Role of visual analytics in supporting mental healthcare systems research and policy: A systematic scoping review

Discovering risk patterns in people with affective disorder-induced disabilities associated with their healthcare delay

BACKGROUND

People with affective disorder-induced disabilities (ADIDs) often experience complex needs that delay their healthcare. Discovering hidden patterns in these people for real-world use of health services is essential to improve healthcare delivery.

METHODS

A cross-sectional study population (2501 adults with ADIDs) was obtained from the Australian national representative survey of disability in 2015, including 21 demographic, health and social characteristics and healthcare delay information in general practice, specialist and hospital services. The Self-Organising Map Network was used to identify hidden risk patterns associated with healthcare delay and investigate potential predictors of class memberships by means of simple visualisations.

RESULTS

While experiencing disability avoidance showed across different healthcare delays, labour force appeared not to have any influence. Approximately 30% delayed their healthcare to general practice services; these were young, single females in great need of psychosocial support and aids for personal activities. Those who delayed their healthcare commonly presented a lack of social connections and a need for contact with family or friends not living in the same household.

CONCLUSIONS

The pattern evidence provides an avenue to further develop integrated care strategies with better targeting of people with ADIDs, considering social participation challenges facing them, to improve health service utilisation.

A Diagram of the Social-Ecological Conditions of Opioid Misuse and Overdose

The United States is experiencing a crisis of opioid misuse and overdose. To understand the underlying factors, researchers have begun looking upstream to identify social and structural determinants. However, no study has yet aggregated these into a comprehensive ecology of opioid overdose. We scoped 68 literature sources and compiled a master list of opioid misuse and overdose conditions. We grouped the conditions and used the Social Ecological Model to organize them into a diagram. We reviewed the diagram with nine subject matter experts (SMEs) who provided feedback on its content, design, and usefulness. From a literature search and SME interviews, we identified 80 unique conditions of opioid overdose and grouped them into 16 categories. In the final diagram, we incorporated 40 SME-recommended changes. In commenting on the diagram's usefulness, SMEs explained that the diagram could improve intervention planning by demonstrating the complexity of opioid overdose and highlighting structural factors. However, care is required to strike a balance between comprehensiveness and legibility. Multiple design formats may be useful, depending on the communication purpose and audience. This ecological diagram offers a visual perspective of the conditions of opioid overdose.

Data Analytics in Healthcare: A Tertiary Study

The field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analytics applications in different healthcare sectors, including diagnosis and disease profiling, diabetes, Alzheimer's disease, and sepsis. Machine learning and data mining were the most widely used data analytics techniques in healthcare applications, with a rising trend in popularity. Healthcare data analytics studies often utilize four popular databases in their primary study search, typically select 25-100 primary studies, and the use of research guidelines such as PRISMA is growing. The results may help both data analytics and healthcare researchers towards relevant and timely literature reviews and systematic mappings, and consequently, towards respective empirical studies. In addition, the meta-analysis presents a high-level perspective on prominent data analytics applications in healthcare, indicating the most popular topics in the intersection of data analytics and healthcare, and provides a big picture on a topic that has seen dozens of secondary studies in the last 2 decades.

Visualization Techniques in Healthcare Applications: A Narrative Review

Nowadays, healthcare management systems are adopting various techniques that facilitate the achievement of the goals of evidence-based medical practice. This review explores different visualization techniques and their importance in healthcare contexts. We performed a thorough search on databases such as the SLD portal, PubMed, and Google Scholar to obtain relevant studies. We selected recent articles published between 2018 and 2021 on visualization techniques in healthcare. The field of healthcare generates massive volumes of data that require visualization techniques to make them easily comprehensible and to guide their efficient presentation. Visualization in healthcare involves the effective presentation of information through graphics, images, and videos. Big data systems handle a massive amount of information and require visualization techniques to present it in a comprehensible manner. The significance of visualization techniques in healthcare is not confined to healthcare practitioners and healthcare management but encompasses all the stakeholders; patients can benefit from the visualization of his/her data for a better understanding of their condition. In short, visualization techniques have demonstrated their benefits in the healthcare sector and can be extended to the payer and the patient. They have also had a positive impact on the quality of the healthcare provided as well as patient safety.

The role of geographic information system and global positioning system in dementia care and research: a scoping review

Background

Geographic Information System (GIS) and Global Positioning System (GPS), vital tools for supporting public health research, provide a framework to collect, analyze and visualize the interaction between different levels of the health care system. The extent to which GIS and GPS applications have been used in dementia care and research is not yet investigated. This scoping review aims to elaborate on the role and types of GIS and GPS applications in dementia care and research.

Methods

A scoping review was conducted based on Arksey and O’Malley’s framework. All published articles in peer-reviewed journals were searched in PubMed, Scopus, and Web of Science, subject to involving at least one GIS/GPS approach focused on dementia. Eligible studies were reviewed, grouped, and synthesized to identify GIS and GPS applications. The PRISMA standard was used to report the study.

Results

Ninety-two studies met our inclusion criteria, and their data were extracted. Six types of GIS/GPS applications had been reported in dementia literature including mapping and surveillance (n = 59), data preparation (n = 26), dementia care provision (n = 18), basic research (n = 18), contextual and risk factor analysis (n = 4), and planning (n = 1). Thematic mapping and GPS were most frequently used techniques in the dementia field.

Conclusions

Even though the applications of GIS/GPS methodologies in dementia care and research are growing, there is limited research on GIS/GPS utilization in dementia care, risk factor analysis, and dementia policy planning. GIS and GPS are space-based systems, so they have a strong capacity for developing innovative research based on spatial analysis in the area of dementia. The existing research has been summarized in this review which could help researchers to know the GIS/GPS capabilities in dementia research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-022-00308-1.

Pathway mapping as a tool to address police use of force in behavioral health crisis

Police use of force is a significant problem in many communities, particularly related to episodes of behavioral health crisis where police are called to respond. Fragmentation of the behavioral health care system creates a revolving door where many residents with behavioral health challenges cycle in and out of the system, often accessing services via the 9-1-1 emergency system during a crisis episode. This work leverages ethnographic and participatory techniques to build a pathway map in order to represent and characterize the behavioral health crisis system in metropolitan Phoenix, Arizona, United States. Map findings illustrate that many nominally existing connections are functionally distant when viewed through the lens of a clinical handoff. The resulting pathway map can be used as a planning and confirmatory tool for community members, practitioners, and policymakers to address challenges in behavioral health and public safety.

Interactive Visualization Applications in Population Health and Health Services Research: Systematic Scoping Review

Background

Simple visualizations in health research data, such as scatter plots, heat maps, and bar charts, typically present relationships between 2 variables. Interactive visualization methods allow for multiple related facets such as numerous risk factors to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big health care data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support.

Objective

The primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods, and tools being used in population health and health services research (HSR) and their subdomains in the last 15 years, from January 1, 2005, to March 30, 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals, and co-design of applications.

Methods

We adapted standard scoping review guidelines with a peer-reviewed search strategy: 2 independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sectors. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and HSR, as well as their subdomains such as epidemiologic surveillance, health resource planning, access, and use and costs among diverse clinical and demographic populations.

Results

In this companion review to our earlier systematic synthesis of the literature on visual analytics applications, we present findings in 6 major themes of interactive visualization applications developed for 8 major problem categories. We found a wide application of interactive visualization methods, the major ones being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality, and studying medication use patterns. The data sources included mostly secondary administrative and electronic medical record data. In addition, at least two-thirds of the applications involved participatory co-design approaches while introducing a distinct category, embedded research, within co-design initiatives. These applications were in response to an identified need for data-driven insights into knowledge generation and decision support. We further discuss the opportunities stemming from the use of interactive visualization methods in studying global health; inequities, including social determinants of health; and other related areas. We also allude to the challenges in the uptake of these methods.

Conclusions

Visualization in health has strong historical roots, with an upward trend in the use of these methods in population health and HSR. Such applications are being fast used by academic and health care agencies for knowledge discovery, hypotheses generation, and decision support.

International Registered Report Identifier (IRRID)

RR2-10.2196/14019

Variables Impacting the Quality of Life of Dementia Caregivers: A Data Visualization Analysis

PURPOSE

Visually explore the rates of and relationships between overall physical and mental health, sleep disturbances, and depression rates in a single sample of caregivers of persons with dementia, caregivers of persons with other chronic illness, and non-caregiving adults.

DESIGN

Exploratory descriptive study utilizing data visualization methods.

METHODS

Data were analyzed from the 2017 Behavioral Risk Factor Surveillance System dataset. Multiple graphs and charts were developed to visualize data between groups. Descriptive statistics analyzed the rates of variables of interest across the three groups. One-way analysis of variance assessed relationships between variables.

RESULTS

Caregivers of persons with dementia and of other chronic illnesses reported poorer health outcomes as compared to non-caregiving adults. However, caregivers of persons with other chronic illnesses reported the worst outcomes of all groups. Depression and sleep disturbances were prevalent in all three groups.

CONCLUSIONS

The quality of life of caregivers of persons with dementia and chronic illness is impacted by poorer health outcomes, specifically mental health and sleep.

CLINICAL RELEVANCE

Findings support the need for caregiver-specific interventions that target overall physical and mental health, depression, and sleep disturbances. However, we also found support for mental health and sleep interventions for all individuals.

Translating health information into policy-making: A pragmatic framework

Knowledge translation (KT) is increasingly acknowledged to have the potential to improve policy-making. The value of health information (HI), as part of the KT context, is now also increasingly understood. This paper aims to identify existing tools for the translation of HI into policy-making and to develop a related framework facilitating future application of these identified tools. Updating and building upon a scoping review undertaken for the Health Evidence Network (HEN) Synthesis Report No. 54, commissioned by the World Health Organization (WHO) Regional Office for Europe in 2017, a literature search was conducted using the same databases (PubMed and Scopus) and the same keywords as in the WHO/HEN scoping review. All papers elaborating on tools enhancing the use of HI in policy-making were included. Of the 2549 records screened, 17 publications were included in this study. This review identified four different types of tools: 1) Visualisation and modelling tools, 2) Information packaging and synthesis tools, 3) Communication and dissemination tools and 4) Information linkage and exchange tools. The distinctions between these are fluid as different tools can be combined or incorporated into one another to complement each other. Our framework shows that communication/dissemination or linkage tools are crucial to effectively inform policy decisions through HI. This study helps to understand and guide the processes of KT of HI.

A Semiautomated Classification System for Producing Service Directories in Social and Health Care (DESDE-AND): Maturity Assessment Study

BACKGROUND

DESDE-LTC (Description and Evaluation of Services and DirectoriEs for Long-Term Care) is an international classification system that allows standardized coding and comparisons between different territories and care sectors, such as health and social care, in defined geographic areas. We adapted DESDE-LTC into a computer tool (DESDE-AND) for compiling a directory of care services in Andalucia, Spain.

OBJECTIVE

The aim of this study was to evaluate the maturity of DESDE-AND. A secondary objective of this study is to show the practicality of a new combined set of standard evaluation tools for measuring the maturity of health technology products.

METHODS

A system for semiautomated coding of service provision has been co-designed. A panel of 23 domain experts and a group of 68 end users participated in its maturity assessment that included its technology readiness level (TRL), usability, validity, adoption (Adoption Impact Ladder [AIL]), and overall degree of maturity [implementation maturity model [IMM]). We piloted the prototype in an urban environment (Seville, Spain).

RESULTS

The prototype was demonstrated in an operational environment (TRL 7). Sixty-eight different care services were coded, generating fact sheets for each service and its geolocation map. The observed agreement was 90%, with moderate reliability. The tool was partially adopted by the regional government of Andalucia (Spain), reaching a level 5 in adoption (AIL) and a level 4 in maturity (IMM) and is ready for full implementation.

CONCLUSIONS

DESDE-AND is a usable and manageable system for coding and compiling service directories and it can be used as a core module of decision support systems to guide planning in complex cross-sectoral areas such as combined social and health care.

Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review

Background

Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots.

Objective

This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR).

Methods

Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences.

Results

After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard.

Conclusions

With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage.

International Registered Report Identifier (IRRID)

RR2-10.2196/14019

Analysis of factors affecting IoT-based smart hospital design

Currently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.

[Everything is on the map: Integrated Mental Health Atlases as support tools for service planning. SESPAS Report 2020]

OBJECTIVE

This article reviews the usability of the Integrated Atlases of Mental Health as a decision support tool for service planning following a health ecosystem research approach.

METHOD

This study describes the types of atlases and the procedure for their development. Atlases carried out in Spain are presented and their impact in mental health service planning is assessed. Atlases comprise information on the local characteristics of the health care system, geographical availability of resources collected with the DESDE-LTC instrument and their use. Atlases use geographic information systems and other visualisation tools. Atlases follow a bottom-up collaborative approach involving decision-makers from planning agencies for their development and external validation.

RESULTS

Since 2005, Integrated Atlases of Mental Health have been developed for nine regions in Spain comprising over 65% of the Spanish inhabitants. The impact on service planning has been unequal for the different regions. Catalonia, Biscay and Gipuzkoa, and Andalusia reach the highest impact. In these areas, health advisors have been actively involved in their co-design and implementation in service planning.

CONCLUSIONS

Atlases allow detecting care gaps and duplications in care provision; monitoring changes of the system over time, and carrying out national and international comparisons, efficiency modelling and benchmarking. The knowledge provided by atlases could be incorporated to decision support systems in order to support an efficient mental health service planning based on evidence-informed policy.

Experts' perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study

BACKGROUND

Health experts including planners and policy-makers face complex decisions in diverse and constantly changing healthcare systems. Visual analytics may play a critical role in supporting analysis of complex healthcare data and decision-making. The purpose of this study was to examine the real-world experience that experts in mental healthcare planning have with visual analytics tools, investigate how well current visualisation techniques meet their needs, and suggest priorities for the future development of visual analytics tools of practical benefit to mental healthcare policy and decision-making.

METHODS

Health expert experience was assessed by an online exploratory survey consisting of a mix of multiple choice and open-ended questions. Health experts were sampled from an international pool of policy-makers, health agency directors, and researchers with extensive and direct experience of using visual analytics tools for complex mental healthcare systems planning. We invited them to the survey, and the experts' responses were analysed using statistical and text mining approaches.

RESULTS

The forty respondents who took part in the study recognised the complexity of healthcare systems data, but had most experience with and preference for relatively simple and familiar visualisations such as bar charts, scatter plots, and geographical maps. Sixty-five percent rated visual analytics as important to their field for evidence-informed decision-making processes. Fifty-five percent indicated that more advanced visual analytics tools were needed for their data analysis, and 67.5% stated their willingness to learn new tools. This was reflected in text mining and qualitative synthesis of open-ended responses.

CONCLUSIONS

This exploratory research provides readers with the first self-report insight into expert experience with visual analytics in mental healthcare systems research and policy. In spite of the awareness of their importance for complex healthcare planning, the majority of experts use simple, readily available visualisation tools. We conclude that co-creation and co-development strategies will be required to support advanced visual analytics tools and skills, which will become essential in the future of healthcare.

Assessing and Improving the Quality in Mental Health Services

Background: The mental health of the population consists of the three essential pillars of quality of life, economy, and society. Mental health services take care of the prevention and treatment of mental disorders and through them maintain, improve, and restore the mental health of the population. The purpose of this study is to describe the methodology for qualitative and quantitative evaluation and improvement of the mental health service system. Methods: This is a narrative review study that searches the literature to provide criteria, indicators, and methodology for evaluating and improving the quality of mental health services and the related qualitative and quantitative indicators. The bibliography was searched in popular databases PubMed, Google Scholar, CINAHL, using the keywords “mental”, “health”, “quality”, “indicators”, alone or in combinations thereof. Results: Important quality indicators of mental health services have been collected and presented, and modified where appropriate. The definition of each indicator is presented here, alongside its method of calculation and importance. Each indicator belongs to one of the eight dimensions of quality assessment: (1) Suitability of services, (2) Accessibility of patients to services, (3) Acceptance of services by patients, (4) Ability of healthcare professionals to provide services, (5) Efficiency of health professionals and providers, (6) Continuity of service over time (ensuring therapeutic continuity), (7) Efficiency of health professionals and services, (8) Safety (for patients and for health professionals). Discussion/Conclusions: Accessibility and acceptability of service indicators are important for the attractiveness of services related to their use by the population. Profitability indicators are important economic indicators that affect the viability and sustainability of services, factors that are now taken into account in any health policy. All of the indicators mentioned are related to public health, affecting the quality of life, morbidity, mortality, and life expectancy, directly or indirectly. The systematic measurement and monitoring of indicators and the measurement and quantification of quality through them, are the basis for evidence-based health policy for improvement of the quality of mental health services.