Health information technology to improve care for people with multiple chronic conditions

Empowering Nurse Health Education: Linguistic and Cultural Validation of the Nurse Health Education Competence Instrument (NHECI) in the Italian Context

BACKGROUND

Nurses worldwide are acknowledged for their role in health education across various settings. However, doubts often arise regarding their competence in this domain. This study aims to validate the Nurse Health Education Competence Instrument (NHECI) linguistically and culturally in the Italian context.

METHODS

Following Beaton et al.'s (2000) guidelines, we conducted cross-cultural adaptation to develop the Italian version of the questionnaire.

RESULTS

The Italian version demonstrates a good internal consistency and stability, making it suitable for assessing nursing students during clinical internships and practicing nurses. The availability of Italian tools promotes healthcare research, ensuring patient-centric care.

CONCLUSIONS

The validity and reliability of the Italian version of the instrument for assessing health education competencies, essential for self-assessment among health education nurses, are established.

Preferences of people with chronic kidney disease regarding digital health interventions that promote healthy lifestyle: qualitative systematic review with meta-ethnography

OBJECTIVES

Diet and physical activity are crucial for people with chronic kidney disease (CKD) to maintain good health. Digital health interventions can increase access to lifestyle services. However, consumers' perspectives are unclear, which may reduce the capacity to develop interventions that align with specific needs and preferences. Therefore, this review aims to synthesise the preferences of people with CKD regarding digital health interventions that promote healthy lifestyle.

DESIGN

Qualitative systematic review with meta-ethnography.

DATA SOURCES

Databases Scopus, CENTRAL, MEDLINE, CINAHL and SPORTDiscus were searched between 2000 and 2023.

ELIGIBILITY CRITERIA

Primary research papers that used qualitative exploration methods to explore the preferences of adults with CKD (≥18 years) regarding digital health interventions that promoted diet, physical activity or a combination of these health behaviours.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers screened title, abstract and full text. Discrepancies were resolved by a third reviewer. Consumers' quotes were extracted verbatim and synthesised into higher-order themes and subthemes.

RESULTS

Database search yielded 5761 records. One record was identified following communication with a primary author. 15 papers were included. These papers comprised 197 consumers (mean age 51.0±7.2), including 83 people with CKD 1-5; 61 kidney transplant recipients; 53 people on dialysis. Sex was reported in 182 people, including 53% male. Five themes were generated regarding consumers' preferences for digital lifestyle interventions. These included simple instruction and engaging design; individualised interventions; virtual communities of care; education and action plans; and timely reminders and automated behavioural monitoring.

CONCLUSION

Digital health interventions were considered an important mechanism to access lifestyle services. Consumers' preferences are important to ensure future interventions are tailored to specific needs and goals. Future research may consider applying the conceptual framework of consumers' preferences in this review to develop and evaluate the effect of a digital lifestyle intervention on health outcomes.

PROSPERO REGISTRATION NUMBER

CRD42023411511.

Chronic Disease Patterns and Their Relationship With Health-Related Quality of Life in South Korean Older Adults With the 2021 Korean National Health and Nutrition Examination Survey: Latent Class Analysis

BACKGROUND

Improved life expectancy has increased the prevalence of older adults living with multimorbidities, which likely deteriorates their health-related quality of life (HRQoL). Understanding which chronic conditions frequently co-occur can facilitate person-centered care tailored to the needs of individuals with specific multimorbidity profiles.

OBJECTIVE

The study objectives were to (1) examine the prevalence of multimorbidity among Korean older adults (ie, those aged 65 years and older), (2) investigate chronic disease patterns using latent class analysis, and (3) assess which chronic disease patterns are more strongly associated with HRQoL.

METHODS

A sample of 1806 individuals aged 65 years and older from the 2021 Korean National Health and Nutrition Examination Survey was analyzed. Latent class analysis was conducted to identify the clustering pattern of chronic diseases. HRQoL was assessed by an 8-item health-related quality of life scale (HINT-8). Multiple linear regression was used to analyze the association with the total score of the HINT-8. Logistic regression analysis was performed to evaluate the odds ratio of having problems according to the HINT-8 items.

RESULTS

The prevalence of multimorbidity in the sample was 54.8%. Three chronic disease patterns were identified: relatively healthy, cardiometabolic condition, arthritis, allergy, or asthma. The total scores of the HINT-8 were the highest in participants characterized as arthritis, allergy, or asthma group, indicating the lowest quality of life.

CONCLUSIONS

Current health care models are disease-oriented, meaning that the management of chronic conditions applies to a single condition and may not be relevant to those with multimorbidities. Identifying chronic disease patterns and their impact on overall health and well-being is critical for guiding integrated care.

Digital health technology utilization is associated with enhanced patient perspectives of care

BACKGROUND

Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores measure patient experience and perspectives on care. Novel health information technologies facilitate communication between patients and healthcare teams. Playback Health is a health information technology that incorporates multimedia for providers to communicate health information to patients, their support network, and their healthcare teams. We hypothesized implementing Playback Health may enhance patient perspectives on care.

METHODS

HCAHPS scores were obtained retrospectively from a neurosurgical practice located in a metropolitan area between 2020 and 2022 for seven providers. Of these, four providers utilized Playback Health, and three did not. Individual providers' scores were compared between domains using two tailed t-tests at a significance level of p < 0.05.

RESULTS

Playback Health use was associated with higher HCAHPS scores across varying domains from 2020 through 2022 as well as overall scores. In 2020, the mean overall score of HCAHPS users was higher than non-users (89.65 vs. 87.28, p = 0.0095). In 2021, again higher mean overall scores were observed in users as compared to non-users (89.11 vs. 87.79, p 0.0266). In 2022, Playback Health users maintained higher scores across communication domains and overall scores (p < 0.00001). Comparisons within domains between Playback Health users and non-users revealed no domains in which non-users had a significantly higher score than users.

CONCLUSION

The addition of Playback Health multimedia health information technology was associated with improved patient satisfaction scores. When used as an adjunct to existing patient care, multimedia health information technologies may improve patient perceived care.

Assessment of Patient Matters in Healthcare Facilities

BACKGROUND AND OBJECTIVES

Ensuring the sustainability of healthcare facilities requires the evaluation of patient matters with appropriate methods and tools. The objective of this research is to develop a new tool for assessing patient matters as a component of social responsibility requirements that contribute to the sustainability of healthcare facilities.

MATERIALS AND METHODS

We carried out an analytical observational study in which, starting from the domains of the reference framework for the sustainability of health facilities (economic, environmental, social, provision of sustainable medical care services and management processes), we designed indicators that describe patient matters. To achieve this, we extracted from the scientific literature the most recent data and aspects related to patient matters that have been reported by representative hospitals from all over the world. These were organized into the four sequences of the quality cycle. We designed the method of evaluating the indicators based on the information couple achievement degree-importance of the indicator. In the experimental part of the study, we validated the indicators for the evaluation of patient matters and the evaluation method at an emergency hospital with an orthopedic profile.

RESULTS

We developed the patient matters indicator matrix, the content of the 8 indicators that make it up, questions for the evaluation of the indicators, and the evaluation grids of the indicators. They describe five levels for each variable of the achievement degree-importance couple. The practical testing of the indicators at the emergency hospital allowed the calculation of sustainability indicators and the development of a prioritization matrix for improvement measures.

CONCLUSIONS

Indicators designed in this research cover social responsibility requirements that describe patient matters. They are compatible and can be used by health facilities along with other implemented national and international requirements. Their added value consists in promoting social responsibility and sustainable development of healthcare facilities.

The effect of using mobile phone applications for intelligent pelvic floor rehabilitation on elderly female patients with stress urinary incontinence

BACKGROUND

Stress urinary incontinence is prevalent among women and the incidence increases with age.

OBJECTIVE

To explore the effect of intelligent pelvic floor muscle rehabilitation on elderly female patients with incontinence.

METHODS

A total of 209 patients with urinary incontinence who were treated with pelvic floor muscle rehabilitation at Peking University International Hospital from September 2020 to January 2022 were selected by convenient sampling. All subjects were divided into the 50-60 year old patient group (n= 51) and over 60 years old patient group according to age (n= 158). The subjects of different age group were divided into an experimental group and a control group. The patients in the control group received routine nursing and health education, and the patients in the observation group received a combination of mobile application use and smart dumbbells. Based on this, we constructed an intervention model for intelligent, continuous pelvic floor rehabilitation. After 7 and 12 weeks, pelvic floor muscle function knowledge and exercise compliance in the two groups were evaluated. The improvement of urinary incontinence symptoms, pelvic floor muscle strength grades and quality-of-life scales were evaluated.

RESULTS

The results showed that pelvic floor knowledge and exercise compliance in the experimental group were better than in the control group at 7 and 12 weeks after intervention (P< 0.05). There was no significant difference in pelvic floor muscle strength and quality of life between the two groups at 7 weeks after intervention (P> 0.05). However, there was a significant difference in pelvic floor muscle strength and quality of life between the two groups at 12 weeks after intervention (P< 0.05). There was no significant difference between different age groups.

CONCLUSION

The intelligent pelvic floor rehabilitation model that combines a mobile application with smart dumbbells can maintain and strengthen the clinical treatment effect for elderly patients with urinary incontinence.

Transforming Healthcare: The Revolutionary Benefits of Cashless Healthcare Services

As cashless hospital services have grown in popularity, the healthcare sector has seen a tremendous transition. That means the payments are done in an electronic manner which is also known as e-pay. The article discusses the benefits of cashless healthcare services, which are transforming the healthcare sector by providing a streamlined, secure, and effective experience for patients and healthcare providers. Cashless healthcare facilities make use of cutting-edge technologies, including mobile applications, digital wallets, and secure internet platforms, to optimize the utilization of resources within the healthcare ecosystem and improve the overall patient experience. The incorporation of technology has led to revolutionary innovations that continue to redefine the way people access and experience health services. The advantages of cashless hospital services have transformed the healthcare sector by enhancing data security, facilitating transparent billing, and encouraging a patient-centered approach. Cashless services are a preferred method of payment for both consumers and organizations due to their convenience and accessibility. Patients can make payments using digital channels such as mobile payment applications, online payment gateways, or contactless payment cards, whether they are paying for medications, lab tests, or complicated surgeries. Cashless transactions drastically reduce administrative challenges for healthcare providers by eliminating the requirement for manual documentation, which facilitates quick electronic transactions and reduces processing times. As the billing and payment process becomes digitized and streamlined, doctors and medical personnel can focus more on treating and caring for patients. Additionally, much faster insurance claim processing and verification processes result in quicker pay-outs and minimize the financial burden on patients.

Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare

This comprehensive review delves into the impact and challenges of Artificial Intelligence (AI) in nursing science and healthcare. AI has already demonstrated its transformative potential in these fields, with applications spanning from personalized care and diagnostic accuracy to predictive analytics and telemedicine. However, the integration of AI has its complexities, including concerns related to data privacy, ethical considerations, and biases in algorithms and datasets. The future of healthcare appears promising, with AI poised to advance diagnostics, treatment, and healthcare practices. Nevertheless, it is crucial to remember that AI should complement, not replace, healthcare professionals, preserving the essential human element of care. To maximize AI's potential in healthcare, interdisciplinary collaboration, ethical guidelines, and the protection of patient rights are essential. This review concludes with a call to action, emphasizing the need for ongoing research and collective efforts to ensure that AI contributes to improved healthcare outcomes while upholding the highest standards of ethics and patient-centered care.

The digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in Shanghai

Background

Chronic disease management (CDM) falls under production relations, and digital technology belongs to the realm of productivity. Production relations must adapt to the development of productivity. Simultaneously, the prevalence and burden of chronic diseases are becoming increasingly severe, leveraging digital technology to innovate chronic disease management model is essential.

Methods

The model was built to cover experts in a number of fields, including administrative officials, public health experts, information technology staff, clinical experts, general practitioners, nurses, metrologists. Integration of multiple big data platforms such as General Practitioner Contract Platform, Integrated Community Multimorbidity Management System and Municipal and District-Level Health Information Comprehensive Platform. This study fully analyzes the organizational structure, participants, service objects, facilities and equipment, digital technology, operation process, etc., required for new model in the era of big data.

Results

Based on information technology, we build Integrated Community Multimorbidity Care Model (ICMCM). This model is based on big data, is driven by "technology + mechanism," and uses digital technology as a tool to achieve the integration of services, technology integration, and data integration, thereby providing patients with comprehensive people-centered services. In order to promote the implementation of the ICMCM, Shanghai has established an integrated chronic disease management information system, clarified the role of each module and institution, and achieved horizontal and vertical integration of data and services. Moreover, we adopt standardized service processes and accurate blood pressure and blood glucose measurement equipment to provide services for patients and upload data in real time. On the basis of Integrated Community Multimorbidity Care Model, a platform and index system have been established, and the platform's multidimensional cross-evaluation and indicators are used for management and visual display.

Conclusions

The Integrated Community Multimorbidity Care Model guides chronic disease management in other countries and regions. We have utilized models to achieve a combination of services and management that provide a grip on chronic disease management.

Nutrition policy critical to optimize response to climate, public health crises

The effects of unanticipated crises on health care and first-responder systems are reflected in climate-fueled environmental emergencies, to which human resilience is diminished by our chronic disease epidemic. For example, people who depend on specialized medications, like refrigerated insulin for diabetes, will likely face additional challenges in receiving treatment and care during extreme heat, floods, disasters, and other adverse events. These circumstances may be compounded by staff and equipment shortages, lack of access to fresh food, and inadequate healthcare infrastructure in the wake of a disaster. Simply put, our health care and first-response systems struggle to meet the demands of chronic disease without such crises and may be fundamentally unable to adequately function with such crises present. However, nutrition's primacy in preventing and controlling chronic disease directly enhances individual and public resilience in the face of existential threats. Highlighting the shared diet-related etiology clearly demonstrates the need for a national policy response to reduce the disease burden and potentiate mitigation of the sequelae of climate risks and capacity limits in our food and health care systems. Accordingly, this article proposes four criteria for nutrition policy in the Anthropocene: objective government nutrition recommendations, healthy dietary patterns, adequate nutrition security, and effective nutrition education. Application of such criteria shows strong potential to improve our resiliency despite the climate and public health crises.

Decreasing Racial Disparities in Preventable Emergency Department Visits Through Hospital Health Information Technology Patient Engagement Functionalities

Introduction: Hospitals are a major source of care for underserved populations in the United States. However, little is known about how hospital-based health information technology (HIT) can improve the efficiency of care and reduce disparities. Objective: We examined the variation of preventable emergency department (ED) visits and associated racial disparities by hospital adoption of HIT patient engagement (HIT-PE) functionalities. Methods: This was an observational study of 6,543,514 non-Hispanic Black (Black) and non-Hispanic White (White) adult patients using 2019 datasets of seven states (Arizona, Florida, Kentucky, Maryland, North Carolina, Vermont, Wisconsin) from the State Emergency Department Databases, American Hospital Association Annual Survey & Information Technology Supplement, and Area Health Resources File. Results: High HIT-PE adoption was associated with lower rates of preventable ED (odds ratio [OR] = 0.992, p < 0.001). Specific HIT-PE functions such as importing medical records from other organizations into the patient portal (OR = 0.977, p < 0.001), electronically sending medical information to a third party (OR = 0.970, p < 0.001), and scheduling appointments online (OR = 0.987, p < 0.001) were also associated with reduced preventable ED rates. Black patients had higher rates of preventable ED compared with Whites (OR = 1.386, p < 0.001); however, the interaction of Black patients and high HIT-PE adoption was associated with lower rates of preventable ED (OR = 0.977, p < 0.001). Our results also showed that higher HIT-PE adoption was associated with a reduction in preventable ED visits among Black patients with comorbidities and Black patients living in low-income areas. Conclusions: The results of our study suggest that there is potential to reduce preventable ED rates and racial disparities through hospital-based HIT-PE functionalities.

Promoting Self-management and Patient Activation Through eHealth: Protocol for a Systematic Literature Review and Meta-analysis

BACKGROUND

Major advances in different cancer treatment modalities have been made, and people are now living longer with cancer. However, patients with cancer experience a range of physical and psychological symptoms during and beyond cancer treatment. New models of care are needed to combat this rising challenge. A growing body of evidence supports the effectiveness of eHealth interventions in the delivery of supportive care to people living with the complexities of chronic health conditions. However, reviews on the effects of eHealth interventions are scarce in the field of cancer-supportive care, particularly for interventions with the aim of empowering patients to manage cancer treatment-related symptoms. For this reason, this protocol has been developed to guide a systematic review and meta-analysis to assess the effectiveness of eHealth interventions for supporting patients with cancer in managing cancer-related symptoms.

OBJECTIVE

This systematic review with meta-analysis is conducted with the aim of identifying eHealth-based self-management intervention studies for adult patients with cancer and evaluating the efficacy of eHealth-based self-management tools and platforms in order to synthesize the empirical evidence on self-management and patient activation through eHealth.

METHODS

A systematic review with meta-analysis and methodological critique of randomized controlled trials is conducted following Cochrane Collaboration methods. Multiple data sources are used to identify all potential research sources for inclusion in the systematic review: (1) electronic databases such as MEDLINE, (2) forward reference searching, and (3) gray literature. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for conducting the review were followed. The PICOS (Population, Interventions, Comparators, Outcomes, and Study Design) framework is used to identify relevant studies.

RESULTS

The literature search yielded 10,202 publications. The title and abstract screening were completed in May 2022. Data will be summarized, and if possible, meta-analyses will be performed. It is expected to finalize this review by Winter 2023.

CONCLUSIONS

The results of this systematic review will provide the latest data on leveraging eHealth interventions and offering effective and sustainable eHealth care, both of which have the potential to improve quality and efficiency in cancer-related symptoms.

TRIAL REGISTRATION

PROSPERO 325582; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=325582.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/38758.

Intelligent Emotion and Sensory Remote Prioritisation for Patients with Multiple Chronic Diseases

An intelligent remote prioritization for patients with high-risk multiple chronic diseases is proposed in this research, based on emotion and sensory measurements and multi-criteria decision making. The methodology comprises two phases: (1) a case study is discussed through the adoption of a multi-criteria decision matrix for high-risk level patients; (2) the technique for reorganizing opinion order to interval levels (TROOIL) is modified by combining it with an extended fuzzy-weighted zero-inconsistency (FWZIC) method over fractional orthotriple fuzzy sets to address objective weighting issues associated with the original TROOIL. In the first hierarchy level, chronic heart disease is identified as the most important criterion, followed by emotion-based criteria in the second. The third hierarchy level shows that Peaks is identified as the most important sensor-based criterion and chest pain as the most important emotion criterion. Low blood pressure disease is identified as the most important criterion for patient prioritization, with the most severe cases being prioritized. The results are evaluated using systematic ranking and sensitivity analysis.

Association of multimorbidity with the use of health information technology

Objective

To examine the association of multimorbidity with health information technology use among adults in the USA.

Methods

We used cross-sectional study design and data from the Health Information National Trends Survey 5 Cycle 4. Health information technology use was measured with ten variables comprising access, recent use, and healthcare management. Unadjusted and adjusted logistic and multinomial logistic regressions were used to model the associations of multimorbidity with health information technology use.

Results

Among adults with multimorbidity, health information technology use for specific purposes ranged from 37.8% for helping make medical decisions to 51.7% for communicating with healthcare providers. In multivariable regressions, individuals with multimorbidity were more likely to report general use of health information technology (adjusted odds ratios  =  1.48, 95% confidence intervals  =  1.01-2.15) and more likely to use health information technology to check test results (adjusted odds ratios  =  1.85, 95% confidence intervals  =  1.33-2.58) compared to adults with only one chronic condition, however, there were no significant differences in other forms of health information technology use. We also observed interactive associations of multimorbidity and age on various components of health information technology use. Compared to younger adults with multimorbidity, older adults (≥ 65 years of age) with multimorbidity were less likely to use almost all aspects of health information technology.

Conclusion

Health information technology use disparities by age and multimorbidity were observed. Education and interventions are needed to promote health information technology use among older adults in general and specifically among older adults with multimorbidity.

Quantifying care delivery team influences on the hospitalization outcomes of patients with multimorbidity: Implications for clinical informatics

The primary objective was to quantify the influences of care delivery teams on the outcomes of patients with multimorbidity. Electronic medical record data on 68,883 patient care encounters (i.e., 54,664 patients) were extracted from the Arkansas Clinical Data Repository. Social network analysis assessed the minimum care team size associated with improved care outcomes (i.e., hospitalizations, days between hospitalizations, and cost) of patients with multimorbidity. Binomial logistic regression further assessed the influence of the presence of seven specific clinical roles. When compared to patients without multimorbidity, patients with multimorbidity had a higher mean age (i.e., 47.49 v. 40.61), a higher mean dollar amount of cost per encounter (i.e., $3,068 v. $2,449), a higher number of hospitalizations (i.e., 25 v. 4), and a higher number of clinicians engaged in their care (i.e., 139,391 v. 7,514). Greater network density in care teams (i.e., any combination of two or more Physicians, Residents, Nurse Practitioners, Registered Nurses, or Care Managers) was associated with a 46-98% decreased odds of having a high number of hospitalizations. Greater network density (i.e., any combination of two or more Residents or Registered Nurses) was associated with 11-13% increased odds of having a high cost encounter. Greater network density was not significantly associated with having a high number of days between hospitalizations. Analyzing the social networks of care teams may fuel computational tools that better monitor and visualize real-time hospitalization risk and care cost that are germane to care delivery.

Prediction of Future Health Care Utilization Through Note-extracted Psychosocial Factors

BACKGROUND

Persons with multimorbidity (≥2 chronic conditions) face an increased risk of poor health outcomes, especially as they age. Psychosocial factors such as social isolation, chronic stress, housing insecurity, and financial insecurity have been shown to exacerbate these outcomes, but are not routinely assessed during the clinical encounter. Our objective was to extract these concepts from chart notes using natural language processing and predict their impact on health care utilization for patients with multimorbidity.

METHODS

A cohort study to predict the 1-year likelihood of hospitalizations and emergency department visits for patients 65+ with multimorbidity with and without psychosocial factors. Psychosocial factors were extracted from narrative notes; all other covariates were extracted from electronic health record data from a large academic medical center using validated algorithms and concept sets. Logistic regression was performed to predict the likelihood of hospitalization and emergency department visit in the next year.

RESULTS

In all, 76,479 patients were eligible; the majority were White (89%), 54% were female, with mean age 73. Those with psychosocial factors were older, had higher baseline utilization, and more chronic illnesses. The 4 psychosocial factors all independently predicted future utilization (odds ratio=1.27-2.77, C -statistic=0.63). Accounting for demographics, specific conditions, and previous utilization, 3 of 4 of the extracted factors remained predictive (odds ratio=1.13-1.86) for future utilization. Compared with models with no psychosocial factors, they had improved discrimination. Individual predictions were mixed, with social isolation predicting depression and morbidity; stress predicting atherosclerotic cardiovascular disease onset; and housing insecurity predicting substance use disorder morbidity.

DISCUSSION

Psychosocial factors are known to have adverse health impacts, but are rarely measured; using natural language processing, we extracted factors that identified a higher risk segment of older adults with multimorbidity. Combining these extraction techniques with other measures of social determinants may help catalyze population health efforts to address psychosocial factors to mitigate their health impacts.

Collecting and sharing self-generated health and lifestyle data: Understanding barriers for people living with long-term health conditions - a survey study

Background

The growing popularity of collecting self-generated health and lifestyle data presents a valuable opportunity to develop our understanding of long-term health conditions and improve care. Barriers remain to the effective sharing of health and lifestyle data by those living with long-term health conditions which include beliefs around concepts of Trust, Identity, Privacy and Security, experiences of stigma, perceptions of risk and information sensitivity.

Method

We surveyed 250 UK adults who reported living with a range of long-term health conditions. We recorded data to assess self-reported behaviours, experiences, attitudes and motivations relevant to sharing self-generated health and lifestyle data. We also asked participants about their beliefs about Trust, Identity, Privacy and Security, stigma, and perceptions of risk and information sensitivity regarding their health and lifestyle data.

Results

Three-quarters of our sample reported recording information about their health and lifestyle on a daily basis. However, two-thirds reported never or rarely sharing this information with others. Trust, Identity, Privacy and Security concerns were considered to be 'very important' by those with long-term health conditions when deciding whether or not to share self-generated health and lifestyle data with others, with security concerns considered most important. Of those living with a long-term health condition, 58% reported experiencing stigma associated with their condition. The greatest perceived risk from sharing with others was the potential for future harm to their social relationships.

Conclusions

Our findings suggest that, in order for health professionals and researchers to benefit from the increased prevalence of self-generated health and lifestyle data, more can be done to address security concerns and to understand perceived risks associated with data sharing. Digital platforms aimed at facilitating the sharing of self-generated health and lifestyle data may look to highlight security features, enable users to control the sharing of certain information types, and emphasise the practical benefits to users of sharing health and lifestyle data with others.

Cultivating informatics capacity for multimorbidity: A learning health systems use case

Background

The aim of this study was to characterize patterns of multimorbidity across patients and identify opportunities to strengthen the informatics capacity of learning health systems that are used to characterize multimorbidity across patients.

Methods

Electronic health record (EHR) data on 225,710 multimorbidity patients were extracted from the Arkansas Clinical Data Repository as a use case. Hierarchical cluster analysis identified the most frequently occurring combinations of chronic conditions within the learning health system's captured data.

Results

Results revealed multimorbidity was highest among patients ages 60 to 74, Caucasians, females, and Medicare payors. The largest numbers of chronic conditions occurred in the smallest numbers of patients (i.e., 70,262 (31%) patients with two conditions, two (<1%) patients with 22 chronic conditions). The results revealed urgent needs to improve EHR systems and processes that collect and manage multimorbidity data (e.g., creating new, multimorbidity-centric data elements in EHR systems, detailed longitudinal tracking of compounding disease diagnoses).

Conclusions

Without additional capacity to collect and aggregate large-scale data, multimorbidity patients cannot benefit from the recent advancements in informatics (i.e., clinical data registries, emerging data standards) that are abundantly working to improve the outcomes of patients with single chronic conditions. Additionally, robust socio-technical system studies of clinical workflows are needed to assess the feasibility of integrating the collection of risk factor data elements (i.e., psycho-social, cultural, ethnic, and socioeconomic attributes of populations) into primary care encounters. These approaches to advancing learning health systems for multimorbidity could substantially reduce the constraints of current technologies, data, and data-capturing processes.