Best Care at Lower Cost: The Path to Continuously Learning Health Care in America

http://www.iom.edu/Reports/2012/Best-Care-at-Lower-Cost-The-Path-to-Continuously-Learning-Health-Care-in-America.asp

Informatics for Health 2017 : advancing both science and practice

Conference report, The Informatics for Health congress, 24-26 April 2017, in Manchester, UK.Introduction : The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress. Discussion : The twin programmes of “Big Data” and “Digital Health” are not always joined up by coherent policy and investment priorities. Substantial global investment in health IT and data science has led to sound progress but highly variable outcomes. Society needs an approach that brings together the science and the practice of health informatics. The goal is multi-level Learning Health Systems that consume and intelligently act upon both patient data and organizational intervention outcomes. Conclusions : Informatics for Health demonstrated the art of the possible, seen in the breadth and depth of our contributions. We call upon policy makers, research funders and programme leaders to learn from this joined-up approach.Publisher PDFPeer reviewe

An evidence-based approach to the use of telehealth in long-term health conditions: development of an intervention and evaluation through pragmatic randomised controlled trials in patients with depression or raised cardiovascular risk

Background: Health services internationally are exploring the potential of telehealth to support the management of the growing number of people with long-term conditions (LTCs). Aim: To develop, implement and evaluate new care programmes for patients with LTCs, focusing on two common LTCs as exemplars: depression or high cardiovascular disease (CVD) risk. Methods Development: We synthesised quantitative and qualitative evidence on the effectiveness of telehealth for LTCs, conducted a qualitative study based on interviews with patients and staff and undertook a postal survey to explore which patients are interested in different forms of telehealth. Based on these studies we developed a conceptual model [TElehealth in CHronic disease (TECH) model] as a framework for the development and evaluation of the Healthlines Service for patients with LTCs. Implementation: The Healthlines Service consisted of regular telephone calls to participants from health information advisors, supporting them to make behaviour change and to use tailored online resources. Advisors sought to optimise participants’ medication and to improve adherence. Evaluation: The Healthlines Service was evaluated with linked pragmatic randomised controlled trials comparing the Healthlines Service plus usual care with usual care alone, with nested process and economic evaluations. Participants were adults with depression or raised CVD risk recruited from 43 general practices in three areas of England. The primary outcome was response to treatment and the secondary outcomes included anxiety (depression trial), individual risk factors (CVD risk trial), self-management skills, medication adherence, perceptions of support, access to health care and satisfaction with treatment. Trial results Depression trial: In total, 609 participants were randomised and the retention rate was 86%. Response to treatment [Patient Health Questionnaire 9-items (PHQ-9) reduction of ≥ 5 points and score of < 10 after 4 months] was higher in the intervention group (27%, 68/255) than in the control group (19%, 50/270) [odds ratio 1.7, 95% confidence interval (CI) 1.1 to 2.5; p = 0.02]. Anxiety also improved. Intervention participants reported better access to health support, greater satisfaction with treatment and small improvements in self-management, but not improved medication adherence. CVD risk trial: In total, 641 participants were randomised and the retention rate was 91%. Response to treatment (maintenance of/reduction in QRISK®2 score after 12 months) was higher in the intervention group (50%, 148/295) than in the control group (43%, 124/291), which does not exclude a null effect (odds ratio 1.3, 95% CI 1.0 to 1.9; p = 0.08). The intervention was associated with small improvements in blood pressure and weight, but not smoking or cholesterol. Intervention participants were more likely to adhere to medication, reported better access to health support and greater satisfaction with treatment, but few improvements in self-management. The Healthlines Service was likely to be cost-effective for CVD risk, particularly if the benefits are sustained, but not for depression. The intervention was implemented largely as planned, although initial delays and later disruption to delivery because of the closure of NHS Direct may have adversely affected participant engagement. Conclusion: The Healthlines Service, designed using an evidence-based conceptual model, provided modest health benefits and participants valued the better access to care and extra support provided. This service was cost-effective for CVD risk but not depression. These findings of small benefits at extra cost are consistent with previous pragmatic research on the implementation of comprehensive telehealth programmes for LTCs

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Compiles peer-reviewed research and literature reviews on issues regarding patient safety and quality of care, ranging from evidence-based practice, patient-centered care, and nurses' working conditions to critical opportunities and tools for improvement

Machine Learning Modelling of Critical Care Patients in the Intensive Care Units

The ICU is a fast-paced data-rich environment which treats the most critically ill patients. On average, over 15 % of patients admitted to the ICU amount in mortality. Therefore, machine learning (ML) is paramount to aiding the optimisation and inference of insight in critical care. In addition, the early and accurate evaluation of the severity at the time of admission is significant for physicians. Such evaluations make patient management more effective as they are more likely to predict whose physical conditions may worsen. Moreover, ML techniques could potentially enhance patients' experience in the clinical setting by providing medical alerts and insight into future events occurring during hospitalisation. The need for interpretable models is crucial in the ICU and clinical setting, as it is vital to explain a decision that leads to any course of action related to an individual patient. This thesis primarily focuses on mortality, length of stay forecasting, and AF classification in critical care. We cover multiple outcomes and modelling methods whilst using multiple cohorts throughout the research. However, the analysis conducted throughout the thesis aims to create interpretable models for each modelling objective. In Chapter 3, we investigate three publicly available critical care databases containing multiple modalities of data and a wide range of parameters. We describe the processes and contemplations which must be considered to create actionable data for analysis in the ICU. Furthermore, we compared the three data sources using traditional statistical and ML methods and compared predictive performance. Based on 24 hours of sequential data, we achieved AUC performances of 79.5% for ICU mortality prediction and a prediction error of approximately 1.3 hours for ICU LOS. In Chapter 4, we investigate a sepsis cohort and conduct three sub-studies. Firstly, we investigated sepsis subtypes and compared biomarkers using traditional modelling methods. Next, we compare our approach to commonly and routinely used scoring systems in the ICU, such as APACHE IV and SOFA. Our tailored approach achieved superior performance with pulmonary and abdominal sepsis (AUC 0.74 and 0.71respectivly), displaying distinct individualities amongst the different sepsis groups. Next, we further expand our analysis by comparing ML methods and inference approaches to our baseline model and ICU acuity scores. We further investigate extending analysis to other outcomes of interest (In-hospital/ICU mortality, In-hospital/ICU LOS) to gain a more holistic view of the sepsis derivatives. This research shows that nonlinear models such as RF and GBM commonly outperformICU scoring, methods such as APACHE IV and SOFA and linear methods such as logistic/linear regression. Lastly, we extend our analysis in a multi-task learning framework for model optimisation and improved predictive performance. Our results showed superior performance with pulmonary, abdominal and renal/UTI sepsis (AUC 0.76, 0.77 and 0.73, respectively). Lastly, Chapter 5 investigates the classification of atrial fibrillation (AF) in long-lead ECG waveforms in sepsis patients. We developed a deep neural network to classify AF ECGs from Non-AF ECG cases in conjunction with refining a method to gain insight from the neural network model. We achieved a predictive performance of 0.99 and 0.89 regarding the test and external validation data. The inference from the model was achieved through the use of saliency maps, dimensionality reduction methods and clustering, utilising the automatic features learned by the developed model. We developed visualisations to help support the inference behind the classification of each ECG prediction. Overall, the research displays a wide range of novelties and unique approaches to solving various outcomes of interest in the ICU. In addition, this research demonstrates the implication of ML applicability in the ICU environment by providing insight and inference to diverse tasks regardless of the level of complexity. With further development, the frameworks and approaches outlined in this thesis have the potential to be used in clinical practice as decision-support tools in the ICU, allowing the automated alert and detection of patient classification, amongst others. The results generated in this thesis resulted in journal publications and medical understanding gained from insight available in the developed ML frameworks