Unlocking Continuous Improvement in Heart Failure Remote Monitoring: A Participatory Approach to Unveil Value Dimensions and Performance Indicators.

Introduction: Heart failure (HF) constitutes a public health concern affecting quality of life, survival, and costs. Remote patient monitoring (RPM) can enhance HF management, involving patients actively and improving follow-up. While current HF RPM assessments emphasise cost-effectiveness analysis, there is a need to consider wider RPM impacts and integrate stakeholders' perspectives into assessments for better comprehensiveness. Methods: We developed a four-stage participatory approach to select value dimensions and indicators for continuous HF RPM assessment: Stage 1 involved building a literature-informed initial list; Stage 2 utilized expert interviews for validation and list expansion; Stage 3 involved a web-Delphi process with Portuguese stakeholders and experts for agreement assessment; and Stage 4 included a conclusive expert interview. Results: A literature review identified fourteen studies on telehealth, RPM, and HF, informing an initial list of four value dimensions (Access, Clinical aspects, Acceptability, and Costs) and 22 indicators. Seven semistructured interviews validated and further adjusted the list to 38 indicators. Subsequently, the web-Delphi process engaged 29 stakeholders, giving their opinions regarding assessment aspects' relevance and proposing additional elements - 1 dimension and 12 indicators. Five value dimensions and 38 indicators (76.0%) reached group agreement for selection, while 12 did not reach an agreement. Upon expert appreciation, 5 dimensions, 43 indicators, and 6 case-mix parameters were considered relevant. Discussion: This comprehensive social approach captured diverse stakeholder perspectives, achieving agreement on pertinent HF RPM monitoring and evaluation indicators. Findings can inform visualization and management tool development, aiding day-to-day RPM evaluation and identification of improvement opportunities.

Towards A Framework for Implementing Remote Patient Monitoring From an Integrated Care Perspective: A Scoping Review.

BACKGROUND: Remote patient monitoring (RPM) has been increasingly adopted over the last decade, with the COVID-19 pandemic fostering its rapid development. As RPM implementation is recognised as complex and highly demanding in terms of resources and processes, there are multiple challenges in providing RPM in an integrated logic. METHODS: To examine the structural elements that are relevant for implementing RPM integrated care, a scoping review was conducted in PubMed, Scopus, and Web of Science, leveraging a search strategy that combines terms relative to (1) conceptual models and reallife initiatives; (2) RPM; and (3) care integration. RESULTS: 28 articles were included, covering nine conceptual models and 19 real-life initiatives. Eighteen structural elements of RPM integrated care implementation were identified among conceptual models, defining a structure for assessing real-life initiatives. 78.9% of those initiatives referred to at least ten structural elements, with patient education and self-monitoring promotion, multidisciplinary core workforce, ICTs (information and communications technologies) and telemonitoring devices, and health indicators measurement being present in all studies, and therefore being core elements to the design of RPM initiatives. CONCLUSION: RPM goes far beyond technology, with underlying processes and involved actors playing a central role in care provision. The structural elements identified can guide RPM implementation and promote maturity in adoption. Future research may focus on assessing design completeness, evaluating impacts, and analysing related financial arrangements.

Telemonitoring in Portugal: where do we stand and which way forward?

Following the pandemic, there is growing pressure in Portugal to adopt new practices that promote more efficient, sustainable, and equitable healthcare. Telemonitoring (TM) has been identified as a valuable solution, particularly for chronically ill, long-term or socially isolated patients. Several initiatives have since emerged. Thus, Portuguese stakeholders recognise the need to reflect upon TM's current state and prospects. This study aims to provide a comprehensive analysis of the TM landscape in Portugal. We begin by analysing the underlying conditions for telehealth development. Then, we describe the governmental strategy and priorities towards TM - the National Strategic Plan for Telehealth development and NHS reimbursement opportunities for TM. To understand TM implementation, adoption, and dissemination in Portugal, we analyse 46 reported initiatives and adoption studies focusing on providers' perspectives. Finally, a structured reflection on current challenges and the way forward is provided, according to the seven domains of the Nonadoption, Abandonment, and challenges to the Scale-up, Spread, and Sustainability (NASSS) framework. The adoption of TM by Portuguese institutions has been growing, leveraged by the telehealth governance model and public reimbursement mechanisms, proving particularly relevant during the pandemic. However, monitored patients are still few. Low digital literacy among patients and providers, lack of care integration and resource scarcity represent barriers hampering pilot TM initiatives' scale-up.

Leveraging electronic health record data to inform hospital resource management : A systematic data mining approach.

Early identification of resource needs is instrumental in promoting efficient hospital resource management. Hospital information systems, and electronic health records (EHR) in particular, collect valuable demographic and clinical patient data from the moment patients are admitted, which can help predict expected resource needs in early stages of patient episodes. To this end, this article proposes a data mining methodology to systematically obtain predictions for relevant managerial variables by leveraging structured EHR data. Specifically, these managerial variables are: i) Diagnosis categories, ii) procedure codes, iii) diagnosis-related groups (DRGs), iv) outlier episodes and v) length of stay (LOS). The proposed methodology approaches the problem in four stages: Feature set construction, feature selection, prediction model development, and model performance evaluation. We tested this approach with an EHR dataset of 5,089 inpatient episodes and compared different classification and regression models (for categorical and continuous variables, respectively), performed temporal analysis of model performance, analyzed the impact of training set homogeneity on performance and assessed the contribution of different EHR data elements for model predictive power. Overall, our results indicate that inpatient EHR data can effectively be leveraged to inform resource management on multiple perspectives. Logistic regression (combined with minimal redundancy maximum relevance feature selection) and bagged decision trees yielded best results for predicting categorical and numerical managerial variables, respectively. Furthermore, our temporal analysis indicated that, while DRG classes are more difficult to predict, several diagnosis categories, procedure codes and LOS amongst shorter-stay patients can be predicted with higher confidence in early stages of patient stay. Lastly, value of information analysis indicated that diagnoses, medication and structured assessment forms were the most valuable EHR data elements in predicting managerial variables of interest through a data mining approach.

Environmental determinants of population health in urban settings. A systematic review.

BACKGROUND: Population health is influenced by interactions between environmental determinants, which are captured by dimensions and indicators. This study aims to systematically review key environmental determinants and respective dimensions and indicators, relevant to evaluate population health in urban settings, and to understand their potential implications into policies. METHODS: A search of literature published between 2008 and 2018 was conducted in PubMed, Web of Science, Scopus and SciELO Portugal databases, on studies with evidence on association between an environmental determinant and a health outcome in urban contexts. Health determinants, dimensions and indicators researched in the selected studies were synthetized, and associations analyzed. An independent assessment of quality of the studies was performed. Key conclusions and policy recommendations were extracted to build a framework to analyze environment related population health and policies in urban settings. RESULTS: Ninety four studies of varied methodological approaches and quality met the inclusion criteria. The review identified positive associations between all environmental determinants -socioeconomic, built environment, natural environment, healthcare, behaviors, and health outcomes - overall mortality and morbidity, in urban settings. Improvements in income, education, air quality, occupation status, mobility and smoking habits indicators have positive impact in overall mortality and chronic diseases morbidity indicators. Initiatives to improve population health in which policymakers can be more evidence-informed include socioeconomic, natural environment and built environment determinants. CONCLUSIONS: There is scope and need to further explore which environmental determinants and dimensions most contribute to population health to create a series of robust evidence-based measures to better inform urban planning policies.

Can structured EHR data support clinical coding? A data mining approach.

Structured data formats are gaining momentum in electronic health records and can be leveraged for decision support and research. Nevertheless, such structured data formats have not been explored for clinical coding, which is an essential process requiring significant manual workload in health organisations. This article explores the extent to which fully structured clinical data can support assignment of clinical codes to inpatient episodes, through a methodology that tackles high dimensionality issues, addresses the multi-label nature of coding and optimises model parameters. The methodology encompasses transformation of raw data to define a feature set, build a data matrix representation, and testing combinations of feature selection methods with machine learning models to predict code assignment. The methodology was tested with a real hospital dataset and showed varying predictive power across codes, while demonstrating the potential of leveraging structuring data to reduce workload and increase efficiency in clinical coding.

Valuing health states: is the MACBETH approach useful for valuing EQ-5D-3L health states?

BACKGROUND: Quality Adjusted Life Years (QALYs) are a key outcome measure widely used within health technology assessment and health service research studies. QALYs combine quantity and quality of life, with quality of life calculations relying on the value of distinct health states. Such health states' values capture the preferences of a population and have been typically built through numerical elicitation methods. Evidence points to these value scores being influenced by methods in use and individuals reporting cognitive difficulties in eliciting their preferences. Evidence from other areas has further suggested that individuals may prefer using distinct elicitation techniques and that this preference can be influenced by their numeracy. In this study we explore the use of the MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) non-numerical preference elicitation approach for health states' evaluation. METHODS: A new protocol for preference elicitation based on MACBETH (only requiring qualitative judgments) was developed and tested within a web survey format. A sample of the Portuguese general population (n=243) valued 25 EQ-5D-3L health states with the MACBETH protocol and with a variant of the time trade-off (TTO) protocol, for comparison purposes and for understanding respondents' preference for distinct protocols and differences in inconsistent evaluations. Respondents answered to a short numeracy test, and basic socio-economic information collected. RESULTS: Results show that the mean values derived from MACBETH and the TTO variant are strongly correlated; however, there are substantial differences for several health states' values. Large and similar numbers of logical inconsistencies were found in respondents' answers with both methods. Participants with higher levels of numeracy according to the test preferred expressing value judgments with MACBETH, while participants with lower levels were mostly indifferent to both methods. Higher correlations between MACBETH and TTO variant evaluations were observed for individuals with higher numeracy. CONCLUSION: Results suggest that it is worth researching the use of non-numerical preference elicitation methods. Numeracy tests more appropriate for preference elicitation when no explicit considerations of uncertainty are made need to be explored and used. Further behavioural research is needed to fully understand the potential for using these methods in distinct settings (e.g. in different evaluation contexts and in face-to-face and non-face-to-face environments), as well as to explore the effect of literacy on assessments and on respondents' preferences.

Preprocessing structured clinical data for predictive modeling and decision support. A roadmap to tackle the challenges.

BACKGROUND: EHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance for researchers on how to build this matrix while avoiding potential pitfalls. OBJECTIVES: This article aims to provide researchers a roadmap of the main technical challenges of preprocessing structured EHR data and possible strategies to overcome them. METHODS: Along standard data processing stages - extracting database entries, defining features, processing data, assessing feature values and integrating data elements, within an EDPAI framework -, we identified the main challenges faced by researchers and reflect on how to address those challenges based on lessons learned from our research experience and on best practices from related literature. We highlight the main potential sources of error, present strategies to approach those challenges and discuss implications of these strategies. RESULTS: Following the EDPAI framework, researchers face five key challenges: (1) gathering and integrating data, (2) identifying and handling different feature types, (3) combining features to handle redundancy and granularity, (4) addressing data missingness, and (5) handling multiple feature values. Strategies to address these challenges include: cross-checking identifiers for robust data retrieval and integration; applying clinical knowledge in identifying feature types, in addressing redundancy and granularity, and in accommodating multiple feature values; and investigating missing patterns adequately. CONCLUSIONS: This article contributes to literature by providing a roadmap to inform structured EHR data preprocessing. It may advise researchers on potential pitfalls and implications of methodological decisions in handling structured data, so as to avoid biases and help realize the benefits of the secondary use of EHR data.

Modeling the demand for long-term care services under uncertain information.

Developing a network of long-term care (LTC) services is currently a health policy priority in many countries, in particular in countries with a health system based on a National Health Service (NHS) structure. Developing such a network requires proper planning and basic information on future demand and utilization of LTC services. Unfortunately, this information is often not available and the development of methods to properly predict demand is therefore essential. The current study proposes a simulation model based on a Markov cycle tree structure to predict annual demand for LTC services so as to inform the planning of these services at the small-area level in the coming years. The simulation model is multiservice, as it allows for predicting the annual number of individuals in need of each type of LTC service (formal and informal home-based, ambulatory and institutional services), the resources/services that are required to satisfy those needs (informal caregivers, domiciliary visits, consultations and beds) and the associated costs. The model developed was validated using past data and key international figures and applied to Portugal at the Lisbon borough level for the 2010-2015 period. Given data imperfections and uncertainties related to predicting future LTC demand, uncertainty was modeled through an integrated approach that combines scenario analysis with probabilistic sensitivity analysis using Monte Carlo simulation. Results show that the model provides information critical for informing the planning and financing of LTC networks.

Networks of primary and secondary care services: how to organise services so as to promote efficiency and quality in access while reducing costs.

Governments in countries with national health systems have been concerned with how to organise services so as to achieve improvements in efficiency and quality in healthcare delivery, as well as to control costs. In this study, a stochastic discrete event simulation model to study the organisation of primary and secondary care services is proposed. The model was built with reference to the context of the Portuguese NHS, was implemented in the Simul8 software program and was applied to the Portuguese Setúbal healthcare subregion (SHCR). For its application, a database with 2005 production, resource and cost indicators was built to calibrate and validate the applied model. After validation, three different policy scenarios were tested: the first one concerning a 10% increase in demand for primary care services; the second considering a shift between specialists and generalist physicians; and a third regarding restructuring of primary care services. Results show that although the current system is not prepared to cope with a rise in demand, the other scenarios indicate that there is room for primary care reforms to increase the system's efficiency and accessibility, while lowering total costs.

Modelling the redistribution of hospital supply to achieve equity taking account of patient's behaviour.

Policies that seek to achieve geographic equity in countries with a National Health Services (NHS) require information on how to change the distribution of supply to achieve greater equity in access and utilisation. Previous methods for analysing the impact of hospital changes have relied on crude assumptions on patients' behaviour in using hospitals. The approach developed in this study is a multi-modelling one based on two mathematical programming location-allocation models to redistribute hospital supply using different objective functions and assumptions about the utilisation behaviour of patients. These models show how different policy objectives seeking equity of geographic access or utilisation produce different results and imply trade-offs in terms of reduction in total utilisation.

Health care reform in Portugal: an evaluation of the NHS experience.

Since 1979, the Portuguese health care system has been based on a National Health Service structure that is expected to promote equity, efficiency, quality, accountability and the devolution of power. In this article, we analyse the content and impact of policies designed to reform the system between 1979 and 2002. This article differs from previous studies in that it uses a stage-based framework to evaluate the policy-making process and the impact of health care reform throughout different political cycles. We show that the NHS model has never been fully implemented and that many policies have diverted the system from its original objectives. Different governments have endorsed a progressive split between financing and provision and the institution of 'new public management' rules in public providers. We conclude that most policies put forward by Social Democratic governments have aimed at influencing demand, while Socialist governments have targeted the supply side. These policies have led to increases in health expenditure that have been comparatively more cost-shared by the State under Socialist governments. We show some overriding trends, namely as follows: despite huge improvements in health outcomes, the system is nonetheless lasting to meet its goals, particularly in terms of the equity of access and utilisation; accountability problems, inadequacies in the use of operational reforming tools (such as resource allocation mechanisms) and a lack of mechanisms to promote efficient behaviour, are all associated with cost containment problems. Structural reforms have been undertaken since 2002 and these have offered some potential for improving accountability and efficiency. Nonetheless, the success of these reforms calls for certain conditions that do not seem to have been fulfilled.

Measuring geographic inequities in the Portuguese health care system: an estimation of hospital care needs.

Portugal created a NHS to achieve greater equity of access to health care. Successive governments continued to assert the importance of equity in the face of evidence of inequities in supply of hospital resources, but lacked methods to provide sound information on the degree of inequities in Portugal and hence how to achieve greater equity. Capitation formulae have been increasingly used in other countries with a NHS to measure geographical inequities and allocate resources to reduce them. The main objective of this paper was to develop a capitation formula to measure need for hospital care for the Portuguese system by transferring this technology from methods used in other countries, and, in particular, in England. We find, however, problems with the common use of standardised mortality ratios (SMRs) as a measure of need and found age-specific mortality ratios to offer more soundly-based estimates. We also raise questions on the use of empirical estimates of utilisation of health care by age and sex as they appear to reflect inadequacies of health care in Portugal. We also believe it is important to improve knowledge of health insurance and care outside the NHS. Our results show that there are considerable inequities on the distribution of hospital resources in Portugal.