A novel framework for predicting patients at risk of readmission

Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income.

Starting antidepressant use: a qualitative synthesis of UK and Australian data

Objective To explore people's experiences of starting antidepressant treatment. Design Qualitative interpretive approach combining thematic analysis with constant comparison. Relevant coding reports from the original studies (generated using NVivo) relating to initial experiences of antidepressants were explored in further detail, focusing on the ways in which participants discussed their experiences of taking or being prescribed an antidepressant for the first time. Participants 108 men and women aged 22–84 who had taken antidepressants for depression. Setting Respondents recruited throughout the UK during 2003–2004 and 2008 and 2012–2013 and in Australia during 2010–2011. Results People expressed a wide range of feelings about initiating antidepressant use. People's attitudes towards starting antidepressant use were shaped by stereotypes and stigmas related to perceived drug dependency and potentially extreme side effects. Anxieties were expressed about starting use, and about how long the antidepressant might begin to take effect, how much it might help or hinder them, and about what to expect in the initial weeks. People worried about the possibility of experiencing adverse effects and implications for their senses of self. Where people felt they had not been given sufficient time during their consultation information or support to take the medicines, the uncertainty could be particularly unsettling and impact on their ongoing views on and use of antidepressants as a viable treatment option. Conclusions Our paper is the first to explore in-depth patient existential concerns about start of antidepressant use using multicountry data. People need additional support when they make decisions about starting antidepressants. Health professionals can use our findings to better understand and explore with patients’ their concerns before their patients start antidepressants. These insights are key to supporting patients, many of whom feel intimidated by the prospect of taking antidepressants, especially during the uncertain first few weeks of treatment.