Decision Support Tools: Realizing the Potential to Improve Quality of Care

2018 ◽  
Vol 34 (7) ◽  
pp. 821-826 ◽  
Author(s):  
Michelle M. Graham ◽  
Matthew T. James ◽  
John A. Spertus
Keyword(s):  
Quality Of Care ◽  
Decision Support ◽  
Decision Support Tools ◽  
Improve Quality ◽  
Support Tools

scholarly journals Decision-support tools via mobile devices to improve quality of care in primary healthcare settings

2021 ◽  
Vol 2021 (7) ◽  
Author(s):  
Smisha Agarwal ◽  
Claire Glenton ◽  
Tigest Tamrat ◽  
Nicholas Henschke ◽  
Nicola Maayan ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Decision Support ◽  
Mobile Devices ◽  
Primary Healthcare ◽  
Decision Support Tools ◽  
Improve Quality ◽  
Healthcare Settings ◽  
Support Tools

Abstract P263: Computer-Assisted Decision Support in the Treatment of Acute Coronary Syndromes and Impact on Cardiovascular Outcomes

2011 ◽  
Vol 4 (suppl_1) ◽  
Author(s):  
Richard V Milani ◽  
Carl J Lavie ◽  
Daniel P Morin ◽  
Andres Rubiano
Keyword(s):  
Quality Of Care ◽  
Decision Support ◽  
Baseline Risk ◽  
Risk Scores ◽  
Computer Assisted ◽  
Improve Quality ◽  
Consensus Guidelines ◽  
Mortality And Morbidity ◽  
Coronary Syndrome

Background: Evidence from clinical trials and consensus guidelines suggest that in-hospital initiation of key therapeutics can reduce mortality and morbidity in patients admitted with acute coronary syndrome (ACS). As a result, the AHA and ACC have co-developed guideline-based “performance measures” for ACS patients, such that when every measure has been performed, the patient is considered to have achieved optimal or “perfect” care (PC). Computer-assisted decision support (CADS) is a tool that can improve quality of care and is well suited for complex algorithms governing treatment decisions. We sought to determine if CADS tailored to ACS would enhance the likelihood of achieving PC, and whether achievement of PC would translate into reduced mortality. Methods: 452 consecutive patients (mean age 68±13 years) admitted with ACS in 2009 were evaluated (unstable angina 29%, NSTEMI 61%, STEMI 10%). Physicians had the option of using either pre-printed ACS orders (standard orders) versus CADS generated orders. The CADS system utilized patient clinical data including risk scoring, to suggest specific therapeutics and drug dosing based on consensus guidelines. Endpoints were attainment of PC and 30-day mortality. Results: The 77 patients admitted using CADS generated orders were statistically similar (age, gender, ACS diagnosis, TIMI risk) to the 375 patients admitted with the standard order set. Attainment of PC was almost twice as likely when using CADS versus standard orders (84% vs. 44%, p<0.05). PC patients trended towards higher TIMI risk scores (3.2 ±1.7 vs 2.9 ±1.6, p = 0.09) but had half the 30-day mortality (2% vs 4%, p=0.05) compared to patients not achieving PC. Conclusions: Use of CADS in the setting of ACS is feasible and doubles the likelihood of attaining PC. Although patients achieving PC had higher baseline risk, their mortality was reduced by 50% compared to those not achieving PC. These data support the use of CADS in the setting of ACS to improve quality of care and subsequent outcomes.

Interactive versus static decision support tools for COVID-19: An experimental comparison (Preprint)

2021 ◽  
Author(s):  
Alice Röbbelen ◽  
Malte L Schmieding ◽  
Marvin Kopka ◽  
Felix Balzer ◽  
Markus A Feufel
Keyword(s):  
Decision Support ◽  
Mental Effort ◽  
Experimental Comparison ◽  
Control Group ◽  
Decision Support Tools ◽  
Self Assessment ◽  
Decision Algorithm ◽  
Support Tools ◽  
Case Vignettes

BACKGROUND During the COVID-19 pandemic, medical laypersons with symptoms indicative of a COVID-19 infection commonly seek guidance on whether and where to seek medical care. Numerous web-based decision support tools (DSTs) have been developed, both by public and commercial stakeholders, to assist their decision-making. Though most of the DST’s underlying algorithms are similar and simple decision trees, their mode of presentation differs: some DSTs present a static flowchart, while others are designed as a conversational agent, guiding the user through the decision tree’s node step-by-step in an interactive manner. OBJECTIVE To investigate whether interactive DSTs provide greater decision support than non-interactive (ie, static) flowcharts. METHODS We developed mock interfaces for two DST (one static, one interactive), mimicking patient-facing, freely available DSTs for COVID-19 related self-assessment. Their underlying algorithm was identical and based on the Center for Disease Control’s guidelines. We recruited adult US residents online. which participants. Participants appraised the appropriate social and care-seeking behavior for seven fictitious descriptions of patients (case vignettes). Participants in the experimental groups received either the static or interactive mock DST as support, while the control group appraised the case vignettes unsupported. We determined participants’ accuracy, decision certainty (after deciding) and mental effort to measure quality of decision support. Participants’ ratings of the DSTs’ usefulness, ease of use, trust and future intention to use the tools served as measure to analyze differences in participants’ perception of the tools. We used ANOVAs and t-tests to assess statistical significance. RESULTS Our survey yielded 196 responses. The mean number of correct assessments was higher in the experimental groups (interactive DST group: M=11.71, SD=2.37; static DST group: M=11.45, SD=2.48) than in the control group (M=10.17, SD=2.00; F(2,193)=8.6, p<.001). Decisional certainty was significantly higher in the experimental groups (interactive DST group: M=80.7%, SD=14.1%; static DST group: M=80.5%, SD=15.8%) compared to the control group (M=65.8%, SD=20.8%; F(2, 193)=15.7, p<.001). Differences for mental effort between the three study were non-significant. Effect sizes of differences between the two experimental groups were small and non-significant for all three measures of quality of decision support and most measures of users’ perception of the DSTs. CONCLUSIONS When the decision space is limited as is the case in common COVID-19 self-assessment DSTs, static flowcharts might prove as beneficial in enhancing decision quality as interactive tools. Given that static flowcharts reveal the underlying decision algorithm more transparently and require less effort to develop, they might prove more efficient in providing guidance to the public. Further research should validate our findings on different use cases, elaborate on the trade-off between transparency and convenience in DSTs, and investigate whether subgroups of users benefit more one type of user interface than the other.

Clinical decision support tools to improve quality and practice efficiency across a large network of oncology practices.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 294-294
Author(s):  
Debra A. Patt ◽  
Madeline Nubie ◽  
Denise R. Kazzaz ◽  
Aimee Arlen ◽  
Mark A. Sitarik ◽  
...  
Keyword(s):  
Decision Support ◽  
Chemotherapy Regimen ◽  
Clinical Decision ◽  
Large Network ◽  
Decision Support Tools ◽  
Improve Quality ◽  
Treatment Regimens ◽  
Support Tools ◽  
Practice Efficiency ◽  
Clinical Decision Support Tools

294 Background: Electronic health records (EHR) provide opportunities for quality enhancements at various points in care. It can support electronic orders, meaningful use, progress notes, medication and allergy data, electronic prescribing, and vital-signs tracking. Clinical decision support tools (CDST) can facilitate high-quality performance and practice efficiency. These enhancements reduce error, improve quality, and drive practice efficiency. The oncology specific EHR (IKnowMed) incorporates CDST including physician driven level 1 pathways prescribing, chemotherapy regimen order entry, dose calculation, supportive care drugs, and guidelines for safe prescribing. Methods: To understand scope and utilization of CDSTs within a large network of individual oncology practices we characterized (qualitatively and quantitatively) common modalities in our EHR (iKnowMed). Treatment regimens were populated by the network collaborative-care committee. Physicians selected regimens pre-populated w/doses and pre-medications. Antiemetic regimens were pre-populated for emetagenic potential of the chemotherapy regimen. Results: Across the US Oncology Network, 952 physicians used the EHR to deliver services over a 5-month period. During that time, 69,448 cancer treatment regimens were ordered, pre-populated by drug, dose and pre-medications; and 68,268 chemotherapy regimens were pre-populated with antiemetic therapy to mirror emetagenic potential. Conclusions: By enhancing the EHR to include CDSTs, treatment and appropriate antiemetic regimens can be pre-populated across a large network of individual oncology practices that have aligned together using common IT and CDST to drive quality care for their patients. The network is using technology to enhance quality and efficiency in practice.

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