Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support

Expert-based collaborative analysis of the situation and prospects of biomarker test implementation in oncology in Spain

PURPOSE

Biomarkers as screening for precision medicine is a fundamental step. The purpose of this article is twofold. First, to highlight the existing barriers in the implementation of Precision Medicine in Spain, with a special emphasis on barriers in access to the determination of biomarkers. Second, to provide a Roadmap that can help implement Precision Medicine equitably at the national level and optimize the use of biomarkers.

METHODS

A systematic review of literature (SRL) and a focus group (FG) with multidisciplinary experts has been carried out in 2023. Participants were contacted individually, and discourse analysis was processed anonymously.

RESULTS

We carried out a quantitative (SRL) and a qualitative approach (FG). The discourse analysis and roadmap were sent individually to each expert for approval.

CONCLUSIONS

The potential of Precision Medicine has not been fulfilled in Spain. While several regional initiatives are in place, a national plan or strategy around Precision Medicine and use of biomarkers is lacking. In a general context of rapid progress at a global and European level, including the 2021 Europe's Beating Cancer Plan, it is time to define and implement a National Plan to make the promise come true. While some comparable countries within Europe - such as the UK or France - are mature enough to adopt such strategies, in Spain there is still a long way to go. We consider that the different strands of work outlined in the Roadmap can be used as basis for such purpose.

Mental health systems modelling for evidence-informed service reform in Australia

Australia's Fifth National Mental Health Plan required governments to report, not only on the progress of changes to mental health service delivery, but to also plan for services that should be provided. Future population demand for treatment and care is challenging to predict and one solution involves modelling the uncertain demands on the system. Modelling can help decision-makers understand likely future changes in mental health service demand and more intelligently choose appropriate responses. It can also support greater scrutiny, accountability and transparency of these processes. Australia has an emerging national capacity for systems modelling in mental health which can enhance the next phase of mental health reform. This paper introduces concepts useful for understanding mental health modelling and identifies where modelling approaches can support health service planners to make evidence-informed decisions regarding planning and investment for the Australian population.

A Realist Evaluation of Case Management Models for People with Complex Health Conditions Using Novel Methods and Tools-What Works, for Whom, and under What Circumstances?

Case management developed from a generalist model to a person-centred model aligned with the evidence-informed evolution of best practice people-centred integrated care. Case management is a multidimensional and collaborative integrated care strategy where the case manager performs a set of interventions/actions to support the person with a complex health condition to progress in their recovery pathway and participate in life roles. It is currently unknown what case management model works in real life for whom and under what circumstances. The purpose of this study was to answer these questions. The study methods used realistic evaluation framework, examined the patterns and associations between case manager actions (mechanisms), the person's characteristics and environment (context), and recovery (outcomes) over 10 years post severe injury. There was mixed methods secondary analysis of data extracted via in-depth retrospective file reviews (n = 107). We used international frameworks and a novel approach with multi-layered analysis including machine learning and expert guidance for pattern identification. The study results confirm that when provided, a person-centred case management model contributes to and enhances the person's recovery and progress towards participation in life roles and maintaining well-being after severe injury.Furthermore, the intensity of case management for people with traumatic brain injury, and the person-centred actions of advising, emotional and motivational support, and proactive coordination contribute to the person achieving their goals. The results provide learnings for case management services on the case management models, for quality appraisal, service planning, and informs further research on case management.

Regional evolution of psychosocial services in Australia before and after the implementation of the National Disability Insurance Scheme

OBJECTIVES

This paper compares the evolution of the psychosocial sector in two Australian regions pre and post introduction of the National Disability Insurance Scheme - a major reform to the financing, planning and provision of disability services in Australia, intended to create greater competition and efficiency in the market, and more choice for service users.

METHODS

We used a standardised service classification instrument based on a health ecosystems approach to assess service availability and diversity of psychosocial services provided by non-government organisations in two Primary Health Network regions.

RESULTS

We identified very different evolutionary pathways in the two regions. Service availability increased in Western Sydney but decreased in the Australian Capital Territory. The diversity of services available did not increase in either Primary Health Network 4 years after the reform. Many services were experiencing ongoing funding uncertainty.

CONCLUSION

Assumptions of increased efficiency through organisational scaling up, and a greater diversity in range of service availability were not borne out.

IMPLICATIONS

This study shows the urgent need for evaluation of the effects of the NDIS on the provision of psychosocial care in Australia. Four years after the implementation of the NDIS at vast expense key objectives not been met for consumers or for the system as a whole, and an environment of uncertainty has been created for providers. It demonstrates the importance of standardised service mapping to monitor the effects of major reforms on mental health care as well as the need for a focus at the local level.

Identification of potential welfare and survival indicators for stranded cetaceans through international, interdisciplinary expert opinion

Management of live cetacean strandings generally focuses on refloating animals, yet there is a lack of scientific data to inform decision-making. Valid indicators that are practical to measure are needed to assess welfare status and survival likelihood for stranded cetaceans. The Delphi method was applied to gather international and interdisciplinary expert opinion to provide face validity to potential indicators of stranded cetacean welfare and survival likelihood. Two online questionnaires were conducted. In the first questionnaire these experts identified potential indicators of stranded cetacean welfare and survival likelihood. These indicators were subsequently scored by the same experts in questionnaire two, based on their value for assessing welfare/survival likelihood and being practical to measure. Indicators considered valuable and practical for assessing welfare and survival likelihood at strandings included animal-based indices of body and skin condition, signs of physical trauma, respiration rate and various behaviours. Resource-/management-based indicators related mainly to human intervention and should be correlated with animal-based indices to provide relevant evaluations. Importantly, inextricable links between welfare and survival for stranded cetaceans are emphasized, with 90% of indicators being similar for both. Investigations into these indicators should be conducted to develop a practical, science-based assessment framework to inform decision-making during stranding events.

Co-leadership to co-design in mental health-care ecosystems: what does it mean to us?

PURPOSE

This study aims to demonstrate how service providers, service users and their families should be able to share the co-leadership, co-auspicing, co-ownership, and co-governance, of a the mental health-care ecosystem, at every level, as it develops upwards and wider, in a process of inclusivity, conviviality and polyphonic discourse, via the overlapping phases of co-creativity, codesign, co-production, co-delivery, co-evaluation, co-research and co-replication, to achieve outcomes of co-communal or organisational well-being.

DESIGN/METHODOLOGY/APPROACH

"Co-design" is shorthand code for encouraging multiple pathways and trajectories toward forming and sustaining a sparkling web or vibrant network of inclusive opportunities for stakeholder participation and a collaborative partnership in organizational development, in these circumstances, for more effective mental health services (MHSs).

FINDINGS

In a co-design framework, all partners should be entitled to expect and "to have and to hold" an ongoing equal stake, voice and power in the discourse from start to finish, in a bottom-up process which is fostered by an interdisciplinary leadership group, providing the strong foundation or nutrient-rich and well-watered soil and support from which a shared endeavor can grow, blossom and generate the desired fruit in ample quality and quantity.

ORIGINALITY/VALUE

The authors should be working toward co-design and co-production of contemporary MHSs in a mental health-care ecosystem.

Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem

Major efforts worldwide have been made to provide balanced Mental Health (MH) care. Any integrated MH ecosystem includes hospital and community-based care, highlighting the role of outpatient care in reducing relapses and readmissions. This study aimed (i) to identify potential expert-based causal relationships between inpatient and outpatient care variables, (ii) to assess them by using statistical procedures, and finally (iii) to assess the potential impact of a specific policy enhancing the MH care balance on real ecosystem performance. Causal relationships (Bayesian network) between inpatient and outpatient care variables were defined by expert knowledge and confirmed by using multivariate linear regression (generalized least squares). Based on the Bayesian network and regression results, a decision support system that combines data envelopment analysis, Monte Carlo simulation and fuzzy inference was used to assess the potential impact of the designed policy. As expected, there were strong statistical relationships between outpatient and inpatient care variables, which preliminarily confirmed their potential and a priori causal nature. The global impact of the proposed policy on the ecosystem was positive in terms of efficiency assessment, stability and entropy. To the best of our knowledge, this is the first study that formalized expert-based causal relationships between inpatient and outpatient care variables. These relationships, structured by a Bayesian network, can be used for designing evidence-informed policies trying to balance MH care provision. By integrating causal models and statistical analysis, decision support systems are useful tools to support evidence-informed planning and decision making, as they allow us to predict the potential impact of specific policies on the ecosystem prior to its real application, reducing the risk and considering the population’s needs and scientific findings.

Augmenting BDI Agency with a Cognitive Service: Architecture and Validation in Healthcare Domain

Autonomous intelligent systems are starting to influence clinical practice, as ways to both readily exploit experts' knowledge when contextual conditions demand so, and harness the overwhelming amount of patient related data currently at clinicians' disposal. However, these two approaches are rarely synergistically exploited, and tend to be used without integration. In this paper, we follow recent efforts reported in the literature regarding integration of BDI agency with machine learning based Cognitive Services, by proposing an integration architecture, and by validating such architecture in the complex domain of trauma management. In particular, we show that augmentation of a BDI agent, endowed with predefined plans encoding experts' knowledge, with a Cognitive Service, trained on past observed data, can enhance trauma management by reducing over triage episodes.

A Pluralistic Perspective on Research in Psychotherapy: Harnessing Passion, Difference and Dialogue to Promote Justice and Relevance

The adoption of a pluralistic perspective on research design, processes of data collection and analysis and dissemination of findings, has the potential to enable psychotherapy research to make a more effective contribution to building a just society. A review of the key features of the concept of pluralism is followed by a historical analysis of the ways in which research in counselling, psychotherapy and related disciplines has moved in the direction of a pluralistic position around knowledge creation. Core principles of a pluralistic approach to research are identified and explored in the context of a critical case study of contemporary research into psychotherapy for depression, examples of pluralistically oriented research practices, and analysis of a pluralistic conceptualisation of the nature of evidence. Implications of a pluralistic perspective for research training and practice are discussed. Pluralistic inquiry that emphasises dialogue, collaboration, epistemic justice and the co-existence of multiple truths, creates opportunities for individuals, families and communities from a wide range of backgrounds to co-produce knowledge in ways that support their capacities for active citizenship and involvement in open democratic decision-making. To fulfil these possibilities, it is necessary for psychotherapy research to be oriented towards social goals that are sufficiently relevant to both researchers and co-participants to harness their passion and work together for a common good.

Patterns of Service Provision in Older People's Mental Health Care in Australia

Australia has a population of around 4 million people aged 65 years and over, many of whom are at risk of developing cognitive decline, mental illness, and/or psychological problems associated with physical illnesses. The aim of this study was to describe the pattern of specialised mental healthcare provision (availability, placement capacity, balance of care and diversity) for this age group in urban and rural health districts in Australia. The Description and Evaluation of Services and DirectoriEs for Long Term Care (DESDE-LTC) tool was used in nine urban and two rural health districts of the thirty-one Primary Health Networks across Australia. For the most part service provision was limited to hospital and outpatient care across all study areas. The latter was mainly restricted to health-related outpatient care, and there was a relative lack of social outpatient care. While both acute and non-acute hospital care were available in urban areas, in rural areas hospital care was limited to acute care. Limited access to comprehensive mental health care, and the uniformity in provision across areas in spite of differences in demographic, socioeconomic and health characteristics raises issues of equity in regard to psychogeriatric care in this country. Comparing patterns of mental health service provision across the age span using the same classification method allows for a better understanding of care provision and gap analysis for evidence-informed policy.

[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.

The future of community psychiatry and community mental health services

PURPOSE OF REVIEW

The aim of this article is to provide a framework and analysis of a series of critical components to inform the future design, development, sustaining, and monitoring of community mental health services.

RECENT FINDINGS

Many mental health services remain too hospital-centric, often without adequate outreach services. On the basis of outcome evidence, we need to shift the balance of mental health services from hospital-centered with community outreach when convenient for staff, to community-centered and mobile, with in-reach to hospital only when necessary. Too few training programs those with emphasize the macroskills of public advocacy, working with service users, families, social movements, and the media to improve mental health and wellbeing of regional and local communities.

SUMMARY

We should adopt a health ecosystems approach to mental healthcare and training, encompassing nano to macrolevels of service in every region. Catchment mental health services should be rebuilt as community-centric mental health services, integrating all community and inpatient components, but led and integrated from community sites. Community psychiatrists and mental health professionals of the future will need to be well trained in the nano to macroskills required to take responsibility for the mental health and wellbeing of their catchment communities and to provide leadership in service-planning, management, and continuing revision on the basis of rigorous evaluation. These approaches should be the core of all training in psychiatry and all mental health professions prior to any subspecialization.

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.

Cost of borderline personality disorder in Catalonia (Spain)

Introduction

The available information on the cost of illness of Borderline Personality Disorder (BPD) is overtly insufficient for policy planning. Our aim was to estimate the costs of illness for BPD in Catalonia (Spain) for 2006.

Methods

This is a multilevel cross-design synthesis study combining a qualitative nominal approach, quantitative ‘top-down’ analysis of multiple health databases, and ‘bottom-up’ data of local surveys. Both direct and indirect costs have been estimated from a governmental and societal perspective.

Results

Estimated year-prevalence of BPD was 0.7% (41,921 cases), but only 9.6% of these cases were treated in the mental health system (4033 cases). The baseline of the total cost of BPD in Catalonia was 45.6 million €, of which 15.8 million € (34.7%) were direct costs related to mental health care. The cost distribution was 0.4% in primary care; 4% in outpatient mental health care; 4.7% in hospitalisation; 0.7% in emergency care; and 24.9% in pharmacotherapy. Additionally, the cost of drug addiction treatment for persons with BPD was 11.2%; costs associated with sheltered employment were 23.9% and those of crime and justice were 9.7%. Indirect costs – including temporary sick leave and premature death (suicide) – represented 20.5% of total costs. The average annual cost per patient was 11,308 €.

Conclusions

An under-reporting of BPD was identified by the experts in all health databases and official registries. Most of the BPD costs were not related to mental health care. Amongst the direct cost categories, pharmacotherapy had the largest proportion despite the lack of specificity for BPD. This distribution of costs reinforces the idea of BPD complexity related to an inadequate and inefficient use of health resources.

Designing combination therapies using multiple optimal controls

Strategic management of populations of interacting biological species routinely requires interventions combining multiple treatments or therapies. This is important in key research areas such as ecology, epidemiology, wound healing and oncology. Despite the well developed theory and techniques for determining single optimal controls, there is limited practical guidance supporting implementation of combination therapies. In this work we use optimal control theory to calculate optimal strategies for applying combination therapies to a model of acute myeloid leukaemia. We present a versatile framework to systematically explore the trade-offs that arise in designing combination therapy protocols using optimal control. We consider various combinations of continuous and bang-bang (discrete) controls, and we investigate how the control dynamics interact and respond to changes in the weighting and form of the pay-off characterising optimality. We demonstrate that the optimal controls respond non-linearly to treatment strength and control parameters, due to the interactions between species. We discuss challenges in appropriately characterising optimality in a multiple control setting and provide practical guidance for applying multiple optimal controls. Code used in this work to implement multiple optimal controls is available on GitHub.

Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges

Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. However, recent developments in artificial intelligence (AI) have led to algorithms and systems that are able to learn and extract complex patterns from such data. AI has already been applied successfully to such combined datasets, with applications such as improving the insurance claim processing pipeline and reducing estimation biases in retrospective studies. Nevertheless, there is still the potential to do much more. The identification of complex patterns within high dimensional datasets may find new predictors for early onset of diseases or lead to a more proactive offering of personalized preventive services. While there are potential risks and challenges associated with the use of AI, these are not insurmountable. As with the introduction of any innovation, it will be necessary to be thoughtful and responsible as we increasingly apply AI methods in healthcare.

A decision support system for assessing management interventions in a mental health ecosystem: The case of Bizkaia (Basque Country, Spain)

Evidence-informed strategic planning is a top priority in Mental Health (MH) due to the burden associated with this group of disorders and its societal costs. However, MH systems are highly complex, and decision support tools should follow a systems thinking approach that incorporates expert knowledge. The aim of this paper is to introduce a new Decision Support System (DSS) to improve knowledge on the health ecosystem, resource allocation and management in regional MH planning. The Efficient Decision Support-Mental Health (EDeS-MH) is a DSS that integrates an operational model to assess the Relative Technical Efficiency (RTE) of small health areas, a Monte-Carlo simulation engine (that carries out the Monte-Carlo simulation technique), a fuzzy inference engine prototype and basic statistics as well as system stability and entropy indicators. The stability indicator assesses the sensitivity of the model results due to data variations (derived from structural changes). The entropy indicator assesses the inner uncertainty of the results. RTE is multidimensional, that is, it was evaluated by using 15 variable combinations called scenarios. Each scenario, designed by experts in MH planning, has its own meaning based on different types of care. Three management interventions on the MH system in Bizkaia were analysed using key performance indicators of the service availability, placement capacity in day care, health care workforce capacity, and resource utilisation data of hospital and community care. The potential impact of these interventions has been assessed at both local and system levels. The system reacts positively to the proposals by a slight increase in its efficiency and stability (and its corresponding decrease in the entropy). However, depending on the analysed scenario, RTE, stability and entropy statistics can have a positive, neutral or negative behaviour. Using this information, decision makers can design new specific interventions/policies. EDeS-MH has been tested and face-validated in a real management situation in the Bizkaia MH system.

Causal Modelling for Supporting Planning and Management of Mental Health Services and Systems: A Systematic Review

Mental health services and systems (MHSS) are characterized by their complexity. Causal modelling is a tool for decision-making based on identifying critical variables and their causal relationships. In the last two decades, great efforts have been made to provide integrated and balanced mental health care, but there is no a clear systematization of causal links among MHSS variables. This study aims to review the empirical background of causal modelling applications (Bayesian networks and structural equation modelling) for MHSS management. The study followed the PRISMA guidelines (PROSPERO: CRD42018102518). The quality of the studies was assessed by using a new checklist based on MHSS structure, target population, resources, outcomes, and methodology. Seven out of 1847 studies fulfilled the inclusion criteria. After the review, the selected papers showed very different objectives and subjects of study. This finding seems to indicate that causal modelling has potential to be relevant for decision-making. The main findings provided information about the complexity of the analyzed systems, distinguishing whether they analyzed a single MHSS or a group of MHSSs. The discriminative power of the checklist for quality assessment was evaluated, with positive results. This review identified relevant strategies for policy-making. Causal modelling can be used for better understanding the MHSS behavior, identifying service performance factors, and improving evidence-informed policy-making.

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning

Background

Decision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning.

Methods

We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model.

Results

The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment).

Conclusions

This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice.

Electronic supplementary material

The online version of this article (10.1186/s12961-018-0308-y) contains supplementary material, which is available to authorized users.

From Population Databases to Research and Informed Health Decisions and Policy

Background

In the era of big data, the medical community is inspired to maximize the utilization and processing of the rapidly expanding medical datasets for clinical-related and policy-driven research. This requires a medical database that can be aggregated, interpreted, and integrated at both the individual and population levels. Policymakers seek data as a lever for wise, evidence-based decision-making and information-driven policy. Yet, bridging the gap between data collection, research, and policymaking, is a major challenge.

The model

To bridge this gap, we propose a four-step model: (A) creating a conjoined task force of all relevant parties to declare a national program to promote collaborations; (B) promoting a national digital records project, or at least a network of synchronized and integrated databases, in an accessible transparent manner; (C) creating an interoperative national research environment to enable the analysis of the organized and integrated data and to generate evidence; and (D) utilizing the evidence to improve decision-making, to support a wisely chosen national policy. For the latter purpose, we also developed a novel multidimensional set of criteria to illuminate insights and estimate the risk for future morbidity based on current medical conditions.

Conclusion

Used by policymakers, providers of health plans, caregivers, and health organizations, we presume this model will assist transforming evidence generation to support the design of health policy and programs, as well as improved decision-making about health and health care, at all levels: individual, communal, organizational, and national.

Using Electronic Case Summaries to Elicit Multi-Disciplinary Expert Knowledge about Referrals to Post-Acute Care

BACKGROUND

Eliciting knowledge from geographically dispersed experts given their time and scheduling constraints, while maintaining anonymity among them, presents multiple challenges.

OBJECTIVES

Describe an innovative, Internet based method to acquire knowledge from experts regarding patients who need post-acute referrals. Compare, 1) the percentage of patients referred by experts to percentage of patients actually referred by hospital clinicians, 2) experts' referral decisions by disciplines and geographic regions, and 3) most common factors deemed important by discipline.

METHODS

De-identified case studies, developed from electronic health records (EHR), contained a comprehensive description of 1,496 acute care inpatients. In teams of three, physicians, nurses, social workers, and physical therapists reviewed case studies and assessed the need for post-acute care referrals; Delphi rounds followed when team members did not agree. Generalized estimating equations (GEEs) compared experts' decisions by discipline, region of the country and to the decisions made by study hospital clinicians, adjusting for the repeated observations from each expert and case. Frequencies determined the most common case characteristics chosen as important by the experts.

RESULTS

The experts recommended referral for 80% of the cases; the actual discharge disposition of the patients showed referrals for 67%. Experts from the Northeast and Midwest referred 5% more cases than experts from the West. Physicians and nurses referred patients at similar rates while both referred more often than social workers. Differences by discipline were seen in the factors identified as important to the decision.

CONCLUSION

The method for eliciting expert knowledge enabled national dispersed expert clinicians to anonymously review case summaries and make decisions about post-acute care referrals. Having time and a comprehensive case summary may have assisted experts to identify more patients in need of post-acute care than the hospital clinicians. The methodology produced the data needed to develop an expert decision support system for discharge planning.

"Intellectual developmental disorders": reflections on the international consensus document for redefining "mental retardation-intellectual disability" in ICD-11

PURPOSE

The debate as to whether intellectual disability (ID) should be conceptualized as a health condition or as a disability has intensified as the revision of World Health Organization's (WHO's) International Classification of Diseases (ICD) is being finalized. Defining ID as a health condition is central to retaining it in ICD, with significant implications for health policy and access to health services. The purpose of this paper is to include some reflections on the consensus document produced by the first WHO Working Group on the Classification of MR (WHO WG-MR) and on the process that was followed to realize it. The consensus report was the basis for the development of official recommendations sent to the WHO Advisory Group for ICD-11.

DESIGN/METHODOLOGY/APPROACH

A mixed qualitative approach was followed in a series of meetings leading to the final consensus report submitted to the WHO Advisory group. These recommendations combined prior expert knowledge with available evidence; a nominal approach was followed throughout with face-to-face conferences.

FINDINGS

The WG recommended a synonym set ("synset") ontological approach to the conceptualisation of this health condition underlying a clinical rationale for its diagnosis. It proposed replacing MR with Intellectual Developmental Disorders (IDD) in ICD-11, defined as "a group of developmental conditions characterized by a significant impairment of cognitive functions, which are associated with limitations of learning, adaptive behaviour and skills". The WG further advised that IDD be included under the parent category of neurodevelopmental disorders, that current distinctions (mild, moderate, severe and profound) be continued as severity qualifiers, and that problem behaviours removed from its core classification structure and instead described as associated features.

ORIGINALITY/VALUE

Within the ID/IDD synset two different names combine distinct aspects under a single construct that describes its clinical as well as social, educational and policy utilities. The single construct incorporates IDD as a clinical meta-syndrome, and ID as its functioning and disability counterpart. IDD and ID are not synonymous or mirror concepts as they have different scientific, social and policy applications. New diagnostic criteria for IDD should be based on a developmental approach, which accounts for the complex causal factors known to impact the acquisition of specific cognitive abilities and adaptive behaviours. The paper focuses on a new clinical framework for the diagnosis of IDD that also includes and complements the existing social, educational and policy components inherent in ID.

Personalized Weight Management Interventions for Cardiovascular Risk Reduction: A Viable Option for African-American Women

Obesity is an independent contributor to cardiovascular disease (CVD) and a major driving force behind racial/ethnic and gender disparities in risk. Due to a multitude of interrelating factors (i.e., personal, social, cultural, economic and environmental), African-American (AA) women are disproportionately obese and twice as likely to succumb to CVD, yet they are significantly underrepresented in behavioral weight management interventions. In this selective review we highlight components of the limited interventions shown to enhance weight loss outcomes in this population and make a case for leveraging Web-based technology and artificial intelligence techniques to deliver personalized programs aimed at obesity treatment and CVD risk reduction. Although many of the approaches discussed are generally applicable across populations burdened by disparate rates of obesity and CVD, we specifically focus on AA women due to the disproportionate impact of these non-communicable diseases and the general paucity of interventions targeted to this high-risk group.

Evidence-based medicine: is it a bridge too far?

AIMS

This paper aims to describe the contextual factors that gave rise to evidence-based medicine (EBM), as well as its controversies and limitations in the current health context. Our analysis utilizes two frameworks: (1) a complex adaptive view of health that sees both health and healthcare as non-linear phenomena emerging from their different components; and (2) the unified approach to the philosophy of science that provides a new background for understanding the differences between the phases of discovery, corroboration, and implementation in science.

RESULTS

The need for standardization, the development of clinical epidemiology, concerns about the economic sustainability of health systems and increasing numbers of clinical trials, together with the increase in the computer's ability to handle large amounts of data, have paved the way for the development of the EBM movement. It was quickly adopted on the basis of authoritative knowledge rather than evidence of its own capacity to improve the efficiency and equity of health systems. The main problem with the EBM approach is the restricted and simplistic approach to scientific knowledge, which prioritizes internal validity as the major quality of the studies to be included in clinical guidelines. As a corollary, the preferred method for generating evidence is the explanatory randomized controlled trial. This method can be useful in the phase of discovery but is inadequate in the field of implementation, which needs to incorporate additional information including expert knowledge, patients' values and the context.

CONCLUSION

EBM needs to move forward and perceive health and healthcare as a complex interaction, i.e. an interconnected, non-linear phenomenon that may be better analysed using a variety of complexity science techniques.

Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems

While the use of machine learning methods in clinical decision support has great potential for improving patient care, acquiring standardized, complete, and sufficient training data presents a major challenge for methods relying exclusively on machine learning techniques. Domain experts possess knowledge that can address these challenges and guide model development. We present Advice-Based-Learning (ABLe), a framework for incorporating expert clinical knowledge into machine learning models, and show results for an example task: estimating the probability of malignancy following a non-definitive breast core needle biopsy. By applying ABLe to this task, we demonstrate a statistically significant improvement in specificity (24.0% with p=0.004) without missing a single malignancy.

Personalized respiratory medicine: exploring the horizon, addressing the issues. Summary of a BRN-AJRCCM workshop held in Barcelona on June 12, 2014

This Pulmonary Perspective summarizes the content and main conclusions of an international workshop on personalized respiratory medicine coorganized by the Barcelona Respiratory Network ( www.brn.cat ) and the AJRCCM in June 2014. It discusses (1) its definition and historical, social, legal, and ethical aspects; (2) the view from different disciplines, including basic science, epidemiology, bioinformatics, and network/systems medicine; (3) the bottlenecks and opportunities identified by some currently ongoing projects; and (4) the implications for the individual, the healthcare system and the pharmaceutical industry. The authors hope that, although it is not a systematic review on the subject, this document can be a useful reference for researchers, clinicians, healthcare managers, policy-makers, and industry parties interested in personalized respiratory medicine.

Framing of scientific knowledge as a new category of health care research

RATIONALE

The new area of health system research requires a revision of the taxonomy of scientific knowledge that may facilitate a better understanding and representation of complex health phenomena in research discovery, corroboration and implementation.

METHOD

A position paper by an expert group following and iterative approach.

RESULTS

'Scientific evidence' should be differentiated from 'elicited knowledge' of experts and users, and this latter typology should be described beyond the traditional qualitative framework. Within this context 'framing of scientific knowledge' (FSK) is defined as a group of studies of prior expert knowledge specifically aimed at generating formal scientific frames. To be distinguished from other unstructured frames, FSK must be explicit, standardized, based on the available evidence, agreed by a group of experts and subdued to the principles of commensurability, transparency for corroboration and transferability that characterize scientific research. A preliminary typology of scientific framing studies is presented. This typology includes, among others, health declarations, position papers, expert-based clinical guides, conceptual maps, classifications, expert-driven health atlases and expert-driven studies of costs and burden of illness.

CONCLUSIONS

This grouping of expert-based studies constitutes a different kind of scientific knowledge and should be clearly differentiated from 'evidence' gathered from experimental and observational studies in health system research.

Evaluation of an integrated system for classification, assessment and comparison of services for long-term care in Europe: the eDESDE-LTC study

Background

The harmonization of European health systems brings with it a need for tools to allow the standardized collection of information about medical care. A common coding system and standards for the description of services are needed to allow local data to be incorporated into evidence-informed policy, and to permit equity and mobility to be assessed. The aim of this project has been to design such a classification and a related tool for the coding of services for Long Term Care (DESDE-LTC), based on the European Service Mapping Schedule (ESMS).

Methods

The development of DESDE-LTC followed an iterative process using nominal groups in 6 European countries. 54 researchers and stakeholders in health and social services contributed to this process. In order to classify services, we use the minimal organization unit or “Basic Stable Input of Care” (BSIC), coded by its principal function or “Main Type of Care” (MTC). The evaluation of the tool included an analysis of feasibility, consistency, ontology, inter-rater reliability, Boolean Factor Analysis, and a preliminary impact analysis (screening, scoping and appraisal).

Results

DESDE-LTC includes an alpha-numerical coding system, a glossary and an assessment instrument for mapping and counting LTC. It shows high feasibility, consistency, inter-rater reliability and face, content and construct validity. DESDE-LTC is ontologically consistent. It is regarded by experts as useful and relevant for evidence-informed decision making.

Conclusion

DESDE-LTC contributes to establishing a common terminology, taxonomy and coding of LTC services in a European context, and a standard procedure for data collection and international comparison.

Background, Structure and Priorities of the 2013 Geneva Declaration on Person-centered Health Research

Declarations are relevant tools to frame new areas in health care, to raise awareness and to facilitate knowledge-to-action. The International College on Person Centered Medicine (ICPCM) is seeking to extend the impact of the ICPCM Conference Series by producing a declaration on every main topic. The aim of this paper is to describe the development of the 2013 Geneva Declaration on Person-centered Health Research and to provide additional information on the research priority areas identified during this iterative process. There is a need for more PCM research and for the incorporation of the PCM approach into general health research. Main areas of research focus include: Conceptual, terminological, and ontological issues; research to enhance the empirical evidence of PCM main components such as PCM informed clinical communication; PCM-based diagnostic models; person-centered care and interventions; and people-centered care, research on training and curriculum development. Dissemination and implementation of PCM knowledge-base is integral to Person-centered Health Research and shall engage currently available scientific and translational dissemination tools such journals, events and eHealth.

Identification and location of hot and cold spots of treated prevalence of depression in Catalonia (Spain)

Background

Spatial analysis is a relevant set of tools for studying the geographical distribution of diseases, although its methods and techniques for analysis may yield very different results. A new hybrid approach has been applied to the spatial analysis of treated prevalence of depression in Catalonia (Spain) according to the following descriptive hypotheses: 1) spatial clusters of treated prevalence of depression (hot and cold spots) exist and, 2) these clusters are related to the administrative divisions of mental health care (catchment areas) in this region.

Methods

In this ecological study, morbidity data per municipality have been extracted from the regional outpatient mental health database (CMBD-SMA) for the year 2009. The second level of analysis mapped small mental health catchment areas or groups of municipalities covered by a single mental health community centre. Spatial analysis has been performed using a Multi-Objective Evolutionary Algorithm (MOEA) which identified geographical clusters (hot spots and cold spots) of depression through the optimization of its treated prevalence. Catchment areas, where hot and cold spots are located, have been described by four domains: urbanicity, availability, accessibility and adequacy of provision of mental health care.

Results

MOEA has identified 6 hot spots and 4 cold spots of depression in Catalonia. Our results show a clear spatial pattern where one cold spot contributed to define the exact location, shape and borders of three hot spots. Analysing the corresponding domain values for the identified hot and cold spots no common pattern has been detected.

Conclusions

MOEA has effectively identified hot/cold spots of depression in Catalonia. However these hot/cold spots comprised municipalities from different catchment areas and we could not relate them to the administrative distribution of mental care in the region. By combining the analysis of hot/cold spots, a better statistical and operational-based visual representation of the geographical distribution is obtained. This technology may be incorporated into Decision Support Systems to enhance local evidence-informed policy in health system research.

Intellectual developmental disorders: towards a new name, definition and framework for "mental retardation/intellectual disability" in ICD-11

Although "intellectual disability" has widely replaced the term "mental retardation", the debate as to whether this entity should be conceptualized as a health condition or as a disability has intensified as the revision of the World Health Organization (WHO)'s International Classification of Diseases (ICD) advances. Defining intellectual disability as a health condition is central to retaining it in ICD, with significant implications for health policy and access to health services. This paper presents the consensus reached to date by the WHO ICD Working Group on the Classification of Intellectual Disabilities. Literature reviews were conducted and a mixed qualitative approach was followed in a series of meetings to produce consensus-based recommendations combining prior expert knowledge and available evidence. The Working Group proposes replacing mental retardation with intellectual developmental disorders, defined as "a group of developmental conditions characterized by significant impairment of cognitive functions, which are associated with limitations of learning, adaptive behaviour and skills". The Working Group further advises that intellectual developmental disorders be incorporated in the larger grouping (parent category) of neurodevelopmental disorders, that current subcategories based on clinical severity (i.e., mild, moderate, severe, profound) be continued, and that problem behaviours be removed from the core classification structure of intellectual developmental disorders and instead described as associated features.