Toward Sustainable Adoption of Integrated Care for Prevention of Unplanned Hospitalizations: A Qualitative Analysis.

Introduction: Complex chronic patients are prone to unplanned hospitalizations leading to a high burden on healthcare systems. To date, interventions to prevent unplanned admissions show inconclusive results. We report a qualitative analysis performed into the EU initiative JADECARE (2020-2023) to design a digitally enabled integrated care program aiming at preventing unplanned hospitalizations. Methods: A two-phase process with four design thinking (DT) sessions was conducted to analyse the management of complex chronic patients in the region of Catalonia (ES). In Phase I, Discovery, two DT sessions, October 2021 and February 2022, were done using as background information: i) the results of twenty structured interviews (five patients and fifteen professionals), ii) two governmental documents on regional deployment of integrated care and on the Catalan digital health strategy, respectively, and iii) the results of a cluster analysis of 761 hospitalizations. In Phase II, Confirmation, we examined the 30- and 90-day post-discharge periods of 49,604 hospitalizations as input for two additional DT sessions conducted in November and December 2022. Discussion: The qualitative analysis identified poor personalization of the interventions, the need for organizational changes, immature digitalization, and suboptimal services evaluation as main explanatory factors of the observed efficacy-effectiveness gap. Additionally, a program for prevention of unplanned hospitalizations, to be evaluated during the period 2024-2025, was generated. Conclusions: A digitally enabled adaptive case management approach to foster collaborative work and personalization of care, as well as organizational re-engineering, are endorsed for value-based prevention of unplanned hospitalizations.

Toward Adoption of Health Risk Assessment in Population-Based and Clinical Scenarios: Lessons From JADECARE.

Introduction: Health risk assessment (HRA) strategies are cornerstone for health systems transformation toward value-based patient-centred care. However, steps for HRA adoption are undefined. This article analyses the process of transference of the Adjusted Morbidity Groups (AMG) algorithm from the Catalan Good Practice to the Marche region (IT) and to Viljandi Hospital (EE), within the JADECARE initiative (2020-2023). Description: The implementation research approach involved a twelve-month pre-implementation period to assess feasibility and define the local action plans, followed by a sixteen-month implementation phase. During the two periods, a well-defined combination of experience-based co-design and quality improvement methodologies were applied. Discussion: The evolution of the Catalan HRA strategy (2010-2023) illustrates its potential for health systems transformation, as well as its transferability. The main barriers and facilitators for HRA adoption were identified. The report proposes a set of key steps to facilitate site customized deployment of HRA contributing to define a roadmap to foster large-scale adoption across Europe. Conclusions: Successful adoption of the AMG algorithm was achieved in the two sites confirming transferability. Marche identified the key requirements for a population-based HRA strategy, whereas Viljandi Hospital proved its potential for clinical use paving the way toward value-based healthcare strategies.

Multicentric Assessment of a Multimorbidity-Adjusted Disability Score to Stratify Depression-Related Risks Using Temporal Disease Maps: Instrument Validation Study.

BACKGROUND: Comprehensive management of multimorbidity can significantly benefit from advanced health risk assessment tools that facilitate value-based interventions, allowing for the assessment and prediction of disease progression. Our study proposes a novel methodology, the Multimorbidity-Adjusted Disability Score (MADS), which integrates disease trajectory methodologies with advanced techniques for assessing interdependencies among concurrent diseases. This approach is designed to better assess the clinical burden of clusters of interrelated diseases and enhance our ability to anticipate disease progression, thereby potentially informing targeted preventive care interventions. OBJECTIVE: This study aims to evaluate the effectiveness of the MADS in stratifying patients into clinically relevant risk groups based on their multimorbidity profiles, which accurately reflect their clinical complexity and the probabilities of developing new associated disease conditions. METHODS: In a retrospective multicentric cohort study, we developed the MADS by analyzing disease trajectories and applying Bayesian statistics to determine disease-disease probabilities combined with well-established disability weights. We used major depressive disorder (MDD) as a primary case study for this evaluation. We stratified patients into different risk levels corresponding to different percentiles of MADS distribution. We statistically assessed the association of MADS risk strata with mortality, health care resource use, and disease progression across 1 million individuals from Spain, the United Kingdom, and Finland. RESULTS: The results revealed significantly different distributions of the assessed outcomes across the MADS risk tiers, including mortality rates; primary care visits; specialized care outpatient consultations; visits in mental health specialized centers; emergency room visits; hospitalizations; pharmacological and nonpharmacological expenditures; and dispensation of antipsychotics, anxiolytics, sedatives, and antidepressants (P<.001 in all cases). Moreover, the results of the pairwise comparisons between adjacent risk tiers illustrate a substantial and gradual pattern of increased mortality rate, heightened health care use, increased health care expenditures, and a raised pharmacological burden as individuals progress from lower MADS risk tiers to higher-risk tiers. The analysis also revealed an augmented risk of multimorbidity progression within the high-risk groups, aligned with a higher incidence of new onsets of MDD-related diseases. CONCLUSIONS: The MADS seems to be a promising approach for predicting health risks associated with multimorbidity. It might complement current risk assessment state-of-the-art tools by providing valuable insights for tailored epidemiological impact analyses of clusters of interrelated diseases and by accurately assessing multimorbidity progression risks. This study paves the way for innovative digital developments to support advanced health risk assessment strategies. Further validation is required to generalize its use beyond the initial case study of MDD.

Retrospective analysis of hospitalization costs using two payment systems: the diagnosis related groups (DRG) and the Queralt system, a newly developed case-mix tool for hospitalized patients.

BACKGROUND: Hospital services are typically reimbursed using case-mix tools that group patients according to diagnoses and procedures. We recently developed a case-mix tool (i.e., the Queralt system) aimed at supporting clinicians in patient management. In this study, we compared the performance of a broadly used tool (i.e., the APR-DRG) with the Queralt system. METHODS: Retrospective analysis of all admissions occurred in any of the eight hospitals of the Catalan Institute of Health (i.e., approximately, 30% of all hospitalizations in Catalonia) during 2019. Costs were retrieved from a full cost accounting. Electronic health records were used to calculate the APR-DRG group and the Queralt index, and its different sub-indices for diagnoses (main diagnosis, comorbidities on admission, andcomplications occurred during hospital stay) and procedures (main and secondary procedures). The primary objective was the predictive capacity of the tools; we also investigated efficiency and within-group homogeneity. RESULTS: The analysis included 166,837 hospitalization episodes, with a mean cost of € 4,935 (median 2,616; interquartile range 1,011-5,543). The components of the Queralt system had higher efficiency (i.e., the percentage of costs and hospitalizations covered by increasing percentages of groups from each case-mix tool) and lower heterogeneity. The logistic model for predicting costs at pre-stablished thresholds (i.e., 80th, 90th, and 95th percentiles) showed better performance for the Queralt system, particularly when combining diagnoses and procedures (DP): the area under the receiver operating characteristics curve for the 80th, 90th, 95th cost percentiles were 0.904, 0.882, and 0.863 for the APR-DRG, and 0.958, 0.945, and 0.928 for the Queralt DP; the corresponding values of area under the precision-recall curve were 0.522, 0.604, and 0.699 for the APR-DRG, and 0.748, 0.7966, and 0.834 for the Queralt DP. Likewise, the linear model for predicting the actual cost fitted better in the case of the Queralt system. CONCLUSIONS: The Queralt system, originally developed to predict hospital outcomes, has good performance and efficiency for predicting hospitalization costs.

Impact of gene-by-trauma interaction in MDD-related multimorbidity clusters.

BACKGROUND: Major depressive disorder (MDD) is considerably heterogeneous in terms of comorbidities, which may hamper the disentanglement of its biological mechanism. In a previous study, we classified the lifetime trajectories of MDD-related multimorbidities into seven distinct clusters, each characterized by unique genetic and environmental risk-factor profiles. The current objective was to investigate genome-wide gene-by-environment (G × E) interactions with childhood trauma burden, within the context of these clusters. METHODS: We analyzed 77,519 participants and 6,266,189 single-nucleotide polymorphisms (SNPs) of the UK Biobank database. Childhood trauma burden was assessed using the Childhood Trauma Screener (CTS). For each cluster, Plink 2.0 was used to calculate SNP × CTS interaction effects on the participants' cluster membership probabilities. We especially focused on the effects of 31 candidate genes and associated SNPs selected from previous G × E studies for childhood maltreatment's association with depression. RESULTS: At SNP-level, only the high-multimorbidity Cluster 6 revealed a genome-wide significant SNP rs145772219. At gene-level, MPST and PRH2 were genome-wide significant for the low-multimorbidity Clusters 1 and 3, respectively. Regarding candidate SNPs for G × E interactions, individual SNP results could be replicated for specific clusters. The candidate genes CREB1, DBH, and MTHFR (Cluster 5) as well as TPH1 (Cluster 6) survived multiple testing correction. LIMITATIONS: CTS is a short retrospective self-reported measurement. Clusters could be influenced by genetics of individual disorders. CONCLUSIONS: The first G × E GWAS for MDD-related multimorbidity trajectories successfully replicated findings from previous G × E studies related to depression, and revealed risk clusters for the contribution of childhood trauma.

The value of admission avoidance: cost-consequence analysis of one-year activity in a consolidated service.

BACKGROUND: Many advantages of hospital at home (HaH), as a modality of acute care, have been highlighted, but controversies exist regarding the cost-benefit trade-offs. The objective is to assess health outcomes and analytical costs of hospital avoidance (HaH-HA) in a consolidated service with over ten years of delivery of HaH in Barcelona (Spain). METHODS: A retrospective cost-consequence analysis of all first episodes of HaH-HA, directly admitted from the emergency room (ER) in 2017-2018, was carried out with a health system perspective. HaH-HA was compared with a propensity-score-matched group of contemporary patients admitted to conventional hospitalization (Controls). Mortality, re-admissions, ER visits, and direct healthcare costs were evaluated. RESULTS: HaH-HA and Controls (n = 441 each) were comparable in terms of age (73 [SD16] vs. 74 [SD16]), gender (male, 57% vs. 59%), multimorbidity, healthcare expenditure during the previous year, case mix index of the acute episode, and main diagnosis at discharge. HaH-HA presented lower mortality during the episode (0 vs. 19 (4.3%); p < 0.001). At 30 days post-discharge, HaH-HA and Controls showed similar re-admission rates; however, ER visits were lower in HaH-HA than in Controls (28 (6.3%) vs. 34 (8.1%); p = 0.044). Average costs per patient during the episode were lower in the HaH-HA group (€ 1,078) than in Controls (€ 2,171). Likewise, healthcare costs within the 30 days post-discharge were also lower in HaH-Ha than in Controls (p < 0.001). CONCLUSIONS: The study showed higher performance and cost reductions of HaH-HA in a real-world setting. The identification of sources of savings facilitates scaling of hospital avoidance. REGISTRATION: ClinicalTrials.gov (26/04/2017; NCT03130283).

Five years of Hospital at Home adoption in Catalonia: impact, challenges, and proposals for quality assurance.

BACKGROUND: Hospital at home (HaH) was increasingly implemented in Catalonia (7.7 M citizens, Spain) achieving regional adoption within the 2011-2015 Health Plan. This study aimed to assess population-wide HaH outcomes over five years (2015-2019) in a consolidated regional program and provide context-independent recommendations for continuous quality improvement of the service. METHODS: A mixed-methods approach was adopted, combining population-based retrospective analyses of registry information with qualitative research. HaH (admission avoidance modality) was compared with a conventional hospitalization group using propensity score matching techniques. We evaluated the 12-month period before the admission, the hospitalization, and use of healthcare resources at 30 days after discharge. A panel of experts discussed the results and provided recommendations for monitoring HaH services. RESULTS: The adoption of HaH steadily increased from 5,185 episodes/year in 2015 to 8,086 episodes/year in 2019 (total episodes 31,901; mean age 73 (SD 17) years; 79% high-risk patients. Mortality rates were similar between HaH and conventional hospitalization within the episode [76 (0.31%) vs. 112 (0.45%)] and at 30-days after discharge [973(3.94%) vs. 1112(3.24%)]. Likewise, the rates of hospital re-admissions at 30 days after discharge were also similar between groups: 2,00 (8.08%) vs. 1,63 (6.58%)] or ER visits [4,11 (16.62%) vs. 3,97 (16.03%). The 27 hospitals assessed showed high variability in patients' age, multimorbidity, severity of episodes, recurrences, and length of stay of HaH episodes. Recommendations aiming at enhancing service delivery were produced. CONCLUSIONS: Besides confirming safety and value generation of HaH for selected patients, we found that this service is delivered in a case-mix of different scenarios, encouraging hospital-profiled monitoring of the service.

A Practice-Proven Adaptive Case Management Approach for Innovative Health Care Services (Health Circuit): Cluster Randomized Clinical Pilot and Descriptive Observational Study.

BACKGROUND: Digital health tools may facilitate the continuity of care. Enhancement of digital aid is imperative to prevent information gaps or redundancies, as well as to facilitate support of flexible care plans. OBJECTIVE: The study presents Health Circuit, an adaptive case management approach that empowers health care professionals and patients to implement personalized evidence-based interventions, thanks to dynamic communication channels and patient-centered service workflows; analyze the health care impact; and determine its usability and acceptability among health care professionals and patients. METHODS: From September 2019 to March 2020, the health impact, usability (measured with the system usability scale; SUS), and acceptability (measured with the net promoter score; NPS) of an initial prototype of Health Circuit were tested in a cluster randomized clinical pilot (n=100) in patients with high risk for hospitalization (study 1). From July 2020 to July 2021, a premarket pilot study of usability (with the SUS) and acceptability (with the NPS) was conducted among 104 high-risk patients undergoing prehabilitation before major surgery (study 2). RESULTS: In study 1, Health Circuit resulted in a reduction of emergency room visits (4/7, 13% vs 7/16, 44%), enhanced patients' empowerment (P<.001) and showed good acceptability and usability scores (NPS: 31; SUS: 54/100). In study 2, the NPS was 40 and the SUS was 85/100. The acceptance rate was also high (mean score of 8.4/10). CONCLUSIONS: Health Circuit showed potential for health care value generation and good acceptability and usability despite being a prototype system, prompting the need for testing a completed system in real-world scenarios. TRIAL REGISTRATION: ClinicalTrials.gov NCT04056663; https://clinicaltrials.gov/ct2/show/NCT04056663.

Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study.

BACKGROUND: Enhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection. OBJECTIVE: The study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients' profiles for personalized service selection purposes. METHODS: Gradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles. RESULTS: Performance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients' profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%). CONCLUSIONS: The results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation.

The Assessment of Medical Device Software Supporting Health Care Services for Chronic Patients in a Tertiary Hospital: Overarching Study.

BACKGROUND: Innovative digital health tools are increasingly being evaluated and, in some instances, integrated at scale into health systems. However, the applicability of assessment methodologies in real-life scenarios to demonstrate value generation and consequently foster sustainable adoption of digitally enabled health interventions has some bottlenecks. OBJECTIVE: We aimed to build on the process of premarket assessment of 4 digital health interventions piloted at the Hospital Clinic de Barcelona (HCB), as well as on the analysis of current medical device software regulations and postmarket surveillance in the European Union and United States in order to generate recommendations and lessons learnt for the sustainable adoption of digitally enabled health interventions. METHODS: Four digital health interventions involving prototypes were piloted at the HCB (studies 1-4). Cocreation and quality improvement methodologies were used to consolidate a pragmatic evaluation method to assess the perceived usability and satisfaction of end users (both patients and health care professionals) by means of the System Usability Scale and the Net Promoter Score, including general questions about satisfaction. Analyses of both medical software device regulations and postmarket surveillance in the European Union and United States (2017-2021) were performed. Finally, an overarching analysis on lessons learnt was conducted considering 4 domains (technical, clinical, usability, and cost), as well as differentiating among 3 different eHealth strategies (telehealth, integrated care, and digital therapeutics). RESULTS: Among the participant stakeholders, the System Usability Scale score was consistently higher in patients (studies 1, 2, 3, and 4: 78, 67, 56, and 76, respectively) than in health professionals (studies 2, 3, and 4: 52, 43, and 54, respectively). In general, use of the supporting digital health tools was recommended more by patients (studies 1, 2, 3, and 4: Net Promoter Scores of -3%, 31%, -21%, and 31%, respectively) than by professionals (studies 2, 3, and 4: Net Promoter Scores of -67%, 1%, and -80%, respectively). The overarching analysis resulted in pragmatic recommendations for the digital health evaluation domains and the eHealth strategies considered. CONCLUSIONS: Lessons learnt on the digitalization of health resulted in practical recommendations that could contribute to future deployment experiences.

Actionable Factors Fostering Health Value Generation and Scalability of Prehabilitation: A Prospective Cohort Study.

IMPORTANCE: Prehabilitation has potential for improving surgical outcomes as shown in previous randomized controlled trials. However, a marked efficacy-effectiveness gap is limiting its scalability. Comprehensive analyses of deployment of the intervention in real-life scenarios are required. OBJECTIVE: To assess health outcomes and cost of prehabilitation. DESIGN: Prospective cohort study with a control group built using propensity score-matching techniques. SETTING: Prehabilitation Unit in a tertiary-care university hospital. PARTICIPANTS: Candidates for major digestive, cardiac, thoracic, gynecologic, or urologic surgeries. INTERVENTION: Prehabilitation program, including supervised exercise training, promotion of physical activity, nutritional optimization, and psychological support. MAIN OUTCOMES AND MEASURES: The comprehensive complication index, hospital and intensive care unit length of stay, and hospital costs per patient until 30 days after surgery. Patients were classified by the degree of program completion and level of surgical aggression for sensitivity analysis. RESULTS: The analysis of the entire study group did not show differences in study outcomes between prehabilitation and control groups (n=328 each). The per-protocol analysis, including only patients completing the program (n=112, 34%), showed a reduction in mean hospital stay [9.9 (7.2) vs 12.8 (12.4) days; P =0.035]. Completers undergoing highly aggressive surgeries (n=60) additionally showed reduction in mean intensive care unit stay [2.3 (2.7) vs 3.8 (4.2) days; P =0.021] and generated mean cost savings per patient of €3092 (32% cost reduction) ( P =0.007). Five priority areas for action to enhance service efficiencies were identified. CONCLUSIONS AND RELEVANCE: The study indicates a low rate of completion of the intervention and identifies priority areas for re-design of service delivery to enhance the effectiveness of prehabilitation.

A sound mind in a sound body: a novel concept unravelling heterogeneity of depression.

Depression is a highly prevalent and debilitating condition, yet we still lack both in-depth knowledge concerning its etiopathology and sufficiently efficacious treatment options. With approximately one third of patients resistant to currently available antidepressants there is a pressing need for a better understanding of depression, identifying subgroups within the highly heterogeneous illness category and to understand the divergent underlying biology of such subtypes, to help develop and personalise treatments. The TRAJECTOME project aims to address such challenges by (1) identifying depression-related multimorbidity subgroups and shared molecular pathways based on temporal disease profiles from healthcare systems and biobank data using machine learning approaches, and by (2) characterising these subgroups from multiple aspects including genetic variants, metabolic processes, lifestyle and environmental factors. Following the identification of multimorbidity trajectories, a disease burden score related to depression and adjusted for multimorbidity was established summarising the current state of the patient to weigh the molecular mechanisms associated with depression. In addition, the role of genetic and environmental factors, and also their interactions were identified for all subgroups. The project also attempted to identify potential metabolomic markers for the early diagnostics of these multimorbidity conditions. Finally, we prioritized molecular drug candidates matching the multimorbidity pathways indicated for the individual subgroups which would potentially offer personalised treatment simultaneously for the observable multimorbid conditions yet minimising polypharmacy and related side effects. The present paper overviews the TRAJECTOME project including its aims, tasks, procedures and accomplishments. (Neuropsychopharmacol Hung 2023; 25(4): 183-193)

Role of Co-creation for Large-Scale Sustainable Adoption of Digitally Supported Integrated Care: Prehabilitation as Use Case.

Introduction: The efficacy-effectiveness gap constitutes a well-known limitation for adoption of digitally enabled integrated care services. The current report describes the co-creation process undertaken (2016-2021) to deploy a prehabilitation service at Hospital Clínic de Barcelona with the final aim of achieving sustainable adoption and facilitate site transferability. Methods: An implementation research approach with a population-based orientation, combining experience-based co-design and quality improvement methodologies, was applied. We undertook several design-thinking sessions (Oct-Nov 2017, June 2021 and December 2021) to generate and follow-up a work plan fostering service scalability. The implementation process was assessed using the Comprehensive Framework for Implementation Research, leading to the identification of key performance indicators. Discussion: Personalization and modularity of the intervention according to patients' surgical risk were identified as core traits to enhance patients' adherence and value generation. A digitally enabled service workflow, with an adaptive and collaborative case management approach, should combine face-to-face and remotely supervised sessions with intelligent systems for patients' and professionals' decision support. The business model envisages operational costs financed by savings generated by the service. Conclusions: Evidence-based co-creation, combining appropriate methodologies and a structured evaluation framework, was key to address challenges associated with sustainable prehabilitation service adoption, scalability and transferability.

Prospective cohort study for assessment of integrated care with a triple aim approach: hospital at home as use case.

BACKGROUND: Applicability of comprehensive assessment of integrated care services in real world settings is an unmet need. To this end, a Triple Aim evaluation of Hospital at Home (HaH), as use case, was done. As ancillary aim, we explored use of the approach for monitoring the impact of adoption of integrated care at health system level in Catalonia (Spain). METHODS: Prospective cohort study over one year period, 2017-2018, comparing hospital avoidance (HaH-HA) with conventional hospitalization (UC) using propensity score matching. Participants were after the first episode directly admitted to HaH-HA or the corresponding control group. Triple Aim assessment using multiple criteria decision analysis (MCDA) was done. Moreover, applicability of a Triple Aim approach at health system level was explored using registry data. RESULTS: HaH-HA depicted lower: i) Emergency Room Department (ER) visits (p < .001), ii) Unplanned re-admissions (p = .012); and iii) costs (p < .001) than UC. The weighted aggregation of the standardized values of each of the eight outcomes, weighted by the opinions of the stakeholder groups considered in the MCDA: i) enjoyment of life; ii) resilience; iii) physical functioning; iv) continuity of care; v) psychological wellbeing; (vi) social relationships & participation; (vii) person-centeredness; and (viii) costs, indicated better performance of HaH-HA than UC (p < .05). Actionable factors for Triple Aim assessment of the health system with a population-health approach were identified. CONCLUSIONS: We confirmed health value generation of HaH-HA. The study identified actionable factors to enhance applicability of Triple Aim assessment at health system level for monitoring the impact of adoption of integrated care. REGISTRATION: ClinicalTrials.gov (26/04/2017; NCT03130283).

Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment.

BACKGROUND: Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model. METHODS: The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment. RESULTS: Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively. CONCLUSIONS: The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates.

Remote Healthcare for Elderly People Using Wearables: A Review.

The growth of health care spending on older adults with chronic diseases faces major concerns that require effective measures to be adopted worldwide. Among the main concerns is whether recent technological advances now offer the possibility of providing remote health care for the aging population. The benefits of suitable prevention and adequate monitoring of chronic diseases by using emerging technological paradigms such as wearable devices and the Internet of Things (IoT) can increase the detection rates of health risks to raise the quality of life for the elderly. Specifically, on the subject of remote health monitoring in older adults, a first approach is required to review devices, sensors, and wearables that serve as tools for obtaining and measuring physiological parameters in order to identify progress, limitations, and areas of opportunity in the development of health monitoring schemes. For these reasons, a review of articles on wearable devices was presented in the first instance to identify whether the selected articles addressed the needs of aged adults. Subsequently, the direct review of commercial and prototype wearable devices with the capability to read physiological parameters was presented to identify whether they are optimal or usable for health monitoring in older adults.

Digital Support to Multimodal Community-Based Prehabilitation: Looking for Optimization of Health Value Generation.

Prehabilitation has shown its potential for most intra-cavity surgery patients on enhancing preoperative functional capacity and postoperative outcomes. However, its large-scale implementation is limited by several constrictions, such as: i) unsolved practicalities of the service workflow, ii) challenges associated to change management in collaborative care; iii) insufficient access to prehabilitation; iv) relevant percentage of program drop-outs; v) need for program personalization; and, vi) economical sustainability. Transferability of prehabilitation programs from the hospital setting to the community would potentially provide a new scenario with greater accessibility, as well as offer an opportunity to effectively address the aforementioned issues and, thus, optimize healthcare value generation. A core aspect to take into account for an optimal management of prehabilitation programs is to use proper technological tools enabling: i) customizable and interoperable integrated care pathways facilitating personalization of the service and effective engagement among stakeholders; ii) remote monitoring (i.e. physical activity, physiological signs and patient-reported outcomes and experience measures) to support patient adherence to the program and empowerment for self-management; and, iii) use of health risk assessment supporting decision making for personalized service selection. The current manuscript details a proposal to bring digital innovation to community-based prehabilitation programs. Moreover, this approach has the potential to be adopted by programs supporting long-term management of cancer patients, chronic patients and prevention of multimorbidity in subjects at risk.

Metabolomics: The Stethoscope for the Twenty-First Century.

Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.

Application of Dynamic Fragmentation Methods in Multimedia Databases: A Review.

Fragmentation is a design technique widely used in multimedia databases, because it produces substantial benefits in reducing response times, causing lower execution costs in each operation performed. Multimedia databases include data whose main characteristic is their large size, therefore, database administrators face a challenge of great importance, since they must contemplate the different qualities of non-trivial data. These databases over time undergo changes in their access patterns. Different fragmentation techniques presented in related studies show adequate workflows, however, some do not contemplate changes in access patterns. This paper aims to provide an in-depth review of the literature related to dynamic fragmentation of multimedia databases, to identify the main challenges, technologies employed, types of fragmentation used, and characteristics of the cost model. This review provides valuable information for database administrators by showing essential characteristics to perform proper fragmentation and to improve the performance of fragmentation schemes. The reduction of costs in fragmentation methods is one of the most desired main properties. To fulfill this objective, the works include cost models, covering different qualities. In this analysis, a set of characteristics used in the cost models of each work is presented to facilitate the creation of a new cost model including the most used qualities. In addition, different data sets or reference points used in the testing stage of each work analyzed are presented.