◽  
Clinical Practices ◽  
The Impact

Abstract Background: Computerized clinical decision support (CDSS) –digital information systems designed to improve clinical decision making by providers – are a promising tool for improving quality of care. This study aims to understand the uptake of ASMAN application (defined as completeness of electronic case sheets), the role of CDSS in improving adherence to key clinical practices and delivery outcomes. Methods: We have conducted secondary analysis of program data (government data) collected from 81 public facilities across four districts each in two sates of Madhya Pradesh and Rajasthan. The data collected between August –October 2017 (baseline) and the data collected between December 2019 – March 2020 was analysed (latest). Results: The completeness of electronic case sheets was low at postpartum period (40.5%), and in facilities with more than 300 deliveries a month (20.9%). In multivariate logistic regression analysis, the introduction of technology yielded to significant improvement in adherence to key clinical practices. We have observed reduction in fresh still births rates and asphyxia, but these results were not statistically significant in interrupted time series analysis. However, our analysis showed that identification of maternal complications has increased over the period of program implementation and at the same time referral outs decreased. Conclusions: Our study indicates CDSS has a potential to improve quality of intrapartum care and delivery outcome. Future studies with rigorous study design is required to understand the impact of technology in improving quality of maternity care.

scholarly journals A Narrative Review of Clinical Decision Support for Inpatient Clinical Pharmacists

2021 ◽  
Vol 12 (02) ◽  
pp. 199-207
Author(s):  
Liang Yan ◽  
Thomas Reese ◽  
Scott D. Nelson
Keyword(s):  
Patient Safety ◽  
Quality Of Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Controlled Trials ◽  
Clinical Pharmacists ◽  
Randomized Controlled ◽  
The Impact

Abstract Objective Increasingly, pharmacists provide team-based care that impacts patient care; however, the extent of recent clinical decision support (CDS), targeted to support the evolving roles of pharmacists, is unknown. Our objective was to evaluate the literature to understand the impact of clinical pharmacists using CDS. Methods We searched MEDLINE, EMBASE, and Cochrane Central for randomized controlled trials, nonrandomized trials, and quasi-experimental studies which evaluated CDS tools that were developed for inpatient pharmacists as a target user. The primary outcome of our analysis was the impact of CDS on patient safety, quality use of medication, and quality of care. Outcomes were scored as positive, negative, or neutral. The secondary outcome was the proportion of CDS developed for tasks other than medication order verification. Study quality was assessed using the Newcastle–Ottawa Scale. Results Of 4,365 potentially relevant articles, 15 were included. Five studies were randomized controlled trials. All included studies were rated as good quality. Of the studies evaluating inpatient pharmacists using a CDS tool, four showed significantly improved quality use of medications, four showed significantly improved patient safety, and three showed significantly improved quality of care. Six studies (40%) supported expanded roles of clinical pharmacists. Conclusion These results suggest that CDS can support clinical inpatient pharmacists in preventing medication errors and optimizing pharmacotherapy. Moreover, an increasing number of CDS tools have been developed for pharmacists' roles outside of order verification, whereby further supporting and establishing pharmacists as leaders in safe and effective pharmacotherapy.

scholarly journals Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice–Aided Diagnosis: Interrupted Time Series Study (Preprint)

2019 ◽  
Author(s):  
Liyuan Tao ◽  
Chen Zhang ◽  
Lin Zeng ◽  
Shengrong Zhu ◽  
Nan Li ◽  
...  
Keyword(s):  
Time Series ◽  
Logistic Regression ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Clinical Decision Support Systems ◽  
Interrupted Time Series ◽  
Clinical Decision ◽  
Interrupted Time Series Analysis ◽  
Aided Diagnosis

BACKGROUND Clinical decision support systems (CDSS) are an integral component of health information technologies and can assist disease interpretation, diagnosis, treatment, and prognosis. However, the utility of CDSS in the clinic remains controversial. OBJECTIVE The aim is to assess the effects of CDSS integrated with British Medical Journal (BMJ) Best Practice–aided diagnosis in real-world research. METHODS This was a retrospective, longitudinal observational study using routinely collected clinical diagnosis data from electronic medical records. A total of 34,113 hospitalized patient records were successively selected from December 2016 to February 2019 in six clinical departments. The diagnostic accuracy of the CDSS was verified before its implementation. A self-controlled comparison was then applied to detect the effects of CDSS implementation. Multivariable logistic regression and single-group interrupted time series analysis were used to explore the effects of CDSS. The sensitivity analysis was conducted using the subgroup data from January 2018 to February 2019. RESULTS The total accuracy rates of the recommended diagnosis from CDSS were 75.46% in the first-rank diagnosis, 83.94% in the top-2 diagnosis, and 87.53% in the top-3 diagnosis in the data before CDSS implementation. Higher consistency was observed between admission and discharge diagnoses, shorter confirmed diagnosis times, and shorter hospitalization days after the CDSS implementation (all <italic>P</italic>&lt;.001). Multivariable logistic regression analysis showed that the consistency rates after CDSS implementation (OR 1.078, 95% CI 1.015-1.144) and the proportion of hospitalization time 7 days or less (OR 1.688, 95% CI 1.592-1.789) both increased. The interrupted time series analysis showed that the consistency rates significantly increased by 6.722% (95% CI 2.433%-11.012%, <italic>P</italic>=.002) after CDSS implementation. The proportion of hospitalization time 7 days or less significantly increased by 7.837% (95% CI 1.798%-13.876%, <italic>P</italic>=.01). Similar results were obtained in the subgroup analysis. CONCLUSIONS The CDSS integrated with BMJ Best Practice improved the accuracy of clinicians’ diagnoses. Shorter confirmed diagnosis times and hospitalization days were also found to be associated with CDSS implementation in retrospective real-world studies. These findings highlight the utility of artificial intelligence-based CDSS to improve diagnosis efficiency, but these results require confirmation in future randomized controlled trials.

scholarly journals Measuring the Impact of Diagnostic Decision Support on the Quality of Clinical Decision Making: Development of a Reliable and Valid Composite Score

2003 ◽  
Vol 10 (6) ◽  
pp. 563-572 ◽  
Author(s):  
Padmanabhan Ramnarayan ◽  
Ritika R. Kapoor ◽  
Michael Coren ◽  
Vasantha Nanduri ◽  
Amanda L. Tomlinson ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Composite Score ◽  
Diagnostic Decision ◽  
The Impact

Decision Support

2019 ◽  
pp. 180-199
Author(s):  
Christoph U. Lehmann ◽  
Karl E. Misulis ◽  
Mark E. Frisse
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Medical Errors ◽  
Clinical Decision Making ◽  
Adverse Drug Events ◽  
Clinical Decision ◽  
Information Resources ◽  
Clinical Practices

Decision support is a broad technique that seeks to bring information to bear at the time a clinician is taking actions that are driven by other data. Clinical decision-making methodology depends on the complexity of the patient’s case, the certainty of a diagnosis, available treatment and diagnostic resources, reliability of information resources, training of the clinician, and psychological makeup of the clinician. Most clinical decision support efforts seek to improve workflow, enforce best clinical practices, or mitigate adverse drug events. Clinical decision support can reduce medical errors, improve nutrition, prevent orders on the wrong patient, and reduce costs. Clinical and administrative decision support can lead to more effective outcomes, improved quality, and lower costs.

scholarly journals The use of computerized clinical decision support systems in emergency care: a substantive review of the literature

2016 ◽  
Vol 24 (3) ◽  
pp. 655-668 ◽  
Author(s):  
Paula Bennett ◽  
Nicholas R Hardiker
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Emergency Care ◽  
Support Systems ◽  
Positive Impact ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Objectives: This paper provides a substantive review of international literature evaluating the impact of computerized clinical decision support systems (CCDSSs) on the care of emergency department (ED) patients. Material and Methods: A literature search was conducted using Medline, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Embase electronic resources, and gray literature. Studies were selected if they compared the use of a CCDSS with usual care in a face-to-face clinical interaction in an ED. Results: Of the 23 studies included, approximately half demonstrated a statistically significant positive impact on aspects of clinical care with the use of CCDSSs. The remaining studies showed small improvements, mainly around documentation. However, the methodological quality of the studies was poor, with few or no controls to mitigate against confounding variables. The risk of bias was high in all but 6 studies. Discussion: The ED environment is complex and does not lend itself to robust quantitative designs such as randomized controlled trials. The quality of the research in ∼75% of the studies was poor, and therefore conclusions cannot be drawn from these results. However, the studies with a more robust design show evidence of the positive impact of CCDSSs on ED patient care. Conclusion: This is the first review to consider the role of CCDSSs in emergency care and expose the research in this area. The role of CCDSSs in emergency care may provide some solutions to the current challenges in EDs, but further high-quality research is needed to better understand what technological solutions can offer clinicians and patients.

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