Background Efficiently caring for frail, older adults will become an increasingly important part of healthcare reform; telemonitoring within homes may be an answer to improve outcomes. This study sought to determine the difference in hospitalizations and emergency room (ER) visits in older adults using telemonitoring versus usual care. Methods This was a randomized trial of adults older than 60 years with high-risk for rehospitalization. Subjects were randomized to telemonitoring with daily input versus patient-driven usual care. Telemonitoring was accomplished by daily biometrics, symptom reporting and videoconference. The primary outcome included a composite end-point of hospitalization and ER visits in the 12 months following enrollment. Secondary end-points included hospital days, hospital admissions, and ER visits. Intention to treat analysis was performed. Results Two hundred and five subjects were enrolled with a mean age of 80.3 years. There was no difference in hospitalizations and ER visits between the telemonitoring group (63.7%) and the group receiving usual care (57.3%) (P value 0.345). There was no difference in individual outcomes including hospital days, hospital admissions and ER visits. There also was no significant change between hospitalizations and ER visits in the pre-enrollment and post-enrollment period. Mortality was higher in the telemonitoring group (14.7%), compared to usual care (3.9%) (P value 0.008). Conclusions Among elderly patients, telemonitoring did not result in lower hospitalizations or ER visits. There were no differences determined within the secondary outcomes. The cause of the mortality difference is unknown.

BackgroundPatient-centered diabetes care requires shared decision making (SDM). Decision aids promote SDM, but their efficacy in nonacademic and rural primary care clinics is unclear.MethodsWe cluster-randomized 10 practices in a concealed fashion to implement either a decision aid (DA) about starting statins or one about choosing antihyperglycemic agents. Each practice served as a control group for another practice implementing the other type of DA. From April 2011 to July 2012, 103 (DA=53) patients with type 2 diabetes participated in the trial. We used patient and clinician surveys administered after the clinical encounter to collect decisional outcomes (patient knowledge and comfort with decision making, patient and clinician satisfaction). Medical records provided data on metabolic control. Pharmacy fill profiles provided data for estimating adherence to therapy.ResultsCompared to usual care, patients receiving the DA were more likely to report discussing medications (77% vs. 45%, p<.001), were more likely to answer knowledge questions correctly (risk reduction with statins 61% vs. 33%, p=.07; knowledge about options 57% vs. 33%, p=.002) and were more engaged by their clinicians in decision making (50. vs. 28, difference 21.4 (95% CI 6.4, 36.3), p=.01). We found no significant impact on patient satisfaction, medication starts, adherence or clinical outcomes, in part due to limited statistical power.ConclusionDAs improved decisional outcomes without significant effect on clinical outcomes. DAs designed for point-of-care use with type 2 diabetes patients promoted shared decision making in nonacademic and rural primary care practices.Trial RegistrationNCT01029288

PurposeOsteoporosis Choice, an encounter decision aid, can engage patients and clinicians in shared decision making about osteoporosis treatment. Its effectiveness compared to the routine provision to clinicians of the patient’s estimated risk of fracture using the FRAX calculator is unknown.MethodsPatient-level, randomized, three-arm trial enrolling women over 50 with osteopenia or osteoporosis eligible for treatment with bisphosphonates, where the use of Osteoporosis Choice was compared to FRAX only and to usual care to determine impact on patient knowledge, decisional conflict, involvement in the decision-making process, decision to start and adherence to bisphosphonates.ResultsWe enrolled 79 women in the three arms. Because FRAX estimation alone and usual care produced similar results, we grouped them for analysis. Compared to these, use of Osteoporosis Choice increased patient knowledge (median score 6 vs. 4, p = .01), improved understanding of fracture risk and risk reduction with bisphosphonates (p = .01 and p<.0001, respectively), had no effect on decision conflict, and increased patient engagement in the decision making process (OPTION scores 57% vs. 43%, p = .001). Encounters with the decision aid were 0.8 minutes longer (range: 33 minutes shorter to 3.0 minutes longer). There were twice as many patients receiving and filling prescriptions in the decision aid arm (83% vs. 40%, p = .07); medication adherence at 6 months was no different across arms.ConclusionSupporting both patients and clinicians during the clinical encounter with the Osteoporosis Choice decision aid efficiently improves treatment decision making when compared to usual care with or without clinical decision support with FRAX results.Trial Registrationclinical trials.gov NCT00949611

Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However, linear parametric models are predicated on assumptions, which are often difficult to verify. In contrast, data-driven machine learning methods can be applied to derive insight from the raw data without a priori assumptions. However, the underlying theory of most machine learning algorithms assume that the data is independent and identically distributed, making them inefficient for longitudinal supervised learning. In this study, we formulate an analytic framework, which integrates the random-effects structure of GLMM into non-linear machine learning models capable of exploiting temporal heterogeneous effects, sparse and varying-length patient characteristics inherent in longitudinal data. We applied the derived mixed-effect machine learning (MEml) framework to predict longitudinal change in glycemic control measured by hemoglobin A1c (HbA1c) among well controlled adults with type 2 diabetes. Results show that MEml is competitive with traditional GLMM, but substantially outperformed standard machine learning models that do not account for random-effects. Specifically, the accuracy of MEml in predicting glycemic change at the 1st, 2nd, 3rd, and 4th clinical visits in advanced was 1.04, 1.08, 1.11, and 1.14 times that of the gradient boosted model respectively, with similar results for the other methods. To further demonstrate the general applicability of MEml, a series of experiments were performed using real publicly available and synthetic data sets for accuracy and robustness. These experiments reinforced the superiority of MEml over the other methods. Overall, results from this study highlight the importance of modeling random-effects in machine learning approaches based on longitudinal data. Our MEml method is highly resistant to correlated data, readily accounts for random-effects, and predicts change of a longitudinal clinical outcome in real-world clinical settings with high accuracy.

ObjectiveTo determine utilisation of endoscopic retrograde cholangiopancreatography (ERCP); incidence of inpatient admissions for complications occurring within 30 days of ERCP and risk factors for procedural-related complications, in a population-based study.DesignRetrospective cohort study.SettingOlmsted County, Minnesota.ParticipantsAll adult residents of Olmsted County, Minnesota, who underwent ERCP from 1997 to 2006.InterventionsDiagnostic and therapeutic ERCPs were assessed.Primary and secondary outcome measuresPatient and procedural characteristics and complications within 30 days; and rates of ERCP utilisation and unplanned admissions and risk factors for admissions.ResultsIn 10 years, 1072 ERCPs were performed on 827 individual patients. Average utilisation of ERCP was 83.1 ERCPs/100 000 persons/year, with an increase from 58 to 104.8 ERCPs/100 000 persons/year over time, driven by increases in therapeutic procedures. Within 30 days after 236 procedures, 62 admissions were definitely related to the index ERCP. The complication rate was 5.3%, including pancreatitis (26, 2.4%), infection/cholangitis (16, 1.5%), bleeding (15, 1.4%) and perforation (4, 0.37%). 30-day mortality was 2.4%, none of which was directly related to the ERCP or complications thereof. Risk factors identified through multivariate analysis to be associated with adverse events included: age <45 years (p=0.0498); body mass index ≥35 (p=0.0024); pancreatic duct cannulation (p=0.0026); outpatient procedure (p<0.0001); intraprocedure sphincterotomy bleeding (p<0.0001); difficulty grade (p=0.115) and patient's first ERCP (p=0.0394).LimitationsRetrospective study.ConclusionsPopulation utilisation of ERCP rose during the study period, specifically in therapeutic procedures. Admissions within 30 days of ERCP are common but often unrelated. Complications of ERCP remain infrequent and deaths quite unusual.