Transforming Neurology Care Delivery Through a Population Health Data Strategy.

Background: With more than 30% of global data originating from health care, deriving usable insights that improve health requires population health analytics. In neurology, data-driven approaches have grown in significance because of digital health records and advanced analytics. A vital aspect of this evolution is adopting a population health data strategy (PHDS). Recent Findings: Crafting a tailored PHDS for neurology involves cataloging data points and measures spanning demographics, clinical history, genetics, and social determinants. Neurologic outcomes include mortality rates, functional and cognitive abilities, and imaging results. A robust strategy relies on interoperability, advanced analytics, and transparent AI algorithms. Summary: Neurology is embracing data-driven health care. The PHDS synthesizes diverse patient data to provide personalized care. It includes a wide range of outcome measures to address neurologic complexities. Advanced analytics and collaboration among neurologists, data scientists, and business leaders uncover hidden patterns and promote outcome-driven medicine in the 21st century.

Risk prediction and segmentation models used in the United States for assessing risk in whole populations: a critical literature review with implications for nurses' role in population health management.

OBJECTIVE: We sought to assess the current state of risk prediction and segmentation models (RPSM) that focus on whole populations. MATERIALS: Academic literature databases (ie MEDLINE, Embase, Cochrane Library, PROSPERO, and CINAHL), environmental scan, and Google search engine. METHODS: We conducted a critical review of the literature focused on RPSMs predicting hospitalizations, emergency department visits, or health care costs. RESULTS: We identified 35 distinct RPSMs among 37 different journal articles (n = 31), websites (n = 4), and abstracts (n = 2). Most RPSMs (57%) defined their population as health plan enrollees while fewer RPSMs (26%) included an age-defined population (26%) and/or geographic boundary (26%). Most RPSMs (51%) focused on predicting hospital admissions, followed by costs (43%) and emergency department visits (31%), with some models predicting more than one outcome. The most common predictors were age, gender, and diagnostic codes included in 82%, 77%, and 69% of models, respectively. DISCUSSION: Our critical review of existing RPSMs has identified a lack of comprehensive models that integrate data from multiple sources for application to whole populations. Highly depending on diagnostic codes to define high-risk populations overlooks the functional, social, and behavioral factors that are of great significance to health. CONCLUSION: More emphasis on including nonbilling data and providing holistic perspectives of individuals is needed in RPSMs. Nursing-generated data could be beneficial in addressing this gap, as they are structured, frequently generated, and tend to focus on key health status elements like functional status and social/behavioral determinants of health.

Going Beyond Clinical Care to Reduce Health Care Spending: Findings From the J-CHiP Community-based Population Health Management Program Evaluation.

BACKGROUND: Addressing both clinical and nonclinical determinants of health is essential for improving population health outcomes. In 2012, the Johns Hopkins Community Health Partnership (J-CHiP) implemented innovative population health management programs across acute and community environments. The community-based program involved multidisciplinary teams [ie, physicians, care managers (CM), health behavior specialists (HBS), community health workers, neighborhood navigators] and collaboration with community-based organizations to address social determinants. OBJECTIVES: To report the impact of a community-based program on cost and utilization from 2011 to 2016. DESIGN: Difference-in-difference estimates were calculated for an inclusive cohort of J-CHiP participants and matched nonparticipants. The analysis was replicated for participants with a CM and/or HBS to estimate the differential impact with more intensive program services. SUBJECTS: A total of 3268 high-risk Medicaid and Medicare beneficiaries (1634 total J-CHiP participants, 1365 with CM and 678 with HBS). OUTCOME MEASURES: Paid costs and counts of emergency department visits, admissions, and readmissions per member per year. RESULTS: For Medicaid, costs were almost $1200 per member per year lower for participants as a whole, $2000 lower for those with an HBS, and $3000 lower for those with a CM; hospital admission and readmission rates were 9%-26% lower for those with a CM and/or HBS. For Medicare, costs were lower (-$476), but utilization was similar or higher than nonparticipants. None of the observed Medicaid or Medicare differences were statistically significant. CONCLUSIONS: Although not statistically significant, the results indicate a promising innovation for Medicaid beneficiaries. For Medicare, the impact was negligible, indicating the need for further program modification.

Key Design Considerations When Calculating Cost Savings for Population Health Management Programs in an Observational Setting.

OBJECTIVE: To illustrate the impact of key quasi-experimental design elements on cost savings measurement for population health management (PHM) programs. DATA SOURCES: Population health management program records and Medicaid claims and enrollment data from December 2011 through March 2016. STUDY DESIGN: The study uses a difference-in-difference design to compare changes in cost and utilization outcomes between program participants and propensity score-matched nonparticipants. Comparisons of measured savings are made based on (1) stable versus dynamic population enrollment and (2) all eligible versus enrolled-only participant definitions. Options for the operationalization of time are also discussed. DATA COLLECTION/EXTRACTION METHODS: Individual-level Medicaid administrative and claims data and PHM program records are used to match study groups on baseline risk factors and assess changes in costs and utilization. PRINCIPAL FINDINGS: Savings estimates are statistically similar but smaller in magnitude when eliminating variability based on duration of population enrollment and when evaluating program impact on the entire target population. Measurement in calendar time, when possible, simplifies interpretability. CONCLUSION: Program evaluation design elements, including population stability and participant definitions, can influence the estimated magnitude of program savings for the payer and should be considered carefully. Time specifications can also affect interpretability and usefulness.

Population health: a nursing action plan.

The passage of the Affordable Care Act shifted the focus of health care from individual, patient specific, episodic care, towards health management of groups of people with an emphasis on primary and preventive care. Population health management assists to attain and maintain health while improving quality and lowering costs. The recent Catalyst for Change report creates an urgent call for harnessing the power of nurses-in our communities, schools, businesses, homes and hospitals-to build capacity for population health. Informatics Nurse Specialists are prepared to bridge roles across practice, research, education, and policy to support this call. Each year, the AMIA Nursing Informatics Working Group convenes an expert panel to reflect on the "hot topics" of interest to nursing. Not surprisingly, the 2017 topic was on the current state and challenges of population health. The following summary reflects the panel's perspectives and recommendations for action.

Triplex real-time PCR for detection of Crithidia mellificae and Lotmaria passim in honey bees.

Currently, light microscopic examination of cell morphology cannot discriminate Crithidia mellificae and Lotmaria passim with 100% certainty. Here, a minor groove-binding (MGB) probe-based multiplex real-time PCR assay was developed for the simultaneous and quantitative detection of C. mellificae and L. passim in honey bees. A conserved Hymenoptera 18S rRNA gene was built in as an internal control that allows accurate detection of PCR inhibition and failure of DNA extraction. The newly developed assay was also applied to field samples. Of 21 honey bee colonies (446 bees) sampled from six counties in both central and eastern Massachusetts, 3 colonies (14.29%) and 8 bees (1.79%) were infected with L. passim, and 1 colony (4.76%) and 1 bee (0.22%) with C. mellificae. Our data showed a low rate of trypanosomatid infection, and L. passim was more prevalent than C. mellificae in honey bee samples in Massachusetts.

Pain, Agitation, and Delirium Guidelines: Interprofessional Perspectives to Translate the Evidence.

BACKGROUND: Societal guidelines exist for the management of pain, agitation, and delirium (PAD) in critically ill patients. This contemporary practice aims for a more awake and interactive patient. Institutions are challenged to translate the interrelated multivariable concepts of PAD into daily clinical practice and to demonstrate improvement in quality outcomes. An interdisciplinary goal-directed approach shows outcomes in high-acuity surgical critical care during the early stages of implementation. METHODS: This study was a prospective preintervention and postintervention design. A formal PAD clinical practice guideline targeting standardized assessment and "light" levels of sedation was instituted. All mechanically ventilated patients admitted to a 24-bed surgical intensive care unit (ICU) at an academic medical center during a 6-month period were included (3 months before and 3 months after implementation). Sedation and agitation were measured using the Richmond Agitation Sedation Scale (RASS), pain measured using a Behavioral or Numeric Pain Scale (NPS/BPS), and delirium using the Confusion Assessment Method for the Intensive Care Unit. Total ventilator days with exposure to continuous opioid or sedative infusions and total ICU days where the patient received a physical activity session exercising out of bed were recorded. RESULTS: There were 106 patients (54 at preintervention and 52 at postintervention). Mean percentage of RASS scores between 0 to -1 increased from 38% to 50% postintervention (P < .02). Mean percentage of NPS/BPS scores within the goal range (<5 for BPS and <3 for NPS) remained stable, 86% to 83% (P = .16). There was a decrease in use of continuous narcotic infusions for mechanically ventilated patients. This was reported as mean percentage of total ventilator days with a continuous opioid infusing: 65% before implementation versus 47% after implementation (P < .01). Mean percentage of ICU days with physical activity sessions increased from 24% to 41% (P < .001). Overall mean ventilator-free days and ICU length of stay were 5.4 to 4.5 days (P = .29) and 11.75 to 9.5 days (P = .20), respectively. CONCLUSION: Measureable patient outcomes are achievable in the early stages of PAD guideline initiatives and can inform future systems-level organizational change. Pain, agitation, and delirium assessment tools form the foundation for clinical implementation and evaluation. High-acuity surgical critical care patients can achieve more time at goal RASS, decreased ventilator days, and less exposure to continuous opioid infusions, all while maintaining stable analgesia.

Big data science: A literature review of nursing research exemplars.

BACKGROUND: Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge. PURPOSE: The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals. METHODS: A literature review of studies published between 2009 and 2015. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice. DISCUSSION: Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods. CONCLUSION: There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science.

The Doctor of Nursing Practice: From Start-Up to Impact.

AIM: This article reflects on the progress of the doctor of nursing practice (DNP) degree and its place in health care. BACKGROUND: The DNP originated over 10 years ago, long enough for a comprehensive evaluation. METHOD: Rogers' Diffusion of Innovation Theory is used to trace the history of the DNP. Nurse leaders from service and academia (n = 120) share strategies and innovations, and evaluate DNP education with a focus on outcomes and impact. RESULTS: As schools of nursing target DNPs to become faculty to mitigate the shortage, participants agreed it is time to focus on graduating strong leaders prepared to transform health care. CONCLUSION: A growing number of nurses practicing in diverse roles have earned the DNP from programs that vary considerably in rigor. Demand for the competencies, skills, and experience which DNPs bring to practice is high as organizations adapt to the accountable care environment.

Scholarly work products of the doctor of nursing practice: one approach to evaluating scholarship, rigour, impact and quality.

AIMS AND OBJECTIVES: The aim of this investigation was to evaluate, monitor and manage the quality of projects conducted and work produced as evidence of scholarship upon completion of Doctor of Nursing Practice education. BACKGROUND: The Doctor of Nursing Practice is a relatively new degree which prepares nurses for high impact careers in diverse practice settings around the globe. Considerable variation characterises curricula across schools preparing Doctors of Nursing Practice. Accreditation assures curricula are focused on attainment of the Doctor of Nursing Practice essentials, yet outcomes have not been reported to help educators engage in programme improvement. This work has implications for nursing globally because translating strong evidence into practice is key to improving outcomes in direct care, leadership, management and education. The Doctor of Nursing Practice student learns to accomplish translation through the conduct of projects. Evaluating the rigour and results of these projects is essential to improving the quality, safety and efficacy of translation, improvements in care and overall system performance. DESIGN: A descriptive study was conducted to evaluate the scholarly products of Doctor of Nursing Practice education in one programme across four graduating classes. METHODS: A total of 80 projects, conducted across the USA and around the globe, are described using a modification of the Uncertainty, Pace, Complexity Model. RESULTS: The per cent of students considered to have produced high quality work in relation to target expectations as well as the per cent that conducted means testing increased over the four study years. CONCLUSIONS: Evaluation of scope, complexity and rigour of scholarly work products has driven improvements in the curriculum and informed the work of faculty and advisors. RELEVANCE TO CLINICAL PRACTICE: Methods, evaluation and outcomes conformed around a set of expectations for scholarship and rigour have resulted in measurable outcomes, and quality publications have increased over time.

Emergency Room Utilization After Medically Complicated Pregnancies: A Medicaid Claims Analysis.

BACKGROUND: Women with pregnancy complications benefit from closer monitoring postpartum and beyond. Increased postpartum emergency room (ER) use may indicate unmet need for outpatient obstetrics and primary care. The purpose of this study was to evaluate whether women with pregnancy complications (gestational diabetes [GDM], gestational hypertension, and preeclampsia) have increased ER use in the first 6 months postpartum, compared with women without these complications. METHODS: We conducted a retrospective population-based cohort study using a 2003-2010 Maryland Medicaid managed care claims data set, linked with U.S. Census data. Data included claims for outpatient and ER visits for women aged 12-45 years who were continuously enrolled in Medicaid for at least 100 days of pregnancy and 90 days postpartum. We used logistic regression to calculate the association between pregnancy complications and having ≥1 ER visit in the 6 months postpartum. RESULTS: We identified 26,074 pregnancies, of which 20% were complicated by GDM, gestational hypertension, or preeclampsia. Of these complicated pregnancies, 42.1% had GDM, 35.4% had gestational hypertension, and 42.5% had preeclampsia (diagnoses were not mutually exclusive). In the 6 months postpartum, 25% of women had ≥1 ER visits. Of the complicated pregnancy group, 27.7% had ≥1 ER visit, versus 23.6% of the comparison group (p<0.0001). In adjusted analyses, women with a pregnancy complication were more likely to have ≥1 ER visit compared with women without these complications (odds ratio [OR]1.14, 95% confidence interval [CI] 1.05-1.23). The strength of association was highest in women under age 25 (OR 1.20, 95% CI 1.09-1.33). Preconception medical comorbidities (type 2 diabetes, chronic hypertension, obesity, asthma, mental health, and substance abuse diagnoses) were also strongly associated with postpartum ER use (OR 1.61, 95% CI 1.51-1.73). CONCLUSIONS: Pregnancy complications increased ER utilization during the 6 months postpartum, especially among women under age 25 years. Interventions that improve discharge planning and early postpartum care may decrease ER use.

Coverage isn't enough: building primary care capacity in the setting of health reform.

The Access Partnership is a program linking uninsured patients with primary and specialty care. Expansion of primary care access resulted in an influx of patients with multiple chronic conditions, causing the primary care practice to reach capacity after seven months. Our program may provide lessons in ensuring primary care access as the Affordable Care Act is implemented.

An approach to clinical data management for the doctor of nursing practice curriculum.

Strong data management skills are essential to doctor of nursing practice (DNP) education and necessary for DNP practice. Completion of the DNP scholarly project requires application of these skills to understand and address a complex practice, process, or systems problem; develop, implement, and monitor an innovative evidence-based intervention to address that problem; and evaluate the outcomes. The purposes of this paper were to describe the demand and context for clinical data management (CDM) within the DNP curriculum; provide an overview of CDM content; describe the process for content delivery; propose a set of course objectives; and describe initial successes and challenges. A two-pronged approach of consultation and a CDM course were developed. Students who participated in this approach were more likely to create and implement an evaluation plan; apply techniques for data cleansing and manipulation; apply concepts of sample size determination using power analysis; use exploratory data analysis techniques to understand population attributes and sampling bias; apply techniques to adjust for bias; apply statistical significance testing; and present project results in a meaningful way. On the basis of this evaluation, CDM has evolved from an elective to a required course integrated in a thread that crosses the entire curriculum.

A method for estimating cost savings for population health management programs.

OBJECTIVE: To develop a quasi-experimental method for estimating Population Health Management (PHM) program savings that mitigates common sources of confounding, supports regular updates for continued program monitoring, and estimates model precision. DATA SOURCES: Administrative, program, and claims records from January 2005 through June 2009. DATA COLLECTION/EXTRACTION METHODS: Data are aggregated by member and month. STUDY DESIGN: Study participants include chronically ill adult commercial health plan members. The intervention group consists of members currently enrolled in PHM, stratified by intensity level. Comparison groups include (1) members never enrolled, and (2) PHM participants not currently enrolled. Mixed model smoothing is employed to regress monthly medical costs on time (in months), a history of PHM enrollment, and monthly program enrollment by intensity level. Comparison group trends are used to estimate expected costs for intervention members. Savings are realized when PHM participants' costs are lower than expected. PRINCIPAL FINDINGS: This method mitigates many of the limitations faced using traditional pre-post models for estimating PHM savings in an observational setting, supports replication for ongoing monitoring, and performs basic statistical inference. CONCLUSION: This method provides payers with a confident basis for making investment decisions.

Work limitations and their relationship to morbidity burden among academic health center employees with diabetes.

The objective of this study was to determine the prevalence of work limitations and their relationship to morbidity burden among academic health center employees with diabetes. Employees with diabetes were surveyed via Internet and mail using the Work Limitations Questionnaire. Morbidity burden was measured using the Adjusted Clinical Groups methodology. Seventy-two percent of the employees with diabetes had a work limitation. Adjusted odds ratios for overall, physical, time, and output limitations were 1.81, 2.27, 2.13, and 2.14, respectively. Morbidity burden level is an indicator of work limitations in employees with diabetes and can be used to identify employees who may benefit from specialized services aimed at addressing their work limitations associated with diabetes.

Predictive modeling in practice: improving the participant identification process for care management programs using condition-specific cut points.

The objective of this study was to optimize predictive modeling in the participant selection process for care management (CM) programs by determining the ideal cut point selection method. Comparisons included: (a) an evidence-based "optimal" cut point versus an "arbitrary" threshold, and (b) condition-specific cut points versus a uniform screening method. Participants comprised adult Medicaid health plan members enrolled during the entire study period (January 2007-December 2008) who had at least 1 of the chronic conditions targeted by the CM programs (n = 6459). Adjusted Clinical Groups Predictive Modeling (ACG-PM) system risk scores in 2007 were used to predict those with the top 5% highest health care expenditures in 2008. Comparisons of model performance (ie, c statistic, sensitivity, specificity, positive predictive value) and identified population size were used to assess differences among 3 cut point selection approaches: (a) single arbitrary cut point, (b) single optimal cut point, and (c) condition-specific optimal cut points. The "optimal" cut points (ie, single and condition-specific) both outperformed the "arbitrary" selection process, yielding higher probabilities of correct prediction and sensitivities. The condition-specific optimal cut point approach also exhibited better performance than applying a single optimal cut point uniformly across the entire population regardless of condition (ie, a higher c statistic, specificity, and positive predictive value, although sensitivity was lower), while identifying a more manageable number of members for CM program outreach. CM programs can optimize targeting algorithms by utilizing evidence-based cut points that incorporate condition-specific variations in risk. By efficiently targeting and intervening with future high-cost members, health care costs can be reduced.

Guided care: cost and utilization outcomes in a pilot study.

Guided Care (GC) is an enhancement to primary care that incorporates the operative principles of disease management and chronic care innovations. In a 6-month quasi-experimental study, we compared the cost and utilization patterns of patients assigned to GC and Usual Care (UC). The setting was a community-based general internal medicine practice. The participants were patients of 4 general internists. They were older, chronically ill, community-dwelling patients, members of a capitated health plan, and identified as high risk. Using the Adjusted Clinical Groups Predictive Model (ACG-PM), we identified those at highest risk of future health care utilization. We selected the 75 highest-risk older patients of 2 internists at a primary care practice to receive GC and the 75 highest-risk older patients of 2 other internists in the same practice to receive UC. Insurance data were used to describe the groups' demographics, chronic conditions, insurance expenditures, and utilization. Among our results, at baseline, the GC (all targeted patients) and UC groups were similar in demographics and prevalence of chronic conditions, but the GC group had a higher mean ACG-PM risk score (0.34 vs. 0.20, p < 0.0001). During the following 6 months, the GC group had lower unadjusted mean insurance expenditures, hospital admissions, hospital days, and emergency department visits (p > 0.05). There were larger differences in insurance expenditures between the GC and UC groups at lower risk levels (at ACG-PM = 0.10, mean difference = $4340; at ACG-PM = 0.6, mean difference = $1304). Thirty-one of the 75 patients assigned to receive GC actually enrolled in the intervention. These results suggest that GC may reduce insurance expenditures for high-risk older adults. If these results are confirmed in larger, randomized studies, GC may help to increase the efficiency of health care for the aging American population.

A pilot test of the effect of guided care on the quality of primary care experiences for multimorbid older adults.

OBJECTIVE: Improving health care of multimorbid older adults is a critical public health challenge. The objective of this study is to evaluate the effect of a pilot intervention to enhance the quality of primary care experiences for chronically ill older persons (Guided Care). DESIGN: Nonrandomized prospective clinical trial. PATIENTS/PARTICIPANTS: Older, chronically ill, community-dwelling patients (N = 150) of 4 General Internists in 1 urban community practice setting who were members of a capitated health plan and identified as being at high risk of heavy use of health services in the coming year by claims-based predictive modeling. INTERVENTIONS: Guided Care, an enhancement to primary care that incorporates the operative principles of chronic care innovations, was delivered by a specially trained, practice-based registered nurse working closely with 2 primary care physicians. Each patient received a geriatric assessment, a comprehensive care plan, evidence-based primary care with proactive follow-up of chronic conditions, coordination of the efforts of health professionals across all health care settings, and facilitated access to community resources. MEASUREMENTS AND MAIN RESULTS: Quality of primary care experiences (physician-patient communication, interpersonal treatment, knowledge of patient, integration of care, and trust in physician) was assessed using the Primary Care Assessment Survey (PCAS) at baseline and 6 months later. At baseline, the patients assigned to receive Guided Care were similar to those assigned to receive usual care in their demographics and disability levels, but they had higher risk scores and were less likely to be married. Thirty-one of the 75 subjects assigned to the Guided Care group received the intervention. At 6 months, intention-to-treat analyses adjusting for age, gender, and risk score suggest that Guided Care may improve the quality of physician-patient communication. In per-protocol analyses, receipt of Guided Care was associated with more favorable change than usual care from baseline to follow-up in all 5 PCAS domains, but only physician-patient communication showed a statistically significant improvement. CONCLUSIONS: In this pilot study, Guided Care appeared to improve the quality of primary care experiences for high-risk, chronically ill older adults. A larger cluster-randomized controlled trial of Guided Care is underway.

Sex pheromone of the cranberry root grub Lichnanthe vulpina.

The cranberry root grub Lichnanthe vulpina (Hentz) (Coleoptera: Glaphyridae) is a pest of cranberries in Massachusetts, reducing yield and vine density. (Z)-7-Hexadecenol and (Z)-7-hexadecenal were identified from the female effluvia collection by gas chromatographic-electroantennographic detection and gas chromatography-mass spectrometry. The double-bond position was confirmed by dimethyl disulfide derivatization. Both compounds were tested in the field, each alone and as blends of the two. Each compound alone captured males; however, (Z)-7-hexadecenol alone captured significantly more males than did (Z)-7-hexadecenal alone. The addition of varying amounts of (Z)-7-hexadecenal to (Z)-7-hexadecenol did not statistically affect male capture. Flight activity of the cranberry root grub may be monitored with traps baited with rubber septa containing 300 microg of (Z)-7-hexadecenol. A test of trap vane colors indicated that traps with green or black vanes maximized target male catch while minimizing nontarget catch of important cranberry pollinators.