Integrating Behavioral Health & Primary Care in New Hampshire: A Path Forward to Sustainable Practice & Payment Transformation

New Hampshire residents face challenges with behavioral and physical health conditions and the interplay between them. National studies show the costs and the burden of illness from behavioral health conditions and co-occurring chronic health conditions that are not adequately treated in either primary care or behavioral health settings. Bringing primary health and behavioral health care together in integrated care settings can improve outcomes for both behavioral and physical health conditions. Primary care integrated behavioral health works in conjunction with specialty behavioral health providers, expanding capacity, improving access, and jointly managing the care of patients with higher levels of acuity In its work to improve the health of NH residents and create effective and cost-effective systems of care, the NH Citizens Health Initiative (Initiative) created the NH Behavioral Health Integration Learning Collaborative (BHI Learning Collaborative) in November of 2015, as a project of its Accountable Care Learning Network (NHACLN). Bringing together more than 60 organizations, including providers of all types and sizes, all of the state’s community mental health centers, all of the major private and public insurers, and government and other stakeholders, the BHI Learning Collaborative built on earlier work of a NHACLN Workgroup focused on improving care for depression and co-occurring chronic illness. The BHI Learning Collaborative design is based on the core NHACLN philosophy of “shared data and shared learning” and the importance of transparency and open conversation across all stakeholder groups. The first year of the BHI Learning Collaborative programming included shared learning on evidence-based practice for integrated behavioral health in primary care, shared data from the NH Comprehensive Healthcare Information System (NHCHIS), and work to develop sustainable payment models to replace inadequate Fee-for-Service (FFS) revenues. Provider members joined either a Project Implementation Track working on quality improvement projects to improve their levels of integration or a Listen and Learn Track for those just learning about Behavioral Health Integration (BHI). Providers in the Project Implementation Track completed a self-assessment of levels of BHI in their practice settings and committed to submit EHR-based clinical process and outcomes data to track performance on specified measures. All providers received access to unblinded NHACLN Primary Care and Behavioral Health attributed claims data from the NHCHIS for provider organizations in the NH BHI Learning Collaborative. Following up on prior work focused on developing a sustainable model for integrating care for depression and co-occurring chronic illness in primary care settings, the BHI Learning Collaborative engaged consulting experts and participants in understanding challenges in Health Information Technology and Exchange (HIT/HIE), privacy and confidentiality, and workforce adequacy. The BHI Learning Collaborative identified a sustainable payment model for integrated care of depression in primary care. In the process of vetting the payment model, the BHI Learning Collaborative also identified and explored challenges in payment for Substance Use Disorder Screening, Brief Intervention and Referral to Treatment (SBIRT). New Hampshire’s residents will benefit from a health care system where primary care and behavioral health are integrated to support the care of the whole person. New Hampshire’s current opiate epidemic accentuates the need for better screening for behavioral health issues, prevention, and treatment referral integrated into primary care. New Hampshire providers and payers are poised to move towards greater integration of behavioral health and primary care and the Initiative looks forward to continuing to support progress in supporting a path to sustainable integrated behavioral and primary care

Proposal of a learning health system to transform the National Health System of Spain

This article identifies the main challenges of the National Health Service of Spain and proposes its transformation into a Learning Health System. For this purpose, the main indicators and reports published by the Spanish Ministries of Health and Finance, Organization for Economic Co-operation and Development (OECD) and World Health Organization (WHO) were reviewed. The Learning Health System proposal is based on some sections of an unpublished report, written by two of the authors under request of the Ministry of Health of Spain on Big Data for the National Health System. The main challenges identified are the rising old age dependency ratio; health expenditure pressures and the likely increase of out-of-pocket expenditure; drug expenditures, both retail and consumed in hospitals; waiting lists for surgery; potentially preventable hospital admissions; and the use of electronic health record (EHR) data to fulfil national health information and research objectives. To improve its efficacy, efficiency, and quality, the National Health Service of Spain should be transformed into a Learning Health System. Information and communication technologies (IT) enablers are a fundamental tool to address the complexity and vastness of health data as well as the urgency that clinical and management decisions require. Big Data solutions are a perfect match for that problem in health systems

Strengths and weaknesses in the implementation of maternal and perinatal death reviews in Tanzania: perceptions, processes and practice.

OBJECTIVES: Tanzania institutionalised maternal and perinatal death reviews (MPDR) in 2006, yet there is scarce evidence on the extent and quality of implementation of the system. We reviewed the national policy documentation and explored stakeholders' involvement in, and perspectives of, the role and practices of MPDR in district and regional hospitals, and assessed current capacity for achieving MPDR. METHODS: We reviewed the national MPDR guidelines and conducted a qualitative study using semi-structured interviews. Thirty-two informants in Mara Region were interviewed within health administration and hospitals, and five informants were included at the central level. Interviews were analysed for comparison of statements across health system level, hospital, profession and MPDR experience. RESULTS: The current MPDR system does not function adequately to either perform good quality reviews or fulfil the aspiration to capture every facility-based maternal and perinatal death. Informants at all levels express differing understandings of the purpose of MPDR. Hospital reviews fail to identify appropriate challenges and solutions at the facility level. Staff are committed to the process of maternal death review, with routine documentation and reporting, yet action and response are insufficient. CONCLUSION: The confusion between MPDR and maternal death surveillance and response results in a system geared towards data collection and surveillance, failing to explore challenges and solutions from within the remit of the hospital team. This reduces the accountability of the health workers and undermines opportunities to improve quality of care. We recommend initiatives to strengthen the quality of facility-level reviews in order to establish a culture of continuous quality of care improvement and a mechanism of accountability within facilities. Effective facility reviews are an important peer-learning process that should remain central to quality of care improvement strategies.<br/

‘The objective was about not blaming one another’: a qualitative study to explore how collaboration is experienced within quality improvement collaboratives in Ethiopia

Background: Quality improvement collaboratives are a common approach to improving quality of care. They rely on collaboration across and within health facilities to enable and accelerate quality improvement. Originating in high-income settings, little is known about how collaboration transfers to low-income settings, despite the widespread use of these collaboratives.// Method: We explored collaboration within quality improvement collaboratives in Ethiopia through 42 in-depth interviews with staff of two hospitals and four health centers and three with quality improvement mentors. Data were analysed thematically using a deductive and inductive approach.// Results: There was collaboration at learning sessions though experience sharing, co-learning and peer pressure. Respondents were used to a blaming environment, which they contrasted to the open and non-blaming environment at the learning sessions. Respondents formed new relationships that led to across facility practical support. Within facilities, those in the quality improvement team continued to collaborate through the plan-do-study-act cycles, although this required high engagement and support from mentors. Few staff were able to attend learning sessions and within facility transfer of quality improvement knowledge was rare. This affected broader participation and led to some resentment and resistance. Improved teamwork skills and behaviors occurred at individual rather than facility or systems level, with implications for sustainability. Challenges to collaboration included unequal participation, lack of knowledge transfer, high workloads, staff turnover and a culture of dependency.// Conclusion: We conclude that collaboration can occur and is valued within a traditionally hierarchical system, but may require explicit support at learning sessions and by mentors. More emphasis is needed on ensuring quality improvement knowledge transfer, buy-in and system level change. This could include a modified collaborative design to provide facility-level support for spread

Approaches to ensuring and improving quality in the context of health system strengthening: a cross-site analysis of the five African Health Initiative Partnership programs

Background: Integrated into the work in health systems strengthening (HSS) is a growing focus on the importance of ensuring quality of the services delivered and systems which support them. Understanding how to define and measure quality in the different key World Health Organization building blocks is critical to providing the information needed to address gaps and identify models for replication. Description of approaches We describe the approaches to defining and improving quality across the five country programs funded through the Doris Duke Charitable Foundation African Health Initiative. While each program has independently developed and implemented country-specific approaches to strengthening health systems, they all included quality of services and systems as a core principle. We describe the differences and similarities across the programs in defining and improving quality as an embedded process essential for HSS to achieve the goal of improved population health. The programs measured quality across most or all of the six WHO building blocks, with specific areas of overlap in improving quality falling into four main categories: 1) defining and measuring quality; 2) ensuring data quality, and building capacity for data use for decision making and response to quality measurements; 3) strengthened supportive supervision and/or mentoring; and 4) operational research to understand the factors associated with observed variation in quality. Conclusions: Learning the value and challenges of these approaches to measuring and improving quality across the key components of HSS as the projects continue their work will help inform similar efforts both now and in the future to ensure quality across the critical components of a health system and the impact on population health

Unmanned surface vehicle for intelligent water quality assessment to promote sustainable human health

Deteriorating water quality poses significant health risks globally, with billions at risk of waterborne diseases due to contamination. Limited data on water quality heightens these risks as conventional monitoring methods lack comprehensive coverage. While technologies like Internet of Things and machine learning offer real-time monitoring capabilities, they often provide point data insufficient for assessing entire water bodies. Remote sensing, though useful, has limitations such as measuring only optical parameters and being affected by climate and resolution issues. To address these challenges, an unmanned surface vehicle named ‘AquaDrone’ has been developed. AquaDrone traverses water bodies, collecting data on four key parameters (pH, dissolved oxygen, electrical conductivity, and temperature) along with GPS coordinates. These data are transmitted to a web portal via LoRa communication and Wi-Fi, where visualizations like trendlines and color-coded heatmaps are generated. A multilayer perceptron classifies water quality into five categories, aiding in real-time assessment. The AquaDrone system offers a feasible solution for monitoring small to medium-sized water bodies, crucial for safeguarding public health

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Continuous IoT-based maternal monitoring: system design, evaluation, opportunities, and challenges

Maternal care encompasses health care services for pregnant women during pregnancy, childbirth, and the postpartum period. Maternity care providers aim to ensure a healthy pregnancy, safe delivery, and smooth transition to motherhood. Traditional maternal care is offered through regular check-ups by health care professionals. In recent years, the emergence of Internet-of-Things (IoT)-based systems has transformed the way health care services are provided. These systems offer low-cost ubiquitous monitoring in everyday life settings and can be used for maternal monitoring. However, IoT-based maternal monitoring systems lack a comprehensive approach in maternal care because they are limited by sensing capabilities, specific health problems, and short periods of monitoring. Moreover, the use of IoT-based systems formaternal health monitoring requires addressing critical quality attributes, such as feasibility, energy efficiency, and reliability and validity of the collected physiological parameters. Quality assessment methods also must be integrated with such systems to discard the noisy part of collected parameters and improve the data quality. Furthermore, long-term, continuous IoT-based maternal monitoring by collecting data that was not traditionally available provides new opportunities, including analyzing the trend of physiological parameters during pregnancy and postpartum, as well as detecting maternal health issues. This thesis presents an IoT-based maternal monitoring system and explores its potential in maternal care. We evaluate the system’s feasibility, reliability, and energy efficiency. We also discuss the practical challenges of implementing the system. Then, we validate the heart rate (HR) and heart rate variability (HRV) parameters that the system collects while the user is asleep and awake. In addition, we propose a deep-learning-based method for quality assessment of HR and HRV parameters to discard unreliable data and improve health decisions. We use the system to collect data from 62 pregnant women during pregnancy and three-months postpartum. Then, the reliable HR and HRV parameters are used to track the trends during pregnancy and postpartum. Finally, we investigate maternal loneliness as a major mental health problem. We develop two predictive models to detect maternal loneliness during late pregnancy and the postpartum period. The models use the objective health parameters passively collected by the system and achieve high performance (weighted F1 scores > 0.87)