From Data to Decision: An Implementation Model for the Use of Evidence-based Medicine, Data Analytics, and Education in Transfusion Medicine Practice

Healthcare in the United States is underperforming despite record increases in spending. The causes are as myriad and complex as the suggested solutions. It is increasingly important to carefully assess the appropriateness and cost-effectiveness of treatments especially the most resource-consuming clinical interventions. Healthcare reimbursement models are evolving from fee-for-service to outcome-based payment. The Patient Protection and Affordable Care Act has added new incentives to address some of the cost, quality, and access issues related to healthcare, making the use of healthcare data and evidence-based decision-making essential strategies. However, despite the great promise of these strategies, the transition to data-driven, evidence-based medical practice is complex and faces many challenges. This study aims to bridge the gaps that exist between data, knowledge, and practice in a healthcare setting through the use of a comprehensive framework to address the administrative, cultural, clinical, and technical issues that make the implementation and sustainability of an evidence-based program and utilization of healthcare data so challenging. The study focuses on promoting evidence-based medical practice by leveraging a performance management system, targeted education, and data analytics to improve outcomes and control costs. The framework was implemented and validated in transfusion medicine practice. Transfusion is one of the top ten coded hospital procedures in the United States. Unfortunately, the costs of transfusion are underestimated and the benefits to patients are overestimated. The particular aim of this study was to reduce practice inconsistencies in red blood cell transfusion among hospitalists in a large urban hospital using evidence-based guidelines, a performance management system, recurrent reporting of practice-specific information, focused education, and data analytics in a continuous feedback mechanism to drive appropriate decision-making prior to the decision to transfuse and prior to issuing the blood component. The research in this dissertation provides the foundation for implementation of an integrated framework that proved to be effective in encouraging evidence-based best practices among hospitalists to improve quality and lower costs of care. What follows is a discussion of the essential components of the framework, the results that were achieved and observations relative to next steps a learning healthcare organization would consider

Deploying a Validated Postnatal Depression Screening Tool & Guideline to Improve Evidence Based Screening for Postpartum Depression in Ambulatory Care

Problem Description: Postpartum depression (PPD) is a significant public health problem that is potentially disabling and can be life-threatening. It is one of the most common diagnoses for maternal morbidity and mortality, affecting one in ten women in the United States. Currently, there is no universal process for the identification of PPD within the ambulatory clinics in this regional health system caring for obstetrical patients. A quality improvement project was developed and implemented with a pilot group in the ambulatory setting. Rationale: Without a standard process for screening, patients and their newborns may be at increased risk for detrimental consequences of PPD. The goal is to improve knowledge of a validated, evidence-based perinatal postpartum depression screening tool, and improve screening for postpartum depression with the tool at patient’s comprehensive post-birth appointments. Interventions: Following a detailed literature review, best practice interventions were implemented. The project sites postpartum depression screening (PPDS) tool was updated to the Edinburgh Postnatal Depression Scale (EPDS). Education was developed and presented regarding the project aims. Success of the interventions were measured with a postpartum depression knowledge questionnaire, an ambulatory EPDS guideline training assessment, chart audits, and an ambulatory EPDS project Evaluation. Results: The pre- and post- assessments with the postpartum depression knowledge questionnaire indicated an overall knowledge increase of 11.4% regarding the EPDS, effects of PPD on mother and baby, and local PPD statistics. By the end of the specified project period 100% of the qualified patients were being screened at the recommended time with the validated evidenced-based EPDS; the screening for PPD improvement rate increased overall by 37%. Virtual education was received positively with recommendations to continue rounding for inperson onsite project management support. There was a realization to the participants that PPD is more prevalent locally. The project evaluation highlighted the recommendations for more mental health providers that are accessible to this population. Interpretation: In the current setting, education related to PPD increased the participants confidence in screening. The screening rate for PPD improved during the project from 63.1% to 100%. Additional goals were realized in that a standard approach with the EPDS is now part of the project sites practice and staff are trained in the use of the EPDS. Conclusion: Statistics and evidence continues to evolve as it relates to PPD and the overall public health impact. The CDC updated national PPD statistics for women from affecting one in ten to affecting one in eight since initiation of this project. The quality improvement project was successful in improving knowledge and increasing postpartum screening rates within an ambulatory setting in a health system in the northwestern United States. It is recommended to continue to implement use of the EPDS with education and knowledge validation throughout the health system as evidence states the continued focused efforts will lead to improved maternal-child health outcomes

Exploring the Intersection of the Digital Divide and Artificial Intelligence: A Hermeneutic Literature Review

Given the rapid advancements in information communication technology (ICT), researchers and practitioners need to understand the impact that emerging phenomena, such as artificial intelligence (AI), have on existing social and economic challenges. We conducted a hermeneutic literature review to present the current state of the digital divide, developments in AI, and AI’s potential impact on the digital divide. We propose three theoretical framings: 1) conceptualizing the divide, 2) modeling the divide, and 3) analyzing the divide. These framings synthesize the digital divide’s essence in relation to AI and provide the foundation for a socio-technical research agenda for the digital divide in light of the evolving phenomena of AI

From the digital data revolution to digital health and digital economy toward a digital society: Pervasiveness of Artificial Intelligence

Technological progress has led to powerful computers and communication technologies that penetrate nowadays all areas of science, industry and our private lives. As a consequence, all these areas are generating digital traces of data amounting to big data resources. This opens unprecedented opportunities but also challenges toward the analysis, management, interpretation and utilization of these data. Fortunately, recent breakthroughs in deep learning algorithms complement now machine learning and statistics methods for an efficient analysis of such data. Furthermore, advances in text mining and natural language processing, e.g., word-embedding methods, enable also the processing of large amounts of text data from diverse sources as governmental reports, blog entries in social media or clinical health records of patients. In this paper, we present a perspective on the role of artificial intelligence in these developments and discuss also potential problems we are facing in a digital society

DataGauge: A Model-Driven Framework for Systematically Assessing the Quality of Clinical Data for Secondary Use

There is growing interest in the reuse of clinical data for research and clinical healthcare quality improvement. However, direct analysis of clinical data sets can yield misleading results. Data Cleaning is often employed as a means to detect and fix data issues during analysis but this approach lacks of systematicity. Data Quality (DQ) assessments are a more thorough way of spotting threats to the validity of analytical results stemming from data repurposing. This is because DQ assessments aim to evaluate ‘fitness for purpose’. However, there is currently no systematic method to assess DQ for the secondary analysis of clinical data. In this dissertation I present DataGauge, a framework to address this gap in the state of the art. I begin by introducing the problem and its general significance to the field of biomedical and clinical informatics (Chapter 1). I then present a literature review that surveys current methods for the DQ assessment of repurposed clinical data and derive the features required to advance the state of the art (Chapter 2). In chapter 3 I present DataGauge, a model-driven framework for systematically assessing the quality of repurposed clinical data, which addresses current limitations in the state of the art. Chapter 4 describes the development of a guidance framework to ensure the systematicity of DQ assessment design. I then evaluate DataGauge’s ability to flag potential DQ issues in comparison to a systematic state of the art method. DataGauge was able to increase ten fold the number of potential DQ issues found over the systematic state of the art method. It identified more specific issues that were a direct threat to fitness for purpose, but also provided broader coverage of the clinical data types and knowledge domains involved in secondary analyses. DataGauge sets the groundwork for systematic and purpose-specific DQ assessments that fully integrate with secondary analysis workflows. It also promotes a team-based approach and the explicit definition of DQ requirements to support communication and transparent reporting of DQ results. Overall, this work provides tools that pave the way to a deeper understanding of repurposed clinical dataset limitations before analysis. It is also a first step towards the automation of purpose-specific DQ assessments for the secondary use of clinical data. Future work will consist of further development of these methods and validating them with research teams making secondary use of clinical data