Practical Use of Process Mapping to Guide Implementation of a Care Coordination Program for Rural Veterans.

BACKGROUND: Transitions of care are high risk for vulnerable populations such as rural Veterans, and adequate care coordination can alleviate many risks. Single-center care coordination programs have shown promise in improving transitional care practices. However, best practices for implementing effective transitional care interventions are unknown, and a common pitfall is lack of understanding of the current process at different sites. The rural Transitions Nurse Program (TNP) is a Veterans Health Administration (VA) intervention that addresses the unique transitional care coordination needs of rural Veterans, and it is currently being implemented in five VA facilities. OBJECTIVE: We sought to employ and study process mapping as a tool for assessing site context prior to implementation of TNP, a new care coordination program. DESIGN AND PARTICIPANTS: Observational qualitative study guided by the Lean Six Sigma approach. Data were collected in January-March 2017 through interviews, direct observations, and group sessions with front-line staff, including VA providers, nurses, and administrative staff from five VA Medical Centers and nine rural Patient-Aligned Care Teams. KEY RESULTS: We integrated key informant interviews, observational data, and group sessions to create ten process maps depicting the care coordination process prior to TNP implementation at each expansion site. These maps were used to adapt implementation through informing the unique role of the Transitions Nurse at each site and will be used in evaluating the program, which is essential to understanding the program's impact. CONCLUSIONS: Process mapping can be a valuable and practical approach to accurately assess site processes before implementation of care coordination programs in complex systems. The process mapping activities were useful in engaging the local staff and simultaneously guided adaptations to the TNP intervention to meet local needs. Our approach-combining multiple data sources while adapting Lean Six Sigma principles into practical use-may be generalizable to other care coordination programs.

Forecasting emergency department arrivals: a tutorial for emergency department directors.

This article is a tutorial for emergency department (ED) medical directors needing to anticipate ED arrivals in support of strategic, tactical, and operational planning and activities. The authors demonstrate our regression-based forecasting models based on data obtained from a large teaching hospital's ED. The versatility of the regression analysis is shown to readily accommodate a variety of forecasting situations. Trend regression analysis using annual ED arrival data shows the long-term growth. The monthly and daily variation in ED arrivals is captured using zero/one variables while Fourier regression effectively describes the wavelike patterns observed in hourly ED arrivals. In our study hospital, these forecasting methods uncovered: long-term growth in demand of about 1,000 additional arrivals per year; February was generally the slowest month of the year while July was the busiest month of the year; there were about 20 fewer arrivals on Fridays (the slowest day) than Sundays (the busiest); and arrivals typically peaked at about 10 per hour in the afternoons from 1 p.m. to 6 p.m., approximately. Because similar data are routinely collected by most hospitals and regression analysis software is widely available, the forecasting models described here can serve as an important tool to support a wide range of ED resource planning activities.

Evaluation of a seven state criminal history screening pilot program for long-term care workers.

This article summarizes results from an evaluation of a federally sponsored criminal history screening (CHS) pilot program to improve screening for workers in long-term care settings. The evaluation addressed eight key issues specified through enabling legislation, including efficiency, costs, and outcomes of screening procedures. Of the 204,339 completed screenings, 3.7% were disqualified due to criminal history, and 18.8% were withdrawn prior to completion for reasons that may include relevant criminal history. Lessons learned from the pilot program experiences may inform a new national background check demonstration program.

Scheduling Diagnostic Testing Kit Deliveries with the Mothership and Drone Routing Problem.

A critical component in the public health response to pandemics is the ability to determine the spread of diseases via diagnostic testing kits. Currently, diagnostic testing kits, treatments, and vaccines for the COVID-19 pandemic have been developed and are being distributed to communities worldwide, but the spread of the disease persists. In conjunction, a strong level of social distancing has been established as one of the most basic and reliable ways to mitigate disease spread. If home testing kits are safely and quickly delivered to a patient, this has the potential to significantly reduce human contact and reduce disease spread before, during, and after diagnosis. This paper proposes a diagnostic testing kit delivery scheduling approach using the Mothership and Drone Routing Problem (MDRP) with one truck and multiple drones. Due to the complexity of solving the MDRP, the problem is decomposed into 1) truck scheduling to carry the drones and 2) drone scheduling for actual delivery. The truck schedule (TS) is optimized first to minimize the total travel distance to cover patients. Then, the drone flight schedule is optimized to minimize the total delivery time. These two steps are repeated until it reaches a solution minimizing the total delivery time for all patients. Heuristic algorithms are developed to further improve the computational time of the proposed model. Experiments are made to show the benefits of the proposed approach compared to the commonly performed face-to-face diagnosis via the drive-through testing sites. The proposed solution method significantly reduced the computation time for solving the optimization model (less than 50 minutes) compared to the exact solution method that took more than 10 hours to reach a 20% optimality gap. A modified basic reproduction rate (i.e., m R 0) is used to compare the performance of the drone-based testing kit delivery method to the face-to-face diagnostic method in reducing disease spread. The results show that our proposed method (m R 0= 0.002) outperformed the face-to-face diagnostic method (m R 0= 0.0153) by reducing m R 0 by 7.5 times.

COVID-19: Short term prediction model using daily incidence data.

BACKGROUND: Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. METHODS: Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree. RESULTS: We apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time. CONCLUSION: We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.

Cost-Effectiveness of Three Doses of a Behavioral Intervention to Prevent or Delay Type 2 Diabetes in Rural Areas.

BACKGROUND: Rural Americans have higher prevalence of obesity and type 2 diabetes (T2D) than urban populations and more limited access to behavioral programs to promote healthy lifestyle habits. Descriptive evidence from the Rural Lifestyle Intervention Treatment Effectiveness trial delivered through local cooperative extension service offices in rural areas previously identified that behavioral modification with both nutrition education and coaching resulted in a lower program delivery cost per kilogram of weight loss maintained at 2-years compared with an education-only comparator intervention. OBJECTIVE: This analysis extended earlier Rural Lifestyle Intervention Treatment Effectiveness trial research regarding weight loss outcomes to assess whether nutrition education with behavioral coaching delivered through cooperative extension service offices is cost-effective relative to nutrition education only in reducing T2D cases in rural areas. DESIGN: A cost-utility analysis was conducted. PARTICIPANTS/SETTING: Trial participants (n=317) from June 2008 through June 2014 were adults residing in rural Florida counties with a baseline body mass index between 30 and 45, but otherwise identified as healthy. INTERVENTION: Trial participants were randomly assigned to low, moderate, or high doses of behavioral coaching with nutrition education (ie, 16, 32, or 48 sessions over 24 months) or a comparator intervention that included 16 sessions of nutrition education without coaching. Participant glycated hemoglobin level was measured at baseline and the end of the trial to assess T2D status. MAIN OUTCOME MEASURES: T2D categories by treatment arm were used to estimate participants' expected annual health care expenditures and expected health-related utility measured as quality adjusted life years (ie, QALYs) over a 5-year time horizon. Discounted incremental costs and QALYs were used to calculate incremental cost-effectiveness ratios for each behavioral coaching intervention dose relative to the education-only comparator. STATISTICAL ANALYSES PERFORMED: Using a third-party payer perspective, Markov transition matrices were used to model participant transitions between T2D states. Replications of the individual participant behavior were conducted using Monte Carlo simulation. RESULTS: All three doses of the behavioral coaching intervention had lower expected total costs and higher estimated QALYs than the education-only comparator. The moderate dose behavioral coaching intervention was associated with higher estimated QALYs but was costlier than the low dose; the moderate dose was favored over the low dose with willingness to pay thresholds over $107,895/QALY. The low dose behavioral coaching intervention was otherwise favored. CONCLUSIONS: Because most rural Americans live in counties with cooperative extension service offices, nutrition education with behavioral coaching programs similar to those delivered through this trial may be effective and efficient in preventing or delaying T2D-associated consequences of obesity for rural adults.

Forecasting the demand for radiology services.

Since the demand for health services is the key driver for virtually all of a health care organisation's financial and operational activities, it is imperative that health care managers invest the time and effort to develop appropriate and accessible forecasting models for their facility's services. In this article, we analyse and forecast the demand for radiology services at a large, tertiary hospital in Florida. We demonstrate that a comprehensive and accurate forecasting model can be constructed using well-known statistical techniques. We then use our model to illustrate how to provide decision support for radiology managers with respect to department staffing. The methodology we present is not limited to radiology services and we advocate for more routine and widespread use of demand forecasting throughout the health care delivery system.

Five constants of information technology adoption in healthcare.

The healthcare industry has developed a dependence on information technology (IT) for maintaining and improving both clinical and business operations. Whether IT is used for office automation or for reducing medical errors, there are five constants that routinely influence the successful integration of IT in healthcare. These constants are the proper use and maintenance of the IT budget, the role of supportive leadership, the use of project management, the process of implementation, and the significance of end user involvement. These constants challenge healthcare organizations to efficiently and effectively use their financial and human resources when adopting new IT. These constants also shape how the healthcare industry approaches the adoption and utilization of new IT. A collective understanding of these constants and their interrelationships will enable healthcare organizations to better integrate new IT and achieve organizational goals of developing a solid technological infrastructure to truly enhance the delivery of quality healthcare.

Transitioning to Patient-Centered Medical Homes: Associations With Appointment Availability.

INTRODUCTION: Recent implementation of the Patient-Centered Medical Home (PCMH) in military primary care has gained significant traction and attention from leadership and policy makers. The study objective was to measure the rate of change in appointment availability before and after primary care clinics were certified as a medical home. Access to care is one core tenet of the medical home and appointment availability is an important indicator of access. MATERIALS AND METHODS: This was a retrospective, longitudinal observational study involving 21 U.S. Navy primary care clinics from 2011 to 2014. Appointment availability, as measured by third next available appointment, was constructed for 21 primary care clinics over a 29-month time period (14 months precertification, certification month, 14 months postcertification). A mixed-effects model with linear splines was applied where third next available appointment was the dependent variable. Main interest independent variables include time (precertification and postcertification). Remaining independent variables include categories pertaining to clinic characteristics, ancillary services, and nonemergent primary care treatable emergency department visits. RESULTS: Appointment availability improved slightly postcertification. Although there were statistically significant differences in appointment availability pre- and postcertification, the differences were so small that patients may not actually experience noticeable improvements. CONCLUSION: Although slight improvements in appointment availability following medical home certification exist, adoption of the medical home model in the military setting may not have all the potential outcomes expected on the basis of prior findings in civilian settings. This study demonstrated that improvements in appointment availability following medical home certification exist, but are quite small. Patients, as a result, are unlikely to notice any improvements. Additional research should test other expected benefits of PCMH in military settings. At that point, military policy makers can decide which aspects of PCMH practices merit sustaining.

Implementation and dissemination of a transition of care program for rural veterans: a controlled before and after study.

BACKGROUND: Adapting promising health care interventions to local settings is a critical component in the dissemination and implementation process. The Veterans Health Administration (VHA) rural transitions nurse program (TNP) is a nurse-led, Veteran-centered intervention designed to improve transitional care for rural Veterans funded by VA national offices for dissemination to other VA sites serving a predominantly rural Veteran population. Here, we describe our novel approach to the implementation and evaluation = the TNP. METHODS: This is a controlled before and after study that assesses both implementation and intervention outcomes. During pre-implementation, we assessed site context using a mixed method approach with data from diverse sources including facility-level quantitative data, key informant and Veteran interviews, observations of the discharge process, and a group brainstorming activity. We used the Practical Robust Implementation and Sustainability Model (PRISM) to inform our inquiries, to integrate data from all sources, and to identify factors that may affect implementation. In the implementation phase, we will use internal and external facilitation, paired with audit and feedback, to encourage appropriate contextual adaptations. We will use a modified Stirman framework to document adaptations. During the evaluation phase, we will measure intervention and implementation outcomes at each site using the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, and Maintenance). We will conduct a difference-in-differences analysis with propensity-matched Veterans and VA facilities as a control. Our primary intervention outcome is 30-day readmission and Emergency Department visit rates. We will use our findings to develop an implementation toolkit that will inform the larger scale-up of the TNP across the VA. DISCUSSION: The use of PRISM to inform pre-implementation evaluation and synthesize data from multiple sources, coupled with internal and external facilitation, is a novel approach to engaging sites in adapting interventions while promoting fidelity to the intervention. Our application of PRISM to pre-implementation and midline evaluation, as well as documentation of adaptations, provides an opportunity to identify and address contextual factors that may impede or enhance implementation and sustainability of health interventions and inform dissemination.

The identification of high-cost patients.

The authors examine whether retrospective claims data are useful to distinguish future high-cost cases among the uninsured. They rely on internal claims and accounting data for the calendar years from 1999 to 2001 from a representative safety net facility to describe the distribution of costs and any characteristics that distinguish high-cost patients from other uninsured patients. They conclude that administrative data combined with in-depth survey information could be a useful approach for identifying cases for intensive case management.

Computer skills for the next generation of healthcare executives.

Students beginning a career in healthcare administration must possess an array of professional and management skills in addition to a strong fundamental understanding of the field of healthcare administration. Proficient computer skills are a prime example of an essential management tool for healthcare administrators. However, it is unclear which computer skills are absolutely necessary for healthcare administrators and the extent of congruency between the computer skills possessed by new graduates and the needs of senior healthcare professionals. Our objectives in this research are to assess which computer skills are the most important to senior healthcare executives and recent healthcare administration graduates and examine the level of agreement between the two groups. Based on a survey of senior healthcare executives and graduate healthcare administration students, we identify a comprehensive and pragmatic array of computer skills and categorize them into four groups, according to their importance, for making recent health administration graduates valuable in the healthcare administration workplace. Traditional parametric hypothesis tests are used to assess congruency between responses of senior executives and of recent healthcare administration graduates. For each skill, responses of the two groups are averaged to create an overall ranking of the computer skills. Not surprisingly, both groups agreed on the importance of computer skills for recent healthcare administration graduates. In particular, computer skills such as word processing, graphics and presentation, using operating systems, creating and editing databases, spreadsheet analysis, using imported data, e-mail, using electronic bulletin boards, and downloading information were among the highest ranked computer skills necessary for recent graduates. However, there were statistically significant differences in perceptions between senior executives and healthcare administration students as to the extent of computer skills required in areas such as word processing, graphics and presentation, spreadsheet analysis, using imported data, and working with local area networks (LANs).

Understanding the impact of variation in the delivery of healthcare services.

In the past several years, healthcare providers have coped with the financial aspects of managed care and the resultant constraints on revenue. In fact, working with decreasing margins of return has become routine for many providers. Beyond straightforward cost cutting, providers must also consider a variety of other operational factors to achieve success. To this end, higher patient satisfaction and improved utilization and efficiency of resources are natural objectives. Ironically, fundamental to the pursuit of better operations management is the fact that the delivery of healthcare services can vary between and among patients, providers, and organizations for many reasons. Unfortunately, such variation may be overlooked or trivialized if the phenomenon is not well understood by healthcare managers. Knowing how variation affects the delivery of services creates opportunities for focused improvement.

Rehabilitation settings after joint replacement: an application of multiattribute preference elicitation.

While advances in medical treatment and technologies have the potential to improve the delivery of health care, their use typically involves making multiple, complex decisions. Patients and their medical providers may share in the decision-making processes and balance a variety of criteria and/or attributes in the pursuit of improved health. This necessitates a stronger understanding of the role of human behavior in health care processes and presents a timely opportunity to use decision analysis tools to contribute to this important aspect of health care operations. This article reports on the application of multiattribute preference elicitation to identify postsurgical rehabilitation setting options for elective hip and knee replacement patients and their discharge planning team prior to placement in these settings. These preferences are analyzed to identify trends in emphases across patients and the discharge planning team, including a comparison with actual outcomes to determine the extent of congruence with each other, an important component of patient-centered care. Variances are identified in what patients and the discharge planning team expected and what actually happened. Reasons for these variances are discussed.

Costs associated with data collection and reporting for diabetes quality improvement in primary care practices: a report from SNOCAP-USA.

PURPOSE: Information about the costs and experiences of collecting and reporting quality measure data are vital for practices deciding whether to adopt new quality improvement initiatives or monitor existing initiatives. METHODS: Six primary care practices from Colorado's Improving Performance in Practice program participated. We conducted structured key informant interviews with Improving Performance in Practice coaches and practice managers, clinicians, and staff and directly observed practices. RESULTS: Practices had 3 to 7 clinicians and 75 to 300 patients with diabetes, half had electronic health records, and half were members of an independent practice association. The estimated per-practice cost of implementation for the data collection and reporting for the diabetes quality improvement program was approximately $15,552 per practice (about $6.23 per diabetic patient per month). The first-year maintenance cost for this effort was approximately $9,553 per practice ($3.83 per diabetic patient per month). CONCLUSIONS: The cost of implementing and maintaining a diabetes quality improvement effort that incorporates formal data collection, data management, and reporting is significant and quantifiable. Policymakers must become aware of the financial and cultural impact on primary care practices when considering value-based purchasing initiatives.

A mixed integer programming model to locate traumatic brain injury treatment units in the Department of Veterans Affairs: a case study.

For the Department of Veterans Affairs (VA), traumatic brain injury (TBI) is a significant problem facing active duty military personnel, veterans, their families, and caregivers. The VA has designated TBI treatment as one of its physical medicine and rehabilitation special emphasis programs, thereby providing a comprehensive array of treatment services to those military personnel and veterans with TBI. Timely treatment of TBI is critical in achieving maximal recovery, and being in geographical proximity to a medical center with specialized TBI treatment services is a major determinant of whether such treatment is utilized. We present a mixed integer programming model for locating TBI treatment units in the VA. This model was developed for the VA Rehabilitation Strategic Healthcare Group to assist in locating new TBI treatment units. The optimization model assigns TBI treatment units to existing VA medical centers while minimizing the sum of patient treatment costs, patient lodging and travel costs, and the penalty costs associated with foregone treatment revenue and excess capacity utilization. We demonstrate our model with VA TBI admission data from one of the VA's integrated service networks, and discuss the expected service and cost implications for a range of TBI treatment unit location options.

A network flow approach to optimizing hospital bed capacity decisions.

The delivery of cost-effective and quality hospital-based health care remains an important and ongoing challenge for the American health care industry. Despite numerous advances in medical procedures and technologies, a growing array of outpatient health care options, limits on inpatient reimbursements, and almost two decades of hospital contraction and consolidation, annual inpatient admissions in the United States are currently at levels not seen since the early 1980s. This combination of increased demand and diminished resources makes planning for hospital bed capacity a difficult problem for health care decision makers. We examine this problem by developing a network flow model that incorporates facility performance and budget constraints to determine optimal hospital bed capacity over a finite planning horizon. Under modest assumptions, we demonstrate that for realistic sized capacity planning problems, our network formulation is not computationally intensive, and allows us to obtain optimal bed capacity plans quickly.