The Perceived Impact and Usability of a Care Management and Coordination System in Delivering Services to Vulnerable Populations: Mixed Methods Study.

BACKGROUND: People with complex needs, such as those experiencing homelessness, require concurrent, seamless support from multiple social service agencies. Sonoma County, California has one of the nation's largest homeless populations among largely suburban communities. To support client-centered care, the county deployed a Care Management and Coordination System (CMCS). This system comprised the Watson Care Manager (WCM), a front-end system, and Connect 360, which is an integrated data hub that aggregates information from various systems into a single client record. OBJECTIVE: The aim of this study is to evaluate the perceived impact and usability of WCM in delivering services to the homeless population in Sonoma County. METHODS: A mixed methods study was conducted to identify ways in which WCM helps to coordinate care. Interviews, observations, and surveys were conducted, and transcripts and field notes were thematically analyzed and directed by a grounded theory approach. Responses to the Technology Acceptance Model survey were analyzed. RESULTS: A total of 16 participants were interviewed, including WCM users (n=8) and department leadership members (n=8). In total, 3 interdisciplinary team meetings were observed, and 8 WCM users were surveyed. WCM provided a central shared platform where client-related, up-to-date, comprehensive, and reliable information from participating agencies was consolidated. Factors that facilitated WCM use were users' enthusiasm regarding the tool functionalities, scalability, and agency collaboration. Constraining factors included the suboptimal awareness of care delivery goals and functionality of the system among the community, sensitivities about data sharing and legal requirements, and constrained funding from government and nongovernment organizations. Overall, users found WCM to be a useful tool that was easy to use and helped to enhance performance. CONCLUSIONS: WCM supports the delivery of care to individuals with complex needs. Integration of data and information in a CMCS can facilitate coordinated care. Future research should examine WCM and similar CMCSs in diverse populations and settings.

Effect of nonpayment for preventable infections in U.S. hospitals.

BACKGROUND: In October 2008, the Centers for Medicare and Medicaid Services (CMS) discontinued additional payments for certain hospital-acquired conditions that were deemed preventable. The effect of this policy on rates of health care-associated infections is unknown. METHODS: Using a quasi-experimental design with interrupted time series with comparison series, we examined changes in trends of two health care-associated infections that were targeted by the CMS policy (central catheter-associated bloodstream infections and catheter-associated urinary tract infections) as compared with an outcome that was not targeted by the policy (ventilator-associated pneumonia). Hospitals participating in the National Healthcare Safety Network and reporting data on at least one health care-associated infection before the onset of the policy were eligible to participate. Data from January 2006 through March 2011 were included. We used regression models to measure the effect of the policy on changes in infection rates, adjusting for baseline trends. RESULTS: A total of 398 hospitals or health systems contributed 14,817 to 28,339 hospital unit-months, depending on the type of infection. We observed decreasing secular trends for both targeted and nontargeted infections long before the policy was implemented. There were no significant changes in quarterly rates of central catheter-associated bloodstream infections (incidence-rate ratio in the postimplementation vs. preimplementation period, 1.00; P=0.97), catheter-associated urinary tract infections (incidence-rate ratio, 1.03; P=0.08), or ventilator-associated pneumonia (incidence-rate ratio, 0.99; P=0.52) after the policy implementation. Our findings did not differ for hospitals in states without mandatory reporting, nor did it differ according to the quartile of percentage of Medicare admissions or hospital size, type of ownership, or teaching status. CONCLUSIONS: We found no evidence that the 2008 CMS policy to reduce payments for central catheter-associated bloodstream infections and catheter-associated urinary tract infections had any measurable effect on infection rates in U.S. hospitals. (Funded by the Agency for Healthcare Research and Quality.).

Population Segmentation Using a Novel Socio-Demographic Dataset.

Appending market segmentation data to a national healthcare knowledge, attitude and behavior survey and medical claims by geocode can provide valuable insight for providers, payers and public health entities to better understand populations at a hyperlocal level and develop cohort-specific strategies for health improvement. A prolonged use case investigates population factors, including social determinants of health, in depression and develops cohort-level management strategies, utilizing market segmentation and survey data. Survey response scores for each segment were normalized against the average national score and appended to claims data to identify at-risk segment whose scores were compared with three socio-demographically comparable but not at-risk segments via Nonparametric Mann-Whitney U test to identify specific risk factors for intervention. The marketing segment, New Melting Point (NMP), was identified as at-risk. The median scores of three comparable segments differed from NMP in "Inability to Pay For Basic Needs" (121% vs 123%), "Lack of Transportation" (112% vs 153%), "Utilities Threatened" (103% vs 239%), "Delay Visiting MD" (67% vs 181%), "Delay/Not Fill Prescription" (117% vs 182%), "Depressed: All/Most Time" (127% vs 150%), and "Internet: Virtual Visit" (55% vs 130%) (all with p<0.001). The appended dataset illustrates NMP as having many stressors (e.g., difficult social situations, delaying seeking medical care). Strategies to improve depression management in NMP could employ virtual visits, or pharmacy incentives. Insights gleaned from appending market segmentation and healthcare utilization survey data can fill in knowledge gaps from claims-based data and provide practical and actionable insights for use by providers, payers and public health entities.

Effectiveness of Contact Tracing for Viral Disease Mitigation and Suppression: Evidence-Based Review.

BACKGROUND: Contact tracing in association with quarantine and isolation is an important public health tool to control outbreaks of infectious diseases. This strategy has been widely implemented during the current COVID-19 pandemic. The effectiveness of this nonpharmaceutical intervention is largely dependent on social interactions within the population and its combination with other interventions. Given the high transmissibility of SARS-CoV-2, short serial intervals, and asymptomatic transmission patterns, the effectiveness of contact tracing for this novel viral agent is largely unknown. OBJECTIVE: This study aims to identify and synthesize evidence regarding the effectiveness of contact tracing on infectious viral disease outcomes based on prior scientific literature. METHODS: An evidence-based review was conducted to identify studies from the PubMed database, including preprint medRxiv server content, related to the effectiveness of contact tracing in viral outbreaks. The search dates were from database inception to July 24, 2020. Outcomes of interest included measures of incidence, transmission, hospitalization, and mortality. RESULTS: Out of 159 unique records retrieved, 45 (28.3%) records were reviewed at the full-text level, and 24 (15.1%) records met all inclusion criteria. The studies included utilized mathematical modeling (n=14), observational (n=8), and systematic review (n=2) approaches. Only 2 studies considered digital contact tracing. Contact tracing was mostly evaluated in combination with other nonpharmaceutical interventions and/or pharmaceutical interventions. Although some degree of effectiveness in decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality was observed, these results were highly dependent on epidemic severity (R0 value), number of contacts traced (including presymptomatic and asymptomatic cases), timeliness, duration, and compliance with combined interventions (eg, isolation, quarantine, and treatment). Contact tracing effectiveness was particularly limited by logistical challenges associated with increased outbreak size and speed of infection spread. CONCLUSIONS: Timely deployment of contact tracing strategically layered with other nonpharmaceutical interventions could be an effective public health tool for mitigating and suppressing infectious outbreaks by decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality.

Leveraging Informatics and Technology to Support Public Health Response: Framework and Illustrations using COVID-19.

OBJECTIVE: To develop a conceptual model and novel, comprehensive framework that encompass the myriad ways informatics and technology can support public health response to a pandemic. METHOD: The conceptual model and framework categorize informatics solutions that could be used by stakeholders (e.g., government, academic institutions, healthcare providers and payers, life science companies, employers, citizens) to address public health challenges across the prepare, respond, and recover phases of a pandemic, building on existing models for public health operations and response. RESULTS: Mapping existing solutions, technology assets, and ideas to the framework helped identify public health informatics solution requirements and gaps in responding to COVID-19 in areas such as applied science, epidemiology, communications, and business continuity. Two examples of technologies used in COVID-19 illustrate novel applications of informatics encompassed by the framework. First, we examine a hub from The Weather Channel, which provides COVID-19 data via interactive maps, trend graphs, and details on case data to individuals and businesses. Second, we examine IBM Watson Assistant for Citizens, an AI-powered virtual agent implemented by healthcare providers and payers, government agencies, and employers to provide information about COVID-19 via digital and telephone-based interaction. DISCUSSION: Early results from these novel informatics solutions have been positive, showing high levels of engagement and added value across stakeholders. CONCLUSION: The framework supports development, application, and evaluation of informatics approaches and technologies in support of public health preparedness, response, and recovery during a pandemic. Effective solutions are critical to success in recovery from COVID-19 and future pandemics.

A System to Support Diverse Social Program Management.

BACKGROUND: Social programs are services provided by governments, nonprofits, and other organizations to help improve the health and well-being of individuals, families, and communities. Social programs aim to deliver services effectively and efficiently, but they are challenged by information silos, limited resources, and the need to deliver frequently changing mandated benefits. OBJECTIVE: We aim to explore how an information system designed for social programs helps deliver services effectively and efficiently across diverse programs. METHODS: This viewpoint describes the configurable and modular architecture of Social Program Management (SPM), a system to support efficient and effective delivery of services through a wide range of social programs and lessons learned from implementing SPM across diverse settings. We explored usage data to inform the engagement and impact of SPM on the efficient and effective delivery of services. RESULTS: The features and functionalities of SPM seem to support the goals of social programs. We found that SPM provides fundamental management processes and configurable program-specific components to support social program administration; has been used by more than 280,000 caseworkers serving more than 30 million people in 13 countries; contains features designed to meet specific user requirements; supports secure information sharing and collaboration through data standardization and aggregation; and offers configurability and flexibility, which are important for digital transformation and organizational change. CONCLUSIONS: SPM is a user-centered, configurable, and flexible system for managing social program workflows.

Effectiveness of non-pharmaceutical interventions related to social distancing on respiratory viral infectious disease outcomes: A rapid evidence-based review and meta-analysis.

OBJECTIVES: Non-pharmaceutical interventions (e.g. quarantine and isolation) are used to mitigate and control viral infectious disease, but their effectiveness has not been well studied. For COVID-19, disease control efforts will rely on non-pharmaceutical interventions until pharmaceutical interventions become widely available, while non-pharmaceutical interventions will be of continued importance thereafter. METHODS: This rapid evidence-based review provides both qualitative and quantitative analyses of the effectiveness of social distancing non-pharmaceutical interventions on disease outcomes. Literature was retrieved from MEDLINE, Google Scholar, and pre-print databases (BioRxiv.org, MedRxiv.org, and Wellcome Open Research). RESULTS: Twenty-eight studies met inclusion criteria (n = 28). Early, sustained, and combined application of various non-pharmaceutical interventions could mitigate and control primary outbreaks and prevent more severe secondary or tertiary outbreaks. The strategic use of non-pharmaceutical interventions decreased incidence, transmission, and/or mortality across all interventions examined. The pooled attack rates for no non-pharmaceutical intervention, single non-pharmaceutical interventions, and multiple non-pharmaceutical interventions were 42% (95% confidence interval = 30% - 55%), 29% (95% confidence interval = 23% - 36%), and 22% (95% confidence interval = 16% - 29%), respectively. CONCLUSION: Implementation of multiple non-pharmaceutical interventions at key decision points for public health could effectively facilitate disease mitigation and suppression until pharmaceutical interventions become available. Dynamics around R 0 values, the susceptibility of certain high-risk patient groups to infection, and the probability of asymptomatic cases spreading disease should be considered.

Leveraging Data and Digital Health Technologies to Assess and Impact Social Determinants of Health (SDoH): a State-of-the-Art Literature Review.

OBJECTIVE: Identify how novel datasets and digital health technology, including both analytics-based and artificial intelligence (AI)-based tools, can be used to assess non-clinical, social determinants of health (SDoH) for population health improvement. METHODS: A state-of-the-art literature review with systematic methods was performed on MEDLINE, Embase, and the Cochrane Library databases and the grey literature to identify recently published articles (2013-2018) for evidence-based qualitative synthesis. Following single review of titles and abstracts, two independent reviewers assessed eligibility of full-texts using predefined criteria and extracted data into predefined templates. RESULTS: The search yielded 2,714 unique database records of which 65 met inclusion criteria. Most studies were conducted retrospectively in a United States community setting. Identity, behavioral, and economic factors were frequently identified social determinants, due to reliance on administrative data. Three main themes were identified: 1) improve access to data and technology with policy - advance the standardization and interoperability of data, and expand consumer access to digital health technologies; 2) leverage data aggregation - enrich SDoH insights using multiple data sources, and use analytics-based and AI-based methods to aggregate data; and 3) use analytics-based and AI-based methods to assess and address SDoH - retrieve SDoH in unstructured and structured data, and provide contextual care management sights and community-level interventions. CONCLUSIONS: If multiple datasets and advanced analytical technologies can be effectively integrated, and consumers have access to and literacy of technology, more SDoH insights can be identified and targeted to improve public health. This study identified examples of AI-based use cases in public health informatics, and this literature is very limited.

Natural Language Processing Tools for Assessing Progress and Outcome of Two Veteran Populations: Cohort Study From a Novel Online Intervention for Posttraumatic Growth.

BACKGROUND: Over 100 million Americans lack affordable access to behavioral health care. Among these, military veterans are an especially vulnerable population. Military veterans require unique behavioral health services that can address military experiences and challenges transitioning to the civilian sector. Real-world programs to help veterans successfully transition to civilian life must build a sense of community, have the ability to scale, and be able to reach the many veterans who cannot or will not access care. Digitally based behavioral health initiatives have emerged within the past few years to improve this access to care. Our novel behavioral health intervention teaches mindfulness-based cognitive behavioral therapy and narrative therapy using peer support groups as guides, with human-facilitated asynchronous online discussions. Our study applies natural language processing (NLP) analytics to assess effectiveness of our online intervention in order to test whether NLP may provide insights and detect nuances of personal change and growth that are not currently captured by subjective symptom measures. OBJECTIVE: This paper aims to study the value of NLP analytics in assessing progress and outcomes among combat veterans and military sexual assault survivors participating in novel online interventions for posttraumatic growth. METHODS: IBM Watson and Linguistic Inquiry and Word Count tools were applied to the narrative writings of combat veterans and survivors of military sexual trauma who participated in novel online peer-supported group therapies for posttraumatic growth. Participants watched videos, practiced skills such as mindfulness meditation, told their stories through narrative writing, and participated in asynchronous, facilitated online discussions with peers. The writings, including online postings, by the 16 participants who completed the program were analyzed after completion of the program. RESULTS: Our results suggest that NLP can provide valuable insights on shifts in personality traits, personal values, needs, and emotional tone in an evaluation of our novel online behavioral health interventions. Emotional tone analysis demonstrated significant decreases in fear and anxiety, sadness, and disgust, as well as increases in joy. Significant effects were found for personal values and needs, such as needing or desiring closeness and helping others, and for personality traits of openness, conscientiousness, extroversion, agreeableness, and neuroticism (ie, emotional range). Participants also demonstrated increases in authenticity and clout (confidence) of expression. NLP results were generally supported by qualitative observations and analysis, structured data, and course feedback. CONCLUSIONS: The aggregate of results in our study suggest that our behavioral health intervention was effective and that NLP can provide valuable insights on shifts in personality traits, personal values, and needs, as well as measure changes in emotional tone. NLP's sensitivity to changes in emotional tone, values, and personality strengths suggests the efficacy of NLP as a leading indicator of treatment progress.

Empowering Caseworkers to Better Serve the Most Vulnerable with a Cloud-Based Care Management Solution.

BACKGROUND: Care-management tools are typically utilized for chronic disease management. Sonoma County government agencies employed advanced health information technologies, artificial intelligence (AI), and interagency process improvements to help transform health and health care for socially disadvantaged groups and other displaced individuals. OBJECTIVES: The objective of this case report is to describe how an integrated data hub and care-management solution streamlined care coordination of government services during a time of community-wide crisis. METHODS: This innovative application of care-management tools created a bridge between social and clinical determinants of health and used a three-step approach-access, collaboration, and innovation. The program Accessing Coordinated Care to Empower Self Sufficiency Sonoma was established to identify and match the most vulnerable residents with services to improve their well-being. Sonoma County created an Interdepartmental Multidisciplinary Team to deploy coordinated cross-departmental services (e.g., health and human services, housing services, probation) to support individuals experiencing housing insecurity. Implementation of a data integration hub (DIH) and care management and coordination system (CMCS) enabled integration of siloed data and services into a unified view of citizen status, identification of clinical and social determinants of health from structured and unstructured sources, and algorithms to match clients across systems. RESULTS: The integrated toolset helped 77 at-risk individuals in crisis through coordinated care plans and access to services in a time of need. Two case examples illustrate the specific care and services provided individuals with complex needs after the 2017 Sonoma County wildfires. CONCLUSION: Unique application of a care-management solution transformed health and health care for individuals fleeing from their homes and socially disadvantaged groups displaced by the Sonoma County wildfires. Future directions include expanding the DIH and CMCS to neighboring counties to coordinate care regionally. Such solutions might enable innovative care-management solutions across a variety of public, private, and nonprofit services.

Population Health Measurement at Centers for Medicare & Medicaid Services: Bridging the Gap Between Public Health and Clinical Quality.

As Medicare and Medicaid increasingly shift to alternative payment models focused on population-based payments, there is an urgent need to develop measures of population health that can drive health improvement. In response, an assessment and design project established a framework for developing population health measures from a payer perspective, conducted environmental scans of existing measures and available data infrastructure, and conducted a gap analysis informing measure development and infrastructure needs. The work, summarized here, makes recommendations for creating a set of core measures, demonstrates some of the key challenges in applying a traditional quality measure development framework to population health, and complements recent efforts by the National Academy of Medicine and others with a focus on a payer perspective.

Impact of Medicare's Hospital-Acquired Condition policy on infections in safety net and non-safety net hospitals.

BACKGROUND: Policymakers may wish to align healthcare payment and quality of care while minimizing unintended consequences, particularly for safety net hospitals. OBJECTIVE: To determine whether the 2008 Centers for Medicare and Medicaid Services Hospital-Acquired Conditions policy had a differential impact on targeted healthcare-associated infection rates in safety net compared with non-safety net hospitals. DESIGN: Interrupted time-series design. SETTING AND PARTICIPANTS: Nonfederal acute care hospitals that reported central line-associated bloodstream infection and ventilator-associated pneumonia rates to the Centers for Disease Control and Prevention's National Health Safety Network from July 1, 2007, through December 31, 2013. RESULTS: We did not observe changes in the slope of targeted infection rates in the postpolicy period compared with the prepolicy period for either safety net (postpolicy vs prepolicy ratio, 0.96 [95% CI, 0.84-1.09]) or non-safety net (0.99 [0.90-1.10]) hospitals. Controlling for prepolicy secular trends, we did not detect differences in an immediate change at the time of the policy between safety net and non-safety net hospitals (P for 2-way interaction, .87). CONCLUSIONS: The Centers for Medicare and Medicaid Services Hospital-Acquired Conditions policy did not have an impact, either positive or negative, on already declining rates of central line-associated bloodstream infection in safety net or non-safety net hospitals. Continued evaluations of the broad impact of payment policies on safety net hospitals will remain important as the use of financial incentives and penalties continues to expand in the United States.

Medicaid managed care and STDs: missed opportunities to control the epidemic.

We examined the extent to which selected Medicaid managed care organizations (MCOs) promoted certain prevention and control services for sexually transmitted diseases (STDs) and the potential influence of health plans and medical groups on the delivery of STD care by primary care providers (PCPs) in seven large U.S. cities. Low-cost clinical services were routinely performed by PCPs, but higher-cost services were less often provided. Lack of organizational priority to promote STD prevention and control is a major barrier even for those MCOs that serve this high-risk, low-income population. Stronger incentives and legally binding provisions in Medicaid contracts are needed to promote adherence to standards of STD care.

Health care-associated infections among critically ill children in the US, 2007-2012.

BACKGROUND: Health care-associated infections (HAIs) are harmful and costly and can result in substantial morbidity for hospitalized children; however, little is known about national trends in HAIs in neonatal and pediatric populations. Our objective was to determine the incidence of HAIs among a large sample of hospitals in the United States caring for critically ill children from 2007 to 2012. METHODS: In this cohort study, we included NICUs and PICUs located in hospitals reporting data to the Centers for Disease Control and Prevention's National Healthcare Safety Network for central line-associated bloodstream infections (CLABSIs), ventilator-associated pneumonias, and catheter-associated urinary tract infections. We used a time-series design to evaluate changes in HAI rates. RESULTS: A total of 173 US hospitals provided data from NICUs, and 64 provided data from PICUs. From 2007 to 2012, rates of CLABSIs decreased in NICUs from 4.9 to 1.5 per 1000 central-line days (incidence rate ratio (IRR) per quarter = 0.96, 95% confidence interval 0.94-0.97) and in PICUs from 4.7 to 1.0 per 1000 central-line days (IRR per quarter = 0.96 [0.94-0.98]). Rates of ventilator-associated pneumonias decreased in NICUs from 1.6 to 0.6 per 1000 ventilator days (IRR per quarter = 0.97 [0.93-0.99]) and PICUs from 1.9 to 0.7 per 1000 ventilator-days (IRR per quarter = 0.95 [0.92-0.98]). Rates of catheter-associated urinary tract infections did not change significantly in PICUs. CONCLUSIONS: Between 2007 and 2012 there were substantial reductions in HAIs among hospitalized neonates and children.