Outpatient visits before and after Lyme disease diagnosis in a Maryland employer-based health plan.

BACKGROUND: Insurance claims data have been used to inform an understanding of Lyme disease epidemiology and cost of care, however few such studies have incorporated post-treatment symptoms following diagnosis. Using longitudinal data from a private, employer-based health plan in an endemic US state, we compared outpatient care utilization pre- and post-Lyme disease diagnosis. We hypothesized that utilization would be higher in the post-diagnosis period, and that temporal trends would differ by age and gender. METHODS: Members with Lyme disease were required to have both a corresponding ICD-9 code and a fill of an antibiotic indicated for treatment of the infection within 30 days of diagnosis. A 2-year 'pre- diagnosis' period and a 2-year 'post-diagnosis period' were centered around the diagnosis month. Lyme disease-relevant outpatient care visits were defined as specific primary care, specialty care, or urgent care visits. Descriptive statistics examined visits during these pre- and post-diagnosis periods, and the association between these periods and the number of visits was explored using generalized linear mixed effects models adjusting for age, season of the year, and gender. RESULTS: The rate of outpatient visits increased 26% from the pre to the post-Lyme disease diagnosis periods among our 317-member sample (rate ratio = 1.26 [1.18, 1.36], p < 0.001). Descriptively, care utilization increases appeared to persist across months in the post-diagnosis period. Women's care utilization increased by 36% (1.36 [1.24, 1.50], p < 0.001), a significantly higher increase than the 14% increase found among men (1.14 [1.02, 1.27], p = 0.017). This gender difference was mainly driven by adult members. We found a borderline significant 17% increase in visits for children < 18 years, (1.17 [0.99, 1.38], p = 0.068), and a 31% increase for adults ≥ 18 years (1.31 [1.21, 1.42], p < 0.001). CONCLUSIONS: Although modest at the population level, the statistically significant increases in post-Lyme diagnosis outpatient care we observed were persistent and unevenly distributed across demographic and place of service categories. As Lyme disease cases continue to grow, so will the cumulative prevalence of persistent symptoms after treatment. Therefore, it will be important to confirm these findings and understand their significance for care utilization and cost, particularly against the backdrop of other post-acute infectious syndromes.

Distributed Caregiving for Cognitively Impaired Individuals: A Case Report.

Many caregivers of people with cognitive impairment spend a significant amount of their time helping patients with instrumental daily functions. Distributed caregiving is an innovative model designed to reduce an individual caregiver's time burden and increase the likelihood of continued independent living for the patient. Echo Show and Google Home platforms were used to enable the participation of remote family members in caregiving, specifically the socialization and entertainment of a person with cognitive impairment. Caregiver interviews, review of medical records, and case study analysis were used to measure caregiver burden, after distributing some components of caregiving to distant family members with human-in-the-loop artificial intelligence. This case explores the use of Alexa, Echo Show, and other commercial technologies in the management of a patient with cognitive impairment. The human-in-the-loop system introduced in this case study is a creative, accessible, low-cost, and sustainable way to potentially reduce caregiver burden and improve patient outcomes with targeted intervention. Targeted distributed caregiving reduced time spent in caregiving, reduced caregiver guilt and frustration, improved patient's compliance with requests for behavior changes (e.g., voiding before leaving the house), and improved the relationship between the caregiver and the person with cognitive impairment. This case study demonstrates how distributed caregiving, including human-in-the-loop artificial intelligence, can lead to better use of technology in reducing the social isolation of persons with cognitive impairment and in reducing caregiver burden.

Incidence of Lyme Disease Diagnosis in a Maryland Medicaid Population, 2004-2011.

The epidemiology of Lyme disease has been examined utilizing insurance claims from privately insured individuals; however, it is unknown whether reported patterns vary among the publicly insured. We examined trends in incidence rates of first Lyme disease diagnosis among 384,652 Maryland Medicaid recipients enrolled from July 2004 to June 2011. Age-, sex-, county-, season-, and year-specific incidence rates were calculated, and mixed-effects multiple logistic regression models were used to study the relationship between Lyme disease diagnosis and these variables. The incidence rate in our sample was 97.65 cases per 100,000 person-years (95% confidence interval (CI): 91.53, 104.06), and there was a 13% average annual increase in the odds of a Lyme disease diagnosis (odds ratio = 1.13, 95% CI: 1.09, 1.17; P < 0.001). Incidence rates for males and females were not significantly different, though males were significantly more likely to be diagnosed during high-season months (relative risk (RR) = 1.24, 95% CI: 1.06, 1.44) and less likely to be diagnosed during low-season months (RR = 0.63, 95% CI: 0.46, 0.87) than females. Additionally, adults were significantly more likely than children to be diagnosed during low-season months (RR = 1.59, 95% CI: 1.19, 2.12). While relatively rare in this study sample, Lyme disease diagnoses do occur in a Medicaid population in a Lyme-endemic state.

Development and applications of an outcomes assessment framework for care management programs in learning health systems.

PURPOSE: To develop and apply an outcomes assessment framework (OAF) for care management programs in health care delivery settings. BACKGROUND: Care management (CM) refers to a regimen of organized activities that are designed to promote health in a population with particular chronic conditions or risk profiles, with focus on the triple aim for populations: improving the quality of care, advancing health outcomes, and lowering health care costs. CM has become an integral part of a care continuum for population-based health care management. To sustain a CM program, it is essential to assure and improve CM effectiveness through rigorous outcomes assessment. To this end, we constructed the OAF as the foundation of a systematic approach to CM outcomes assessment. INNOVATIONS: To construct the OAF, we first systematically analyzed the operation process of a CM program; then, based on the operation analysis, we identified causal relationships between interventions and outcomes at various implementation stages of the program. This set of causal relationships established a roadmap for the rest of the outcomes assessment. Built upon knowledge from multiple disciplines, we (1) formalized a systematic approach to CM outcomes assessment, and (2) integrated proven analytics methodologies and industrial best practices into operation-oriented CM outcomes assessment. CONCLUSION: This systematic approach to OAF for assessing the outcomes of CM programs offers an opportunity to advance evidence-based care management. In addition, formalized CM outcomes assessment methodologies will enable us to compare CM effectiveness across health delivery settings.

Using sentiment analysis to review patient satisfaction data located on the internet.

PURPOSE: The purpose of this paper is to test the usefulness of sentiment analysis and time-to-next-complaint methods in quantifying text-based information located on the internet. As important, the authors demonstrate how managers can use time-to-next-complaint techniques to organize sentiment analysis derived data into useful information, which can be shared with doctors and other staff. DESIGN/METHODOLOGY/APPROACH: The authors used sentiment analysis to review patient feedback for a select group of gynecologists in Virginia. The authors utilized time-to-next-complaint methods along with other techniques to organize this data into meaningful information. FINDINGS: The authors demonstrated that sentiment analysis and time-to-next-complaint techniques might be useful tools for healthcare managers who are interested in transforming web-based text into meaningful, quantifiable information. RESEARCH LIMITATIONS/IMPLICATIONS: This study has several limitations. For one thing, neither the data set nor the techniques the authors used to analyze it will account for biases that resulted from selection issues related to gender, income, and culture, as well as from other socio-demographic concerns. Additionally, the authors lacked key data concerning patient volumes for the targeted physicians. Finally, it may be difficult to convince doctors to consider web-based comments as truthful, thereby preventing healthcare managers from using data located on the internet. PRACTICAL IMPLICATIONS: The report illustrates some of the ways in which healthcare administrators can utilize sentiment analysis, along with time-to-next-complaint techniques, to mine web-based, patient comments for meaningful information. ORIGINALITY/VALUE: The paper is one of the first to illustrate ways in which administrators at clinics and physicians' offices can utilize sentiment analysis and time-to-next-complaint methods to analyze web-based patient comments.