Simulation and Modeling for Improving Access to Care for Underserved Populations

Indiana University-Purdue University Indianapolis (IUPUI)This research, through partnership with seven Community Health Centers (CHCs) in Indiana, constructed effective outpatient appointment scheduling systems by determining care needs of CHC patients, designing an infrastructure for meaningful use of patient health records and clinic operational data, and developing prediction and simulation models for improving access to care for underserved populations. The aims of this study are 1) redesigning appointment scheduling templates based on patient characteristics, diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive modeling to improve understanding the complexity of appointment adherence in underserved populations; and 3) developing simulation models with complex data to guide operational decision-making in community health centers. This research addresses its aims by applying a multi-method approach from different disciplines, such as statistics, industrial engineering, computer science, health informatics, and social sciences. First, a novel method was developed to use Electronic Health Record (EHR) data for better understanding appointment needs of the target populations based on their characteristics and reasons for seeking health, which helped simplify, improve, and redesign current appointment type and duration models. Second, comprehensive and informative predictive models were developed to better understand appointment non-adherence in community health centers. Logistic Regression, Naïve Bayes Classifier, and Artificial Neural Network found factors contributing to patient no-show. Predictors of appointment non-adherence might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems in CHCs, and necessary steps to extract information for simulation modeling of scheduling systems in CHCs are described. Agent-Based Models were built in AnyLogic to test different scenarios of scheduling methods, and to identify how these scenarios could impact clinic access performance. This research potentially improves well-being of and care quality and timeliness for uninsured, underinsured, and underserved patients, and it helps clinics predict appointment no-shows and ensures scheduling systems are capable of properly meeting the populations’ care needs.2021-12-2

Planning for Continuity of Services: A Comprehensive Strategic Assessment Model for Healthcare Business Continuity Planning

With the release of the 2016 Centers for Medicare and Medicaid Services’ (CMS) requirements for healthcare institutions to implement business continuity planning into their organizations by November 15, 2017, the focus of business continuity and disaster recovery planning solely for information services has now transitioned into an enterprise-wide requirement. Over the past decade, there have been increasing numbers of naturally occurring and man-made disasters that have significantly interrupted or altogether closed healthcare facilities, impacting the health and well-being of entire communities. This study examines the changing regulatory landscape that requires healthcare institutions to develop, maintain, and regularly test their business continuity plans in an effort to enhance their operational resiliency. After a retrospective review of regulations, guidelines, and best practices, this study pilots an addition to the Kaiser Permanente hazard vulnerability assessment (HVA) tool that is intended to enable healthcare organizations to objectively identify, prioritize, and maintain their business continuity and emergency management planning efforts through the identification of potential operational and financial impacts to healthcare facilities during and following disasters. The major benefits of this study are to identify the historical shortcomings of a healthcare facility’s hazard and risk identification processes and to facilitate the use of the information collected during that process. Identified inadequacies from past healthcare preparedness efforts will be used to form new meaningful efforts to enhance the recognition of risks to healthcare organizations, in an effort to enhance their resiliency to interruptions of services and to minimize financial losses during austere events

Data completeness in healthcare: A literature survey.

As the adoption of eHealth has made it easier to access and aggregate healthcare data, there has been growing application for clinical decisions, health services planning, and public health monitoring with daily collected data in clinical care. Reliable data quality is a precursor of the aforementioned tasks. There is a body of research on data quality in healthcare, however, a clear picture of data completeness in this field is missing. This research aims to identify and classify current research themes related to data completeness in healthcare. In addition, the paper presents problems with data completeness in the reviewed literature and identifies methods that have been adopted to address those problems. This study has reviewed 24 papers (January 2011–April 2016) published in information and computing sciences, biomedical engineering, and medicine and health sciences journals. The paper uncovers three main research themes, including design and development, evaluation, and determinants. In conclusion, this paper improves our understanding of the current state of the art of data completeness in healthcare records and indicates future research directions.N

Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Compiles peer-reviewed research and literature reviews on issues regarding patient safety and quality of care, ranging from evidence-based practice, patient-centered care, and nurses' working conditions to critical opportunities and tools for improvement

Evidence-Based Practice Screening Protocol to Integrate Physical Health Services into a Behavioral Health Center

ABSTRACT Individuals aged 18 or older who suffer from a serious mental illness (SMI) often have coexisting chronic physical health problems such as diabetes, hypertension, obesity, and cardiovascular disease. Multiple providers in various settings provide care for individuals with co-morbid SMI and physical health problems. Early, effective and efficient screening leads to successful treatment and management of patients with both SMI and chronic physical health complications. The purpose of this DNP project was to develop and implement an evidence-based integrated screening protocol for adult males (18 and over) diagnosed with SMI and co-morbid physical health problems who presented for care in a community based mental health center. A practice protocol was developed and implemented as a key part of the community-based mental health center clinical pathway. The protocol focused on comprehensive care management and care coordination for health and clinical services to include early screening and referrals. Two registered nurses screened 35 adult males with a SMI diagnosis for physical health complications. The mean age for the individuals screened was 41.88 years; 40% (14) were smokers; 23% (8) had elevated glucose levels; 20% (7) had hypertension; and 17% (6) were obese. No referrals to a primary care provider were completed. The evidence-based screening protocol for identifying physical health problems in individuals with SMI was effective. The development of the protocol improved quality of care delivery through screening, to identify individuals who would necessitate a referral to a primary care provider

A big data augmented analytics platform to operationalize efficiencies at community clinics

Indiana University-Purdue University Indianapolis (IUPUI)Community Health Centers (CHCs) play a pivotal role in delivery of primary healthcare to the underserved, yet have not benefited from a modern data analytics platform that can support clinical, operational and financial decision making across the continuum of care. This research is based on a systems redesign collaborative of seven CHC organizations spread across Indiana to improve efficiency and access to care. Three research questions (RQs) formed the basis of this research, each of which seeks to address known knowledge gaps in the literature and identify areas for future research in health informatics. The first RQ seeks to understand the information needs to support operations at CHCs and implement an information architecture to support those needs. The second RQ leverages the implemented data infrastructure to evaluate how advanced analytics can guide open access scheduling – a specific use case of this research. Finally, the third RQ seeks to understand how the data can be visualized to support decision making among varying roles in CHCs. Based on the unique work and information flow needs uncovered at these CHCs, an end to-end analytics solution was designed, developed and validated within the framework of a rapid learning health system. The solution comprised of a novel heterogeneous longitudinal clinic data warehouse augmented with big data technologies and dashboard visualizations to inform CHCs regarding operational priorities and to support engagement in the systems redesign initiative. Application of predictive analytics on the health center data guided the implementation of open access scheduling and up to a 15% reduction in the missed appointment rates. Performance measures of importance to specific job profiles within the CHCs were uncovered. This was followed by a user-centered design of an online interactive dashboard to support rapid assessments of care delivery. The impact of the dashboard was assessed over time and formally validated through a usability study involving cognitive task analysis and a system usability scale questionnaire. Wider scale implementation of the data aggregation and analytics platform through regional health information networks could better support a range of health system redesign initiatives in order to address the national ‘triple aim’ of healthcare

Strategies for Identifying and Selecting Performance Measures of Effectiveness for Nonprofit Organizations

There is a growing demand for accountability of nonprofit organizations, and nonprofit business leaders are increasingly under pressure to demonstrate operational effectiveness. The problem is that some business leaders of nonprofit organizations lack strategies for identifying and selecting actionable performance measures of operational effectiveness. Using the plan-do-study-act conceptual framework, this single case study of a nonprofit organization located in the mid-Atlantic region of United States was conducted to explore strategies that 3 of its business leaders used to identify and select actionable performance measures of operational effectiveness. Using thematic analysis of data collected from semistructured interviews, documents, and public sources, emergent themes included: (a) usefulness of measures, (b) customer experience, and (c) workforce education. The findings of this study may have implications for social change by helping nonprofit business leaders achieve consensus on measures of effectiveness beyond financial measures. Additionally, the findings could support the usefulness of transparency in reporting performance outcomes, encourage a shift in focus from program spending and ratios to effectiveness, and prompt external stakeholders to expect performance measures that demonstrate effectiveness in nonprofit program operations

Mentoring Nursing Leaders to Foster Frontline Accountability and Engagement in Continuous Performance/Process Improvement through the Utilization of Team Huddles and a Huddle Board

BACKGROUND: Establishing a connection for staff between the work being done and the associated implications and outcomes on patient safety and quality care delivery is not often a simple task for any nursing leader. Team huddles and huddle boards aim to establish and foster frontline accountability and engagement for continuous process and performance improvement. The setting of the project was a 17-bed acute care inpatient unit specializing in neurosciences within an academic medical center. Participants included nursing leaders and staff of the unit. METHODS: Consultation and mentorship in conjunction with standard work were utilized for this project. The intervention was developed utilizing Kotter’s model of change. Literature review to identify Lean management best practices in nursing/healthcare was conducted. INTERVENTION: Team huddle/huddle board rollout nursing leader behaviors standard work was created, along with a team huddle/huddle board score card and satisfaction survey. Consultation and mentorship provided to nursing leaders and team over eight-week project period. Nursing leader ability to implement interventions/processes assessed and team huddle and huddle board components scored weekly. A post-implementation satisfaction survey was administered to the nursing leaders and team. RESULTS: Nursing leaders implemented all interventions and processes defined in standard work within the eight-week project period. Team huddles and huddle board possessed 100% of required components by week six and were sustained through the end of the project period. Over 90% of project participants responded as ‘agree’ or ‘strongly agree’ on all six satisfaction survey items. CONCLUSION: Results suggest that nursing leaders can successfully implement team huddles and huddle boards through consultation and mentorship and the utilization of standard work. Team huddles and huddle boards can benefit individual and team dynamics such as information sharing, problem solving, work environment, and communication

The Impact of Individual Learning on Electronic Health Record Routinization: An Empirical Study

Since the passage of the HITECH Act, adoption of electronic health records (EHR) has increased significantly EHR refers to an electronic version of a patient’s medical history. The adoption of EHR has potential to reduce medical errors, duplication of testing, and delays in treatment. However, current literature indicates that implementation of EHR is not resulting in the automatic routinization of EHR. Routinization refers to the notion that truly successful technological innovations are no longer perceived as being new or out-of-the-ordinary. The complexity of EHRs allow individual users to use these systems at different levels of sophistication. Research shows that healthcare professionals are using non-standard ways to use or circumvent the EHR to complete their work and are limited in EHR systems use. Further, although workarounds may seem necessary to physicians and are not perceived to be problematic, they can pose a threat to patient safety and hinder the potential benefits. Hence, we argue the EHR implementations are limited in their potential due to the lack of routinization. Any new technological innovation requires the physician support and willingness to learn about the system to move to the routinization phase of implementation. Hence, we draw from the literature on organization learning, individual learning, and routines to understand factors that influence EHR routinization