Improving the Accuracy of Publicly Reported PSI Rates through Enhanced Internal Documentation Review

Patient Safety Indicators, or PSIs, are used by several healthcare related federal agencies and third-party payers to determine the quality of care being delivered by a healthcare provider. A composite PSI, PSI-90, includes a group of PSIs that are publicly reported as quality indicators for a provider, and that are used as part of the Value Based Purchasing calculation. Poor PSI-90 rates directly influence healthcare services reimbursement rates by CMS and may be considered an indication of a quality of care problem by potential patients and third party payers. This research is a case study on the effectiveness of a program implemented by the Medical University of South Carolina (MUHA) to improve the accuracy of their reported PSI-90 composite score

Patient-Specific Factors Associated with Surgical Delay in a Large Academic Hospital

The high cost of healthcare is driving the search for more efficient practice, especially in high-stakes locations like the operating room. In addition to financial losses, patients suffer physical and emotional distress, including an increased risk of morbidity or mortality when surgical cases are delayed due to inefficiency. While patient-related causes of delay have been implicated, it is unclear which specific factors are most significant. This study aimed to identify specific patient factors correlated with surgical delay and develop a predictive risk algorithm that describes the relationship between patient-specific factors and surgical delay. A retrospective review of 36,543 patients’ charts who underwent surgery at a large academic hospital over a 5-year period was conducted. Patient-specific factors, including demographics, insurance type, proximity to the hospital, anesthesia type, American Society of Anesthesiologists (ASA) classification, system-specific comorbidities, and medication usage, were identified. Bivariate analysis using chi-square analysis was conducted to determine if any of these factors were significantly correlated with surgical delay. The significant patient-specific factors were entered into a logistic regression model. Black race, ASA =\u3e3, renal failure, insulin, steroid, and several surgical specialties (colorectal, gynecologic oncology, hepatobiliary, neurosurgery, ophthalmology, and plastic surgery) were associated with an increased odds of surgical delay in this sample. Obesity, general anesthesia, and cardiovascular anesthesia were associated with a decreased odds of surgical delay. The model explains approximately 3.8-5.3% of surgical delays in this sample. The overall predictive rate of the model was 57.1%. Despite previous studies attributing a significant amount of surgical delay to patient factors, reasons other than patient factors were responsible for 94-95% of surgical delay in this sample. Further research in other populations or studies using different methods such as a prospective approach are necessary to fully understand the role of patient-specific factors in surgical delay. On the other hand, the power of this study permitted the discovery of seemingly small disparities that are nonetheless clinically significant. This study demonstrates that there are certain types of patients more at risk for surgical delay and therefore a diminished access to care

The Second International Conference on Health Information Technology Advancement

TABLE OF CONTENTS I. Message from the Conference Co-Chairs B. Han and S. Falan …………………………....….……………. 5 II. Message from the Transactions Editor H. Lee …...………..………….......………….……….………….... 7 III. Referred Papers A. Emerging Health Information Technology and Applications The Role of Mobile Technology in Enhancing the Use of Personal Health Records Mohamed Abouzahra and Joseph Tan………………….……………. 9 Mobile Health Information Technology and Patient Care: Methods, Themes, and Research Gaps Bahae Samhan, Majid Dadgar, and K. D. Joshi…………..…. 18 A Balanced Perspective to Perioperative Process Management Jim Ryan, Barbara Doster, Sandra Daily, and Carmen Lewis…..….…………… 30 The Impact of Big Data on the Healthcare Information Systems Kuo Lane Chen and Huei Lee………….…………… 43 B. Health Care Communication, Literacy, and Patient Care Quality Digital Illness Narratives: A New Form of Health Communication Jofen Han and Jo Wiley…..….……..…. 47 Relationships, Caring, and Near Misses: Michael’s Story Sharie Falan and Bernard Han……………….…..…. 53 What is Your Informatics Skills Level? -- The Reliability of an Informatics Competency Measurement Tool Xiaomeng Sun and Sharie Falan.….….….….….….…. 61 C. Health Information Standardization and Interoperability Standardization Needs for Effective Interoperability Marilyn Skrocki…………………….…….………….… 76 Data Interoperability and Information Security in Healthcare Reid Berryman, Nathan Yost, Nicholas Dunn, and Christopher Edwards.…. 84 Michigan Health Information Network (MiHIN) Shared Services vs. the HIE Shared Services in Other States Devon O’Toole, Sean O’Toole, and Logan Steely…..……….…… 94 D. Health information Security and Regulation A Threat Table Based Approach to Telemedicine Security John C. Pendergrass, Karen Heart, C. Ranganathan, and V.N. Venkatakrishnan …. 104 Managing Government Regulatory Requirements for Security and Privacy Using Existing Standard Models Gregory Schymik and Dan Shoemaker…….…….….….… 112 Challenges of Mobile Healthcare Application Security Alan Rea………………………….……………. 118 E. Healthcare Management and Administration Analytical Methods for Planning and Scheduling Daily Work in Inpatient Care Settings: Opportunities for Research and Practice Laila Cure….….……………..….….….….… 121 Predictive Modeling in Post-reform Marketplace Wu-Chyuan Gau, Andrew France, Maria E. Moutinho, Carl D. Smith, and Morgan C. Wang…………...…. 131 A Study on Generic Prescription Substitution Policy as a Cost Containment Approach for Michigan’s Medicaid System Khandaker Nayeemul Islam…….…...……...………………….… 140 F. Health Information Technology Quality Assessment and Medical Service Delivery Theoretical, Methodological and Practical Challenges in Designing Formative Evaluations of Personal eHealth Tools Michael S. Dohan and Joseph Tan……………….……. 150 The Principles of Good Health Care in the U.S. in the 2010s Andrew Targowski…………………….……. 161 Health Information Technology in American Medicine: A Historical Perspective Kenneth A. Fisher………………….……. 171 G. Health Information Technology and Medical Practice Monitoring and Assisting Maternity-Infant Care in Rural Areas (MAMICare) Juan C. Lavariega, Gustavo Córdova, Lorena G Gómez, Alfonso Avila….… 175 An Empirical Study of Home Healthcare Robots Adoption Using the UTUAT Model Ahmad Alaiad, Lina Zhou, and Gunes Koru.…………………….….………. 185 HDQM2: Healthcare Data Quality Maturity Model Javier Mauricio Pinto-Valverde, Miguel Ángel Pérez-Guardado, Lorena Gomez-Martinez, Martha Corrales-Estrada, and Juan Carlos Lavariega-Jarquín.… 199 IV. A List of Reviewers …………………………..…….………………………208 V. WMU – IT Forum 2014 Call for Papers …..…….…………………20

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

Non-Psychiatric Hospitalization For Patients With Psychotic Disorders: A Mixed-Methods Study

Patients with psychotic disorders face a multitude of medical health disparities in addition to psychological symptoms. They have a higher medical disease burden than the general population and are more likely to have a non-psychiatric hospitalization. In the hospital, these patients have an increased risk of adverse events, readmission and extended length-of-stay. Working with a Health Equity lens and the Quality Health Outcomes Model, we reviewed the literature on adverse events during medical-surgical hospitalizations for these patients and identified differences at the patient, provider and system levels between these patients and the general population. Next, a mixed methods, exploratory sequential study was conducted to: 1) explore the experience of patients with psychotic disorders hospitalized on medical-surgical unit; 2) examine patient characteristics and care processes associated with length-of-stay (primary outcome), adverse events and readmissions (secondary outcomes), among patients with psychotic disorders during non-psychiatric hospitalizations; and 3) integrate qualitative and quantitative data to contextualize factors associated with hospital outcomes among patients with psychotic disorders during non-psychiatric hospitalizations. For Phase 1, interviews were conducted with twenty patients with psychotic disorders on medical-surgical units. Five themes were developed through thematic analysis: 1) managing through hard times, 2) ignored and treated unfairly, 3) actively involved in health, 4) appreciation of caring providers and 5) violence: expected and experienced. In Phase 2, information from these interviews guided variable selection for an analysis of patient hospital records. A general linear model was conducted to examine length-of-stay’s relationship with patient characteristics and care processes. Of patient characteristics, only medical comorbidities were significantly related to length-of-stay. Certain processes of care highlighted by patients from the qualitative sample were found to be associated with length-of-stay like physical restraints (64% longer), psychiatrist consult (20% longer) and outpatient appointment in the previous six months (10% shorter). Results suggest specific patient characteristics and care processes are highly related to length-of-stay and that many of these were important to the patients in the qualitative portion. The use of mixed methods research for hospital outcomes research in this population creates valuable information for educational and clinical settings to improve care for patients with psychotic disorders

Doctor of Philosophy

DissertationHealth information technology (HIT) in conjunction with quality improvement (QI) methodologies can promote higher quality care at lower costs. Unfortunately, most inpatient hospital settings have been slow to adopt HIT and QI methodologies. Successful adoption requires close attention to workflow. Workflow is the sequence of tasks, processes, and the set of people or resources needed for those tasks that are necessary to accomplish a given goal. Assessing the impact on workflow is an important component of determining whether a HIT implementation will be successful, but little research has been conducted on the impact of eMeasure (electronic performance measure) implementation on workflow. One solution to addressing implementation challenges such as the lack of attention to workflow is an implementation toolkit. An implementation toolkit is an assembly of instruments such as checklists, forms, and planning documents. We developed an initial eMeasure Implementation Toolkit for the heart failure (HF) eMeasure to allow QI and information technology (IT) professionals and their team to assess the impact of implementation on workflow. During the development phase of the toolkit, we undertook a literature review to determine the components of the toolkit. We conducted stakeholder interviews with HIT and QI key informants and subject matter experts (SMEs) at the US Department of Veteran Affairs (VA). Key informants provided a broad understanding about the context of workflow during eMeasure implementation. Based on snowball sampling, we also interviewed other SMEs based on the recommendations of the key informants who suggested tools and provided information essential to the toolkit development. The second phase involved evaluation of the toolkit for relevance and clarity, by experts in non-VA settings. The experts evaluated the sections of the toolkit that contained the tools, via a survey. The final toolkit provides a distinct set of resources and tools, which were iteratively developed during the research and available to users in a single source document. The research methodology provided a strong unified overarching implementation framework in the form of the Promoting Action on Research Implementation in Health Services (PARIHS) model in combination with a sociotechnical model of HIT that strengthened the overall design of the study