A HYBRID METHODOLOGY FOR MODELING RISK OF ADVERSE EVENTS IN COMPLEX HEALTHCARE SETTINGS

Despite efforts to provide safe, effective medical care, adverse events still occur with some regularity. While risk cannot be entirely eliminated from healthcare activities, an important goal is to develop effective and durable mitigation strategies to render the system `safer'. In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the healthcare domain, this can be extremely challenging due to the wide variability in the way that healthcare processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study we have developed a generic methodology for evaluating dynamic changes in adverse event risk in acute care hospitals as a function of organizational and non-organizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational level and policy level contributions to risk evolve over time, and how policies and decisions may affect the general system-level contribution to adverse event risk. It also captures the feedback of organizational factors and decisions over time and the non-linearities in these feedback effects. Second, Bayesian Belief Network (BBN) framework is used to represent patient-level factors and also physician level decisions and factors in the management of an individual patient, which contribute to the risk of hospital-acquired adverse event. The model is intended to support hospital decisions with regards to staffing, length of stay, and investment in safeties, which evolve dynamically over time. The methodology has been applied in modeling the two types of common adverse events; pressure ulcers and vascular catheter-associated infection, and has been validated with eight years of clinical data

Evidence-Based Hospitals

In 2011 the University of Kentucky opened the first two inpatient floors of its new hospital. With an estimated cost of over $872 million, the new facility represents a major investment in the future of healthcare in Kentucky. This facility is outfitted with many features that were not present in the old hospital, with the expectation that they would improve the quality and efficiency of patient care. After one year of occupancy, hospital administration questioned the effectiveness of some features. Through focus groups of key stakeholders, surveys of frontline staff, and direct observational data, this dissertation evaluates the effectiveness of two such features, namely the ceiling-based patient lifts and the placement of large team meeting spaces on every unit, while also describing methods that can improve the overall state of quality improvement research in healthcare

Reusability of EMR Data for Applying Cubbin and Jackson Pressure Ulcer Risk Assessment Scale in Critical Care Patients

OBJECTIVES: The purposes of this study were to examine the predictive validity of the Cubbin and Jackson pressure ulcer risk assessment scale for the development of pressure ulcers in intensive care unit (ICU) patients retrospectively and to evaluate the reusability of Electronic Medical Records (EMR) data. METHODS: A retrospective design was used to examine 829 cases admitted to four ICUs in a tertiary care hospital from May 2010 to April 2011. Patients who were without pressure ulcers at admission to ICU, 18 years or older, and had stayed in ICU for 24 hours or longer were included. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) were calculated. RESULTS: The reported incidence rate of pressure ulcers among the study subjects was 14.2%. At the cut-off score of 24 of the Cubbin and Jackson scale, the sensitivity, specificity, positive predictive value, negative predictive value, and AUC were 72.0%, 68.8%, 27.7%, 93.7%, and 0.76, respectively. Eight items out 10 of the Cubbin and Jackson scale were readily available in the EMR data. CONCLUSIONS: The Cubbin and Jackson scale performed slightly better than the Braden scale to predict pressure ulcer development. Eight items of the Cubbin and Jackson scale except mobility and hygiene can be extracted from the EMR, which initially demonstrated the reusability of EMR data for pressure ulcer risk assessment. If the Cubbin and Jackson scale is a part of the EMR assessment form, it would help nurses perform tasks to effectively prevent pressure ulcers with an EMR alert for high-risk patients.ope

Clinical decision support systems in the care of hospitalised patients with diabetes

This thesis explored the role of health informatics (decision support systems) in caring for hospitalised patients with diabetes through a systematic review and by analysing data from University Hospital Birmingham, UK. Findings from the thesis: 1) highlight the potential role of computerised physician order entry system in improving guideline based anti-diabetic medication prescription in particular insulin prescription, and their effectiveness in contributing to better glycaemic control; 2) quantify the occurrence of missed discharge diagnostic codes for diabetes using electronic prescription data and suggests 60% of this could be potentially reduced using an algorithm that could be introduced as part of the information system; 3) found that hypoglycaemia and foot disease in hospitalised diabetes patients were independently associated with higher in-hospital mortality rates and longer length of stay; 4) quantify the hypoglycaemia rates in non-diabetic patients and proposes one method of establishing a surveillance system to identify non diabetic hypoglycaemic patients; and 5) introduces a prediction model that may be useful to identify patients with diabetes at risk of poor clinical outcomes during their hospital stay

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science\u27s ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploratio

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Nursing-Relevant Patient Outcomes and Clinical Processes in Data Science Literature: 2019 Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this paper, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (e.g., natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope the studies described in this paper help readers: (a) understand the breadth and depth of data science’s ability to improve clinical processes and patient outcomes that are relevant to nurses and (b) identify gaps in the literature that are in need of exploration