IMPORTANCE Reducing early (<30 days) hospital readmissions is a policy priority aimed at improving health care quality. The cumulative complexity model conceptualizes patient context. It predicts that highly supportive discharge interventions will enhance patient capacity to enact burdensome self-care and avoid readmissions.OBJECTIVE To synthesize the evidence of the efficacy of interventions to reduce early hospital readmissions and identify intervention features-including their impact on treatment burden and on patients' capacity to enact postdischarge self-care-that might explain their varying effects.DATA SOURCES We searched PubMed, Ovid MEDLINE, Ovid EMBASE, EBSCO CINAHL, and Scopus (1990 until April 1, 2013), contacted experts, and reviewed bibliographies.STUDY SELECTION Randomized trials that assessed the effect of interventions on all-cause or unplanned readmissions within 30 days of discharge in adult patients hospitalized for a medical or surgical cause for more than 24 hours and discharged to home. DATA EXTRACTION AND SYNTHESISReviewer pairs extracted trial characteristics and used an activity-based coding strategy to characterize the interventions; fidelity was confirmed with authors. Blinded to trial outcomes, reviewers noted the extent to which interventions placed additional work on patients after discharge or supported their capacity for self-care in accordance with the cumulative complexity model. MAIN OUTCOMES AND MEASURESRelative risk of all-cause or unplanned readmission with or without out-of-hospital deaths at 30 days postdischarge. RESULTSIn 42 trials, the tested interventions prevented early readmissions (pooled random-effects relative risk, 0.82 [95% CI, 0.73-0.91]; P < .001; I 2 = 31%), a finding that was consistent across patient subgroups. Trials published before 2002 reported interventions that were 1.6 times more effective than those tested later (interaction P = .01). In exploratory subgroup analyses, interventions with many components (interaction P = .001), involving more individuals in care delivery (interaction P = .05), and supporting patient capacity for self-care (interaction P = .04) were 1.4, 1.3, and 1.3 times more effective than other interventions, respectively. A post hoc regression model showed incremental value in providing comprehensive, postdischarge support to patients and caregivers.CONCLUSIONS AND RELEVANCE Tested interventions are effective at reducing readmissions, but more effective interventions are complex and support patient capacity for self-care. Interventions tested more recently are less effective.

SummaryBackgroundThe 2013 American College of Cardiology / American Heart Association Guidelines for the Treatment of Blood Cholesterol emphasize treatment based on cardiovascular risk. But finding time in a primary care visit to manually calculate cardiovascular risk and prescribe treatment based on risk is challenging. We developed an informatics-based clinical decision support tool, MayoExpertAdvisor, to deliver automated cardiovascular risk scores and guideline-based treatment recommendations based on patient-specific data in the electronic heath record.ObjectiveTo assess the impact of our clinical decision support tool on the efficiency and accuracy of clinician calculation of cardiovascular risk and its effect on the delivery of guideline-consistent treatment recommendations.MethodsClinicians were asked to review the EHR records of selected patients. We evaluated the amount of time and the number of clicks and keystrokes needed to calculate cardiovascular risk and provide a treatment recommendation with and without our clinical decision support tool. We also compared the treatment recommendation arrived at by clinicians with and without the use of our tool to those recommended by the guidelines.ResultsClinicians saved 3 minutes and 38 seconds in completing both tasks with MayoExpertAdvisor, used 94 fewer clicks and 23 fewer key strokes, and improved accuracy from the baseline of 60.61% to 100% for both the risk score calculation and guideline-consistent treatment recommendation.ConclusionInformatics solution can greatly improve the efficiency and accuracy of individualized treatment recommendations and have the potential to increase guideline compliance.

Tumours of the central nervous system are the most common solid tumour, accounting for a quarter of the 1500 cases of childhood cancer diagnosed each year in the U.K. They are the most common cause of cancer-related death in children. Treatment consists of surgery followed by adjuvant chemotherapy and/or radiotherapy. Survival rates have generally increased, but many survivors suffer from radiotherapy-related neurocognitive and endocrine side effects as well as an increased risk of secondary cancer. Adjuvant chemotherapy is normally given in combination to circumvent chemoresistance, but several studies have demonstrated it to be ineffective in the absence of radiotherapy. The identification of children with drug-resistant disease at the outset could allow stratification of those that are potentially curable by chemotherapy alone. Ultimately, however, what is required is a means to overcome this drug resistance and restore the effectiveness of chemotherapy. Medulloblastomas and ependymomas account for over 30% of paediatric brain tumours. Advances in neurosurgery, adjuvant radiotherapy and chemotherapy have led to improvements in 5-year overall survival rates. There remain, however, significant numbers of medulloblastoma patients that have intrinsically drug-resistant tumours and/or present with disseminated disease. Local relapse in ependymoma is also common and has an extremely poor prognosis with only 25% of children surviving first relapse. Each of these is consistent with the acquisition of drug and radiotherapy resistance. Since the majority of chemotherapy drugs currently used to treat these patients are transport substrates for ATP-binding cassette sub-family B member 1 (ABCB1) we will address the hypothesis that ABCB1 expression underlies this drug resistance.

Background A clinical decision support system (CDSS) for cervical cancer screening identifies patients due for routine cervical cancer screening. Yet, high-risk patients who require more frequent screening or earlier follow-up to address past abnormal results are not identified. We aimed to assess the effect of a complex CDSS, incorporating national guidelines for high-risk patient screening and abnormal result management, its implementation to identify patients overdue for testing, and the outcome of sending a targeted recommendation for follow-up. Methods At 3 primary care clinics affiliated with an academic medical center, a reminder recommending an appointment for Papanicolaou (Pap) testing or Pap and human papillomavirus co-testing was sent to high-risk women age 18 through 65 years (intervention group) identified by CDSS as overdue for testing. Historical control patients, who did not receive a reminder, were identified by CDSS 1 year before the date when reminders were sent to the intervention group. Test completion rates were compared between the intervention and control groups through a generalized estimating equation extension. Results Across the 3 sites, the average completion rate of recommended follow-up testing was significantly higher in the intervention group at 23.7% (61/257) than the completion rate at 3.3% (17/516) in the control group (P<.001). Conclusions A CDSS with enhanced capabilities to identify high-risk women due for cervical cancer testing beyond routine screening intervals, with subsequent patient notification, has the potential to decrease cervical precancer and cancer by improving adherence to guidelines-compliant follow-up and needed treatment.

BackgroundShared decision making is essential to patient centered care, but can be difficult for busy clinicians to implement into practice. Tools have been developed to aid in shared decision making and embedded in electronic medical records (EMRs) to facilitate use. This study was undertaken to explore the patterns of use and barriers and facilitators to use of two decision aids, the Statin Choice Decision Aid (SCDA) and the Diabetes Medication Choice Decision Aid (DMCDA), in primary care practices where the decision aids are embedded in the EMR.MethodsA survey exploring factors that influenced use of each decision aid was sent to eligible primary care clinicians affiliated with the Mayo Clinic in Rochester, MN. Survey data was collected and clinician use of each decision aid via links from the EMR was tracked.ResultsThe survey response rate was 40% (105/262). Log file data indicated 51% of clinicians used the SCDA and 9% of clinicians used the DMCDA. Reasons for lack of use included lack of knowledge of the EMR link, not finding the decision aids helpful, and time constraints. Survey responses indicated that use of the tool as intended was low, with many clinicians only discussing decision aid topics that they found relevant.ConclusionAlthough guidelines for both the treatment of blood cholesterol with a statin and for the treatment of hyperglycemia in type 2 diabetes recommend shared decision making, tools that facilitate shared decision making are not routinely used even when embedded in the EMR. Even when decision aids are used, their use may not reflect patient centered care.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0514-5) contains supplementary material, which is available to authorized users.

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Keywords► clinical decision support system ► ambulatory care information systems ► testing and evaluation of health information technology ► electronic health records ► knowledge delivery ► knowledge management AbstractBackground Clinical decision support systems (CDSS) for cervical cancer prevention are generally limited to identifying patients who are overdue for their next routine/next screening, and they do not provide recommendations for follow-up of abnormal results. We previously developed a CDSS to automatically provide follow-up recommendations based on the American Society of Colposcopy and Cervical Pathology (ASCCP) guidelines for women with both previously normal and abnormal test results leveraging information available in the electronic medical record (EMR). Objective Enhance the CDSS by improving its accuracy and incorporating changes to reflect the latest revision of the guidelines. Methods After making enhancements to the CDSS, we evaluated the performance of the clinical recommendations on 393 patients selected through stratified sampling from a set of 3,704 patients in a nonclinical setting. We performed chart review of individual patient's record to evaluate the performance of the system. An expert clinician assisted by a resident manually reviewed the recommendation made by the system and verified whether the recommendations were as per the ASCCP guidelines.Results The recommendation accuracy of the enhanced CDSS improved to 93%, which is a substantial improvement over the 84% reported previously. A detailed analysis of errors is presented in this article. We fixed the errors identified in this evaluation that were amenable to correction to further improve the accuracy of the system. The source code of the updated CDSS is available at https://github.com/ohnlp/ MayoNlpPapCdss. Conclusion We made substantial enhancements to our earlier prototype CDSS with the updated ASCCP guidelines and performed a thorough evaluation in a nonclinical setting to improve the accuracy of the CDSS. The CDSS will be further refined as it is utilized in the practice.

IntroductionClinical practice guidelines facilitate optimal clinical practice. Point of care access, interpretation and application of such guidelines, however, is inconsistent. Informatics-based tools may help clinicians apply guidelines more consistently. We have developed a novel clinical decision support tool that presents guideline-relevant information and actionable items to clinicians at the point of care. We aim to test whether this tool improves the management of hyperlipidaemia, atrial fibrillation and heart failure by primary care clinicians.Methods/analysisClinician care teams were cluster randomised to receive access to the clinical decision support tool or passive access to institutional guidelines on 16 May 2016. The trial began on 1 June 2016 when access to the tool was granted to the intervention clinicians. The trial will be run for 6 months to ensure a sufficient number of patient encounters to achieve 80% power to detect a twofold increase in the primary outcome at the 0.05 level of significance. The primary outcome measure will be the percentage of guideline-based recommendations acted on by clinicians for hyperlipidaemia, atrial fibrillation and heart failure. We hypothesise care teams with access to the clinical decision support tool will act on recommendations at a higher rate than care teams in the standard of care arm.Ethics and disseminationThe Mayo Clinic Institutional Review Board approved all study procedures. Informed consent was obtained from clinicians. A waiver of informed consent and of Health Insurance Portability and Accountability Act (HIPAA) authorisation for patients managed by clinicians in the study was granted. In addition to publication, results will be disseminated via meetings and newsletters.Trial registration number NCT02742545.