Data-Driven Decision Making in Healthcare

The increasing availability of healthcare data has provided a great opportunity for the development of data-driven models to guide health policy and medical practice. The objective of this dissertation is to present new methods that use these data to make better healthcare decisions at a population and patient level. We first model the supply, demand, and allocation of organs for transplantation using data from the Organ Procurement and Transplantation Network and the US Census Bureau. Then, we introduce personalized treatment plans and genetic testing strategies for the management of cardiovascular diseases. We evaluate the clinical and policy implications of the treatment and testing strategies at a population level using data from the National Health and Nutrition Examination Survey. Lastly, we propose a modeling framework to consider physicians' judgment and patients' preferences in the implementation of treatment protocols. To illustrate how this method can be implemented in medical practice, we find ranges of near-optimal antihypertensive treatment choices for 16.72 million adults in the US. This research has the potential to improve healthcare practice by giving flexible and achievable guidelines to policymakers and medical professionals based on patient and population-level data.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167908/1/wmarrero_1.pd

Separator fluid volume requirements in multi-infusion settings

INTRODUCTION. Intravenous (IV) therapy is a widely used method for the administration of medication in hospitals worldwide. ICU and surgical patients in particular often require multiple IV catheters due to incompatibility of certain drugs and the high complexity of medical therapy. This increases discomfort by painful invasive procedures, the risk of infections and costs of medication and disposable considerably. When different drugs are administered through the same lumen, it is common ICU practice to flush with a neutral fluid between the administration of two incompatible drugs in order to optimally use infusion lumens. An important constraint for delivering multiple incompatible drugs is the volume of separator fluid that is sufficient to safely separate them. OBJECTIVES. In this pilot study we investigated whether the choice of separator fluid, solvent, or administration rate affects the separator volume required in a typical ICU infusion setting. METHODS. A standard ICU IV line (2m, 2ml, 1mm internal diameter) was filled with methylene blue (40 mg/l) solution and flushed using an infusion pump with separator fluid. Independent variables were solvent for methylene blue (NaCl 0.9% vs. glucose 5%), separator fluid (NaCl 0.9% vs. glucose 5%), and administration rate (50, 100, or 200 ml/h). Samples were collected using a fraction collector until <2% of the original drug concentration remained and were analyzed using spectrophotometry. RESULTS. We did not find a significant effect of administration rate on separator fluid volume. However, NaCl/G5% (solvent/separator fluid) required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). Also, G5%/G5% required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). The significant decrease in required flushing volume might be due to differences in the viscosity of the solutions. However, mean differences were small and were most likely caused by human interactions with the fluid collection setup. The average required flushing volume is 3.7 ml. CONCLUSIONS. The choice of separator fluid, solvent or administration rate had no impact on the required flushing volume in the experiment. Future research should take IV line length, diameter, volume and also drug solution volumes into account in order to provide a full account of variables affecting the required separator fluid volume

ESICM LIVES 2017 : 30th ESICM Annual Congress. September 23-27, 2017.

INTRODUCTION. Unplanned readmission to intensive care is highly undesirable in that it contributes to increased variance in care, disruption, difficulty in resource allocation and may increase length of stay and mortality particularly if subject to delays. Unlike the ICU admission from the ward, readmission prediction has received relatively little attention, perhaps in part because at the point of ICU discharge, full physiological information is systematically available to the clinician and so it is expected that readmission should be largely due to unpredictable factors. However it may be that there are multidimensional trends that are difficult for the clinician to perceive that may nevertheless be predictive of readmission. OBJECTIVES. We investigated whether machine learning (ML) techniques could be used to improve on the simple published SWIFT score [1] for the prediction of unplanned readmission to ICU within 48 hours. METHODS. We extracted systolic BP, pulse pressure, heart and respiration rate, temperature, SpO2, bilirubin, creatinine, INR, lactate, white cell count, platelet count, pH, FiO2, and total Glasgow Coma Score from ICU stays of over 2000 adult patients from our hospital electronic patient record system. We trained our own custom multidimensional / time-sensitive algorithmic ML system to predict failed discharges defined as either readmission or unexpected death within 48 hours of discharge. We used 10-fold cross validation to assess performance. We also assessed the effect of augmenting our system by transfer learning (TL) with 44,000 additional cases from the MIMIC III database. RESULTS. The SWIFT score performed relatively poorly with an AUROC of around 0.6 which our ML system trained on local data was also able to match. However when augmented with an additional dataset by TL, the AUROC for the ML system improved statistically and clinically significantly to over 0.7. CONCLUSIONS. Machine learning is able to improve on predictors based on simple multiple logistic regression. Thus there is likely to be information in the trends and in combinations of variables. A disadvantage with this technique is that ML approaches require large amounts of data for training. However, ML approaches can be improved by TL. Basing prediction models on locally derived data augmented by TL is a potentially novel approach to generating tools that customised to the institution yet can exploit the potential power of ML algorithms. REFERENCES [1] Gajic O, Malinchoc M, Comfere TB, et al. The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med. 2008;36(3):676–82. Grant Acknowledgement This work was internally funded

Exploring perspectives of people with type-1 diabetes on goalsetting strategies within self-management education and care

Background. Collaborative goal-setting strategies are widely recommended for diabetes self-management support within healthcare systems. Creating self-management plans that fit with peoples’ own goals and priorities has been linked with better diabetic control. Consequently, goal-setting has become a core component of many diabetes selfmanagement programmes such as the ‘Dose Adjustment for Normal Eating (DAFNE) programme’. Within DAFNE, people with Type-1 Diabetes (T1D) develop their own goals along with action-plans to stimulate goal-achievement. While widely implemented, limited research has explored how goal-setting strategies are experienced by people with diabetes.Therefore, this study aims to explore the perspectives of people with T1D on theimplementation and value of goal-setting strategies within DAFNE and follow-up diabetes care. Furthermore, views on barriers and facilitators to goal-attainment are explored.Methods. Semi-structured interviews were conducted with 20 people with T1D who attended a DAFNE-programme. Following a longitudinal qualitative research design, interviews took place 1 week, and 6-8 months after completion of DAFNE. A recurrent cross-sectional approach is applied in which themes will be identified at each time-point using thematic analyses.Expected results. Preliminary identified themes surround the difference in value that participants place on goal-setting strategies, and the lack of support for goal-achievement within diabetes care.Current stage. Data collection complete; data-analysis ongoing.Discussion. Goal-setting strategies are increasingly included in guidelines for diabetes support and have become essential parts of many primary care improvement schemes. Therefore, exploring the perspectives of people with T1D on the value and implementation of goal-setting strategies is vital for their optimal application