Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454

Study protocol for the Anesthesiology Control Tower—Feedback Alerts to Supplement Treatments (ACTFAST-3) trial: A pilot randomized controlled trial in intraoperative telemedicine [version 1; referees: 2 approved]

Background: Each year, over 300 million people undergo surgical procedures worldwide. Despite efforts to improve outcomes, postoperative morbidity and mortality are common. Many patients experience complications as a result of either medical error or failure to adhere to established clinical practice guidelines. This protocol describes a clinical trial comparing a telemedicine-based decision support system, the Anesthesiology Control Tower (ACT), with enhanced standard intraoperative care. Methods: This study is a pragmatic, comparative effectiveness trial that will randomize approximately 12,000 adult surgical patients on an operating room (OR) level to a control or to an intervention group. All OR clinicians will have access to decision support software within the OR as a part of enhanced standard intraoperative care. The ACT will monitor patients in both groups and will provide additional support to the clinicians assigned to intervention ORs. Primary outcomes include blood glucose management and temperature management. Secondary outcomes will include surrogate, clinical, and economic outcomes, such as incidence of intraoperative hypotension, postoperative respiratory compromise, acute kidney injury, delirium, and volatile anesthetic utilization. Ethics and dissemination: The ACTFAST-3 study has been approved by the Human Resource Protection Office (HRPO) at Washington University in St. Louis and is registered at clinicaltrials.gov (NCT02830126). Recruitment for this protocol began in April 2017 and will end in December 2018. Dissemination of the findings of this study will occur via presentations at academic conferences, journal publications, and educational materials

The Utility of the U.S. Diabetes Conversation Map as an Intervention to Promote Diabetes Self-Management Adherence

Diabetes has reached epidemic levels, to the currently estimated 29 million individuals who are living with diabetes. Those with diabetes must manage their disease through a combination of medication, physical activity recommendations, and nutritional guidelines. The consequences of non-adherence to recommendations include cardiovascular disease, kidney failure, vision loss, or ultimately, death. Despite the risks of non-adherence, individuals often do not adhere to recommended treatment. Researchers have attempted to identify strategies to promote diabetes self-management adherence, thereby decreasing complications related to the disease. Specific Aims: describe the factors that prohibit individuals from adhering from diabetes self-management behaviors as well as the factors that promote self-management adherence, compare adherence rates of individuals participating in an enhanced diabetes education program with the adherence rates of individuals that participated in enhanced diabetes education and also attended group social support sessions, evaluate the adherence to self-management behaviors of individuals participating in a diabetes care coordination program. Results: A review of research articles from 2009 through 2013 identified barriers to diabetes self-management adherence as complexity of self-management, low health literacy, the financial burden of adherence, availability of resources, and lack of knowledge. Factors that promote diabetes self-management adherence include diabetes self-management education, self-efficacy, social support, and goal setting. A retrospective chart review of participants in an employer-sponsored health program was performed to examine the effectiveness of a social support intervention administered through the health program to promote adherence to recommended diabetes treatment. Results of the study revealed that individuals who participated in the social support intervention, in addition to the employer-sponsored health program, demonstrated increased adherence to recommended diabetes treatment from baseline to 12 months, in comparison to those who participated in only the health program (p = .048). Additional chart review compared participants’ self-management behaviors at baseline with their self-management behaviors at 12 months after entry into the program. There was a significant improvement in adherence to self-management behaviors of receiving an influenza vaccination (p = .036), decreased reported use of alcohol (p = .002) and tobacco (p = .043), and fewer reports of skipped meals (p = .009)

Embedding Licensed Independent Providers In A Va Regional Clinical Contact Center

Inappropriate attendance in US emergency departments (ED) account for up to 40% of all ED visits. Overcrowding can lead to poorer outcomes for patients in the ED, including a higher rate of triage to non-monitored area and longer timeframes to physician initial assessment. Additionally, the cost for unnecessary ED care is two to three times greater than care is in an Urgent Care or Clinic and cost billions of dollars in the U.S. yearly. Licensed Independent Providers (LIP); Medical Doctors and Nurse Practitioners were embedded into a VHA VISN registered nurse telephone triage system. Protocols were developed for additional opportunities for assessment and patient health concern resolution of calls by the LIP. These calls included those already determined to require ED disposition by the RN protocols. Calls were tracked for 3 months. There were 1608 calls sent from RN to LIP staff. Of these, 104 were initially designated as ED dispositions. After the LIP intervention, 55 calls, 53%, were resolved and the patient no longer given an ED disposition. Patient satisfaction was also reviewed via Press Ganey and there overall was no difference in patient satisfaction for the 2 quarters before the intervention and the quarter that included the intervention. The percentage of calls sent from RN to the LIP that involved initial ED dispositions was only 7% of the total calls, RN staff outnumbered LIP 7 to 1 and RNs covered 24 hours a day, while LIP staff 12 hours a day. These represent opportunities for future exploration and expansion. Scalability is available through the VHA, the largest healthcare organization is the US and its nationwide telephone triage network and national level program managers

The Use of Routinely Collected Data in Clinical Trial Research

RCTs are the gold standard for assessing the effects of medical interventions, but they also pose many challenges, including the often-high costs in conducting them and a potential lack of generalizability of their findings. The recent increase in the availability of so called routinely collected data (RCD) sources has led to great interest in their application to support RCTs in an effort to increase the efficiency of conducting clinical trials. We define all RCTs augmented by RCD in any form as RCD-RCTs. A major subset of RCD-RCTs are performed at the point of care using electronic health records (EHRs) and are referred to as point-of-care research (POC-R). RCD-RCTs offer several advantages over traditional trials regarding patient recruitment and data collection, and beyond. Using highly standardized EHR and registry data allows to assess patient characteristics for trial eligibility and to examine treatment effects through routinely collected endpoints or by linkage to other data sources like mortality registries. Thus, RCD can be used to augment traditional RCTs by providing a sampling framework for patient recruitment and by directly measuring patient relevant outcomes. The result of these efforts is the generation of real-world evidence (RWE). Nevertheless, the utilization of RCD in clinical research brings novel methodological challenges, and issues related to data quality are frequently discussed, which need to be considered for RCD-RCTs. Some of the limitations surrounding RCD use in RCTs relate to data quality, data availability, ethical and informed consent challenges, and lack of endpoint adjudication which may all lead to uncertainties in the validity of their results. The purpose of this thesis is to help fill the aforementioned research gaps in RCD-RCTs, encompassing tasks such as assessing their current application in clinical research and evaluating the methodological and technical challenges in performing them. Furthermore, it aims to assess the reporting quality of published reports on RCD-RCTs

Complex Care Management Program Overview

This report includes brief updates on various forms of complex care management including: Aetna - Medicare Advantage Embedded Case Management ProgramBrigham and Women's Hospital - Care Management ProgramIndependent Health - Care PartnersIntermountain Healthcare and Oregon Health and Science University - Care Management PlusJohns Hopkins University - Hospital at HomeMount Sinai Medical Center -- New York - Mount Sinai Visiting Doctors Program/ Chelsea-Village House Calls ProgramsPartners in Care Foundation - HomeMeds ProgramPrinceton HealthCare System - Partnerships for PIECEQuality Improvement for Complex Chronic Conditions - CarePartner ProgramSenior Services - Project Enhance/EnhanceWellnessSenior Whole Health - Complex Care Management ProgramSumma Health/Ohio Department of Aging - PASSPORT Medicaid Waiver ProgramSutter Health - Sutter Care Coordination ProgramUniversity of Washington School of Medicine - TEAMcar

Computerized advice on drug dosage to improve prescribing practice

International audienceComputerized advice on drug dosage to improve prescribing practice (Review) 1 Copyright © 2013 The Cochrane Collaboration. Published by JohnWiley & Sons, Ltd. Data collection and analysis Two review authors independently extracted data and assessed study quality.We grouped the results from the included studies by drug used and the effect aimed at for aminoglycoside antibiotics, amitriptyline, anaesthetics, insulin, anticoagulants, ovarian stimulation, anti-rejection drugs and theophylline. We combined the effect sizes to give an overall effect for each subgroup of studies, using a random-effects model. We further grouped studies by type of outcome when appropriate (i.e. no evidence of heterogeneity). Main results Forty-six comparisons (from 42 trials) were included (as compared with 26 comparisons in the last update) including a wide range of drugs in inpatient and outpatient settings. All were randomized controlled trials except two studies. Interventions usually targeted doctors, although some studies attempted to influence prescriptions by pharmacists and nurses. Drugs evaluated were anticoagulants, insulin, aminoglycoside antibiotics, theophylline, anti-rejection drugs, anaesthetic agents, antidepressants and gonadotropins. Although all studies used reliable outcome measures, their quality was generally low. This update found similar results to the previous update and managed to identify specific therapeutic areas where the computerized advice on drug dosage was beneficial compared with routine care: 1. it increased target peak serum concentrations (standardized mean difference (SMD) 0.79, 95% CI 0.46 to 1.13) and the proportion of people with plasma drug concentrations within the therapeutic range after two days (pooled risk ratio (RR) 4.44, 95% CI 1.94 to 10.13) for aminoglycoside antibiotics; 2. it led to a physiological parameter more often within the desired range for oral anticoagulants (SMD for percentage of time spent in target international normalized ratio +0.19, 95% CI 0.06 to 0.33) and insulin (SMD for percentage of time in target glucose range: +1.27, 95% CI 0.56 to 1.98); 3. it decreased the time to achieve stabilization for oral anticoagulants (SMD -0.56, 95% CI -1.07 to -0.04); 4. it decreased the thromboembolism events (rate ratio 0.68, 95% CI 0.49 to 0.94) and tended to decrease bleeding events for anticoagulants although the difference was not significant (rate ratio 0.81, 95%CI 0.60 to 1.08). It tended to decrease unwanted effects for aminoglycoside antibiotics (nephrotoxicity: RR 0.67, 95% CI 0.42 to 1.06) and anti-rejection drugs (cytomegalovirus infections: RR 0.90, 95% CI 0.58 to 1.40); 5. it tended to reduce the length of time spent in the hospital although the difference was not significant (SMD -0.15, 95% CI -0.33 to 0.02) and to achieve comparable or better cost-effectiveness ratios than usual care; 6. there was no evidence of differences in mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, antirejection drugs and antidepressants. For all outcomes, statistical heterogeneity quantified by I2 statistics was moderate to high. Authors’ conclusions This review update suggests that computerized advice for drug dosage has some benefits: it increases the serum concentrations for aminoglycoside antibiotics and improves the proportion of people for which the plasma drug is within the therapeutic range for aminoglycoside antibiotics. It leads to a physiological parameter more often within the desired range for oral anticoagulants and insulin. It decreases the time to achieve stabilization for oral anticoagulants. It tends to decrease unwanted effects for aminoglycoside antibiotics and anti-rejection drugs, and it significantly decreases thromboembolism events for anticoagulants. It tends to reduce the length of hospital stay compared with routine care while comparable or better cost-effectiveness ratios were achieved. However, there was no evidence that decision support had an effect on mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants. In addition, there was no evidence to suggest that some decision support technical features (such as its integration into a computer physician order entry system) or aspects of organization of care (such as the setting) could optimize the effect of computerized advice. Taking into account the high risk of bias of, and high heterogeneity between, studies, these results must be interpreted with caution. P L A I N L A N G U A G E S U M M A R Y Computerized advice on drug dosage to improve prescribing practice (Review) 2 Copyright © 2013 The Cochrane Collaboration. Published by JohnWiley & Sons, Ltd. Computerized advice on drug dosage to improve prescribing practice Background Physicians and other healthcare professionals often prescribe drugs that will only work at certain concentrations. These drugs are said to have a narrow therapeutic window. This means that if the concentration of the drug is too high or too low, they may cause serious side effects or not provide the benefits they should. For example, blood thinners (anticoagulants) are prescribed to thin the blood to prevent clots. If the concentration is too high, people may experience excessive bleeding and even death. In contrast, if the concentration is too low, a clot could form and cause a stroke. For these types of drugs, it is important that the correct amount of the drug be prescribed. Calculating and prescribing the correct amount can be complicated and time-consuming for healthcare professionals. Sometimes determining the correct dose can take a long time since healthcare professionals may not want to prescribe high doses of the drugs initially because they make mistakes in calculations. Several computer systems have been designed to do these calculations and assist healthcare professionals in prescribing these types of drugs. Study characteristics We sought clinical trial evidence from scientific databases to evaluate the effectiveness of these computer systems. The evidence is current to January 2012. We found data from 42 trials (40 randomized controlled trials (trials that allocate people at random to receive one of a number of drugs or procedures) and two non-randomized controlled trials). Key results Computerized advice for drug dosage can benefit people taking certain drugs compared with empiric dosing (where a dose is chosen based on a doctor’s observations and experience)without computer assistance.When using the computer system, healthcare professionals prescribed appropriately higher doses of the drugs initially for aminoglycoside antibiotics and the correct drug dose was reached more quickly for oral anticoagulants. It significantly decreased thromboembolism (blood clotting) events for anticoagulants and tended to reduce unwanted effects for aminoglycoside antibiotics and anti-rejection drugs (although not an important difference). It tended to reduce the length of hospital stay compared with routine care with comparable or better cost-effectiveness. There was no evidence of effects on death or clinical side events for insulin (low blood sugar (hypoglycaemia)), anaesthetic agents, anti-rejection drugs (drugs taken to prevent rejection of a transplanted organ) and antidepressants. Quality of evidence The quality of the studies was low so these results must be interpreted with caution

Evaluation of medication safety in the discharge medication of 509 surgical inpatients using electronic prescription support software and an extended operational interaction classification

Purpose: Our aim was to study drug interactions and dose adjustments in patients with renal impairment in the discharge medication of surgical inpatients and to evaluate the strengths and limitations of clinical decision support software (CDSS) for this task. Methods: This was a cross-sectional study involving 509 surgical patients of a primary care hospital. We developed a customized interface for the CDSS MediQ, which we used for automated retrospective identification of drug interactions in the patients' discharge medication. The clinical relevance of the interactions was evaluated based on the Zurich Interaction System (ZHIAS) that incorporates the operational classification of drug interactions (ORCA). Prescriptions were further analyzed for recommended dose adjustments in patients with a glomerular filtration rate <60ml/min. Results: For the total of 2,729 prescriptions written for the 509 patients enrolled in the study, MediQ generated 2,558 interaction alerts and 1,849 comments. Among these were ten "high danger” and 551 "average danger” alerts that we reclassified according to ORCA criteria. This reclassification resulted in ten contraindicated combinations, 77 provisionally contraindicated combinations, and 310 with a conditional and 164 with a minimal risk of adverse outcomes. The ZHIAS classification also provides categorical information on expected adverse outcomes and management recommendations, which are presented in detail. We identified 56 prescriptions without a recommended dose adjustment for impaired renal function. Conclusions: CDSS identified a large number of drug interactions in surgical discharge medication, but according to ZHIAS criteria only a minor fraction of these appeared to involve a substantial risk to the patient. CDSS should therefore aim at reducing over-alerting and improve usability in order to become more efficacious in terms of the prevention of adverse drug events in clinical practic

Doctor of Philosophy

dissertationMany nutritional assessment techniques, including food frequency questionnaires (FFQs) and 24-hour dietary recalls have innate limitations such as expensive protocols, high respondent burden, and self-reporting biases. Supermarket sales data have shown promise as a new, indirect, inexpensive nutritional assessment method in recent studies. The goals of the research in this dissertation were to link nutritional content to supermarket sales data and to determine the relationship between supermarket purchases and traditional nutritional measures through correlation and regression analyses. Nutritional data was mapped to sales data at the nutrient and food group levels. One year retrospective supermarket sales data, household food inventory data, and FFQ results were then obtained for 50 households recruited for the study. A correlation analysis was completed to compare percentage of food groups purchased over 52 weeks against food groups in the household inventory and in the FFQ results. Additionally, stepwise regression models were created to predict BMI, energy intake, fat intake, and saturated fat intake based on supermarket sales data. Nutritional content was mapped to 100% of the supermarket sales data at the food group level and at 69% for the nutrient level. The correlation coefficients between the household inventory and sales data over the course of 52 weeks ranged from -0.13 to 0.83 with an average value of 0.23 at week 32, while correlation for the comparison between the FFQ and sales data ranged from -0.17 to 0.47 with an average of 0.23 at 32 weeks. 5 The regression models to predict BMI, energy intake, fat intake, and saturated fat intake each yielded significant results for several food group purchases from the sales data. Mapping nutritional content to sales data was successful, given that there are potential strategies to increase the linkage for nutrient data. The correlation results are in line with other studies comparing nutritional assessment methods against each other and the regression models produced many significant food groups that are substantiated by multiple studies. Overall, the work presented gives an excellent starting point for further informatics research into the untapped potential of supermarket sales data as a nutritional assessment method and public health tool

Stepping Up Telehealth: Using telehealth to support a new model of care for type 2 diabetes management in rural and regional primary care

Our proposal is to pilot the feasibility and acceptability of a telehealth intervention to enhance care in rural general practice for people with out-of-target Type 2 Diabetes (T2D). Our research program builds on the UK Medical Research Council framework in developing a model of care intervention that is well matched to the setting of General Practice and to the experiences and priorities of patients. We undertook an exploratory qualitative study, leading to the development of a practice-based intervention that we pilot tested for feasibility and acceptability before undertaking a larger pilot and a cluster RCT. We based our work on Normalisation Process Theory (NPT), a sociological theory of implementation, which describes how new practices become incorporated into routine clinical care as a result of individual and collective work. NPT suggested that our model of care intervention would need to be patient centred and include all members of the multidisciplinary diabetes team, including Endocrinologist, RN-CDE General Practitioners (GP), and generalist Practice Nurses (PNs). All of these groups are involved in the �work� of insulin initiation.The research reported in this paper is a project of the Australian Primary Health Care Research Institute which is supported by a grant from the Australian Government Department of Health and Ageing under the Primary Health Care Research Evaluation and Development Strategy