Decision-support tools via mobile devices to improve quality of care in primary healthcare settings.
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ABSTRACT:
SUBMITTER: Agarwal S
PROVIDER: S-EPMC8406991 | biostudies-literature | 2021 Jul
REPOSITORIES: biostudies-literature
Publications
Decision-support tools via mobile devices to improve quality of care in primary healthcare settings.
Agarwal Smisha S Glenton Claire C Tamrat Tigest T Henschke Nicholas N Maayan Nicola N Fønhus Marita S MS Mehl Garrett L GL Lewin Simon S
The Cochrane database of systematic reviews 20210727
<h4>Background</h4>The ubiquity of mobile devices has made it possible for clinical decision-support systems (CDSS) to become available to healthcare providers on handheld devices at the point-of-care, including in low- and middle-income countries. The use of CDSS by providers can potentially improve adherence to treatment protocols and patient outcomes. However, the evidence on the effect of the use of CDSS on mobile devices needs to be synthesized. This review was carried out to support a Worl ...[more]
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Project description:BackgroundMinistries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative.MethodsThree electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making.ResultsData from 300 health facilities in four health districts was collected and analyzed (November 2014-June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7-100% of all health facilities achieved all the required items for each indicator.ConclusionsOur experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology.
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Project description:Certified Cancer Centers must present all patients in multidisciplinary tumor boards (MTB), including standard cases with well-established treatment strategies. Too many standard cases can absorb much of the available time, which can be unfavorable for the discussion of complex cases. In any case, this leads to a high quantity, but not necessarily a high quality of tumor boards. Our aim was to develop a partially algorithm-driven decision support system (DSS) for smart phones to provide evidence-based recommendations for first-line therapy of common urological cancers. To assure quality, we compared each single digital decision with recommendations of an experienced MTB and obtained the concordance.1873 prostate cancer patients presented in the MTB of the urological department of the University Hospital of Cologne from 2014 to 2018 have been evaluated. Patient characteristics included age, disease stage, Gleason Score, PSA and previous therapies. The questions addressed to MTB were again answered using DSS. All blinded pairs of answers were assessed for discrepancies by independent reviewers. Overall concordance rate was 99.1% (1856/1873). Stage specific concordance rates were 97.4% (stage I), 99.2% (stage II), 100% (stage III), and 99.2% (stage IV). Quality of concordance were independent of age and risk profile. The reliability of any DSS is the key feature before implementation in clinical routine. Although our system appears to provide this safety, we are now performing cross-validation with several clinics to further increase decision quality and avoid potential clinic bias.
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Project description:BackgroundResidents of rural areas have poorer health status, less healthy behaviours and higher mortality than urban dwellers, issues which are commonly addressed in primary care. Strengthening primary care may be an important tool to improve the health status of rural populations.ObjectiveSynthesize and categorize studies that examine interventions to improve rural primary care.Eligibility criteriaExperimental or observational studies published between January 1, 1996 and December 2022 that include an historical or concurrent control comparison.Sources of evidencePubmed, CINAHL, Cochrane Library, Embase.Charting methodsWe extracted and charted data by broad category (quality, access and efficiency), study design, country of origin, publication year, aim, health condition and type of intervention studied. We assigned multiple categories to a study where relevant.Results372 papers met our inclusion criteria, divided among quality (82%), access (20%) and efficiency (13%) categories. A majority of papers were completed in the USA (40%), Australia (15%), China (7%) or Canada (6%). 35 (9%) papers came from countries in Africa. The most common study design was an uncontrolled before-and-after comparison (32%) and only 24% of studies used randomized designs. The number of publications each year has increased markedly over the study period from 1-2/year in 1997-99 to a peak of 49 papers in 2017.ConclusionsDespite substantial inequity in health outcomes associated with rural living, very little attention is paid to rural primary care in the scientific literature. Very few studies of rural primary care use randomized designs.
| S-EPMC11239038 | biostudies-literature
Project description:BackgroundMineralocorticoid receptor antagonists (MRAs) are underprescribed for patients with heart failure with reduced ejection fraction (HFrEF).ObjectivesThis study sought to compare effectiveness of 2 automated, electronic health record-embedded tools vs usual care on MRA prescribing in eligible patients with HFrEF.MethodsBETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure) was a 3-arm, pragmatic, cluster-randomized trial comparing the effectiveness of an alert during individual patient encounters vs a message about multiple patients between encounters vs usual care on MRA prescribing. This study included adult patients with HFrEF, no active MRA prescription, no contraindication to MRAs, and an outpatient cardiologist in a large health system. Patients were cluster-randomized by cardiologist (60 per arm).ResultsThe study included 2,211 patients (alert: 755, message: 812, usual care [control]: 644), with average age 72.2 years, average ejection fraction 33%, who were predominantly male (71.4%) and White (68.9%). New MRA prescribing occurred in 29.6% of patients in the alert arm, 15.6% in the message arm, and 11.7% in the control arm. The alert more than doubled MRA prescribing compared to usual care (relative risk: 2.53; 95% CI: 1.77-3.62; P < 0.0001) and improved MRA prescribing compared to the message (relative risk: 1.67; 95% CI: 1.21-2.29; P = 0.002). The number of patients with alert needed to result in an additional MRA prescription was 5.6.ConclusionsAn automated, patient-specific, electronic health record-embedded alert increased MRA prescribing compared to both a message and usual care. These findings highlight the potential for electronic health record-embedded tools to substantially increase prescription of life-saving therapies for HFrEF. (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations-Heart Failure [BETTER CARE-HF]; NCT05275920).
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Project description:Several artificial intelligence algorithms have been proposed to help diagnose glaucoma by analyzing the functional and/or structural changes in the eye. These algorithms require carefully curated datasets with access to ocular images. In the current study, we have modeled and evaluated classifiers to predict self-reported glaucoma using a single, easily obtained ocular feature (intraocular pressure (IOP)) and non-ocular features (age, gender, race, body mass index, systolic and diastolic blood pressure, and comorbidities). The classifiers were trained on publicly available data of 3015 subjects without a glaucoma diagnosis at the time of enrollment. 337 subjects subsequently self-reported a glaucoma diagnosis in a span of 1-12 years after enrollment. The classifiers were evaluated on the ability to identify these subjects by only using their features recorded at the time of enrollment. Support vector machine, logistic regression, and adaptive boosting performed similarly on the dataset with F1 scores of 0.31, 0.30, and 0.28, respectively. Logistic regression had the highest sensitivity at 60% with a specificity of 69%. Predictive classifiers using primarily non-ocular features have the potential to be used for identifying suspected glaucoma in non-eye care settings, including primary care. Further research into finding additional features that improve the performance of predictive classifiers is warranted.
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Project description:BACKGROUND:Breast cancer chemoprevention can reduce breast cancer incidence in high-risk women; however, chemoprevention is underutilized in the primary care setting. We conducted a pilot study of decision support tools among high-risk women and their primary care providers (PCPs). METHODS:The intervention included a decision aid (DA) for high-risk women, RealRisks, and a provider-centered tool, Breast Cancer Risk Navigation (BNAV). Patients completed validated surveys at baseline, after RealRisks and after their PCP clinical encounter or at 6-months. Referral for high-risk consultation and chemoprevention uptake were assessed via the electronic health record. The primary endpoint was accuracy of breast cancer risk perception at 6-months. RESULTS:Among 40 evaluable high-risk women, median age was 64.5 years and median 5-year breast cancer risk was 2.19%. After exposure to RealRisks, patients demonstrated an improvement in accurate breast cancer risk perceptions (p = 0.02), an increase in chemoprevention knowledge (p < 0.01), and 24% expressed interest in taking chemoprevention. Three women had a high-risk referral, and no one initiated chemoprevention. Decisional conflict significantly increased from after exposure to RealRisks to after their clinical encounter or at 6-months (p < 0.01). Accurate breast cancer risk perceptions improved and was sustained at 6-months or after clinical encounters. We discuss the side effect profile of chemoprevention and the care pathway when RealRisks was introduced to understand why patients experienced increased decision conflict. CONCLUSION:Future interventions should carefully link the use of a DA more proximally to the clinical encounter, investigate timed measurements of decision conflict and improve risk communication, shared decision making, and chemoprevention education for PCPs. Additional work remains to better understand the impact of decision aids targeting both patients and providers. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02954900 November 4, 2016 Retrospectively registered.
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