Behavioral Health Information Technology: From Chaos To Clarity

Development of an ontology to characterize mental functioning

PURPOSE OF THE ARTICLE

This article describes a conceptual and methodological approach to integrating functional information into an ontology to categorize mental functioning, which to date is an under-developed area of classification, and supports our work with the United States (U.S.) Social Security Administration (SSA).

DESIGN AND METHODOLOGICAL PROCEDURES

Conceptualizing and defining mental functioning was paramount to develop natural language processing (NLP) tools to support our use case. The International Classification of Functioning, Disability, and Health (ICF) was the framework used to conceptualize mental functioning at the activities and participation level in clinical records. To address challenges that arose when applying the ICF as to what should or should not be classified as mental functioning, a mental functioning domain ontology was developed that rearranged, reclassified and incorporated all ICF key components, concepts, classifications, and their definitions.

CONCLUSIONS

Challenges emerged in the extent to which we could directly align components in the ICF into an applied ontology of mental functioning. These conceptual challenges required rearrangement of ICF components to adequately support our use case within the social security disability determination process. Findings also have implications to support future NLP efforts for behavioral health outcomes and policy research.

Perspectives on Challenges and Opportunities for Interoperability: Findings From Key Informant Interviews With Stakeholders in Ohio

BACKGROUND

Interoperability-the exchange and integration of data across the health care system-remains a challenge despite ongoing policy efforts aimed at promoting interoperability.

OBJECTIVE

This study aimed to identify current challenges and opportunities to advancing interoperability across stakeholders.

METHODS

Primary data were collected through qualitative, semistructured interviews with stakeholders (n=24) in Ohio from July to October 2021. Interviewees were sampled using a stratified purposive sample of key informants from 4 representative groups as follows: acute care and children's hospital leaders, primary care providers, behavioral health providers, and regional health information exchange networks. Interviews focused on key informant perspectives on electronic health record implementation, the alignment of public policy with organizational strategy, interoperability implementation challenges, and opportunities for health information technology. The interviews were transcribed verbatim followed by rigorous qualitative analysis using directed content analysis.

RESULTS

The findings illuminate themes related to challenges and opportunities for interoperability that align with technological (ie, implementation challenges, mismatches in interoperability capabilities across stakeholders, and opportunities to leverage new technology and integrate social determinants of health data), organizational (ie, facilitators of interoperability and strategic alignment of participation in value-based payment programs with interoperability), and environmental (ie, policy) domains.

CONCLUSIONS

Interoperability, although technically feasible for most providers, remains challenging for technological, organizational, and environmental reasons. Our findings suggest that the incorporation of end user considerations into health information technology development, implementation, policy, and standard deployment may support interoperability advancement.

Value-Based Payment and Behavioral Health

This Viewpoint discusses behavioral health and its transition to value-based payment models.

Expectations for Artificial Intelligence (AI) in Psychiatry

PURPOSE OF REVIEW

Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine.

RECENT FINDINGS

For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.

Examining the adoption and implementation of behavioral electronic health records by healthcare professionals based on the clinical adoption framework

Background

While various quantitative studies based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Models (TAM) exist in the general medical sectors, just a few have been conducted in the behavioral sector; they have all been qualitative interview-based studies.

Objective

The purpose of this study is to assess the adoption dimensions of a behavioral electronic health record (EHR) system for behavioral clinical professionals using a modified clinical adoption (CA) research model that incorporates a variety of micro, meso, and macro level factors.

Methods

A questionnaire survey with quantitative analysis approach was used via purposive sampling method. We modified the existing CA framework to be suitable for evaluating the adoption of an EHR system by behavioral clinical professionals. We designed and verified questionnaires that fit into the dimensions of the CA framework. The survey was performed in five US behavioral hospitals, and the adoption factors were analyzed using a structural equation analysis.

Results

We derived a total of seven dimensions, omitting those determined to be unsuitable for behavioral clinical specialists to respond to. We polled 409 behavioral clinical experts from five hospitals. As a result, the ease of use and organizational support had a substantial impact on the use of the behavioral EHR system. Although the findings were not statistically significant, information and service quality did appear to have an effect on the system's ease of use. The primary reported benefit of behavioral EHR system adoption was the capacity to swiftly locate information, work efficiently, and access patient information via a mobile app, which resulted in more time for better care. The primary downside, on the other hand, was an unhealthy reliance on the EHR system.

Conclusions

We demonstrated in this study that the CA framework can be a useful tool for evaluating organizational and social elements in addition to the EHR system's system features. Not only the EHR system's simplicity of use, but also organizational support, should be considered for the effective implementation of the behavioral EHR system.

Trial Registration: The study was approved by the Institutional Review Board of Seoul National University Bundang Hospital (IRB No.: B-1904-534-301).

Impact of Electronic Health Records on Information Practices in Mental Health Contexts: Scoping Review

BACKGROUND

The adoption of electronic health records (EHRs) and electronic medical records (EMRs) has been slow in the mental health context, partly because of concerns regarding the collection of sensitive information, the standardization of mental health data, and the risk of negatively affecting therapeutic relationships. However, EHRs and EMRs are increasingly viewed as critical to improving information practices such as the documentation, use, and sharing of information and, more broadly, the quality of care provided.

OBJECTIVE

This paper aims to undertake a scoping review to explore the impact of EHRs on information practices in mental health contexts and also explore how sensitive information, data standardization, and therapeutic relationships are managed when using EHRs in mental health contexts.

METHODS

We considered a scoping review to be the most appropriate method for this review because of the relatively recent uptake of EHRs in mental health contexts. A comprehensive search of electronic databases was conducted with no date restrictions for articles that described the use of EHRs, EMRs, or associated systems in the mental health context. One of the authors reviewed all full texts, with 2 other authors each screening half of the full-text articles. The fourth author mediated the disagreements. Data regarding study characteristics were charted. A narrative and thematic synthesis approach was taken to analyze the included studies' results and address the research questions.

RESULTS

The final review included 40 articles. The included studies were highly heterogeneous with a variety of study designs, objectives, and settings. Several themes and subthemes were identified that explored the impact of EHRs on information practices in the mental health context. EHRs improved the amount of information documented compared with paper. However, mental health-related information was regularly missing from EHRs, especially sensitive information. EHRs introduced more standardized and formalized documentation practices that raised issues because of the focus on narrative information in the mental health context. EHRs were found to disrupt information workflows in the mental health context, especially when they did not include appropriate templates or care plans. Usability issues also contributed to workflow concerns. Managing the documentation of sensitive information in EHRs was problematic; clinicians sometimes watered down sensitive information or chose to keep it in separate records. Concerningly, the included studies rarely involved service user perspectives. Furthermore, many studies provided limited information on the functionality or technical specifications of the EHR being used.

CONCLUSIONS

We identified several areas in which work is needed to ensure that EHRs benefit clinicians and service users in the mental health context. As EHRs are increasingly considered critical for modern health systems, health care decision-makers should consider how EHRs can better reflect the complexity and sensitivity of information practices and workflows in the mental health context.

Health Plans Struggle to Report on Depression Quality Measures That Require Clinical Data

OBJECTIVE

Depression quality measures aligned with evidence-based practices require that health care organizations use standardized tools for tracking and monitoring patient-reported symptoms and functioning over time. This study describes challenges and opportunities for reporting 5 HEDIS measures which use electronic clinical data to assess adolescent and perinatal depression care quality.

METHODS

Two learning collaboratives were convened with 10 health plans from 5 states to support reporting of the depression measures. We conducted content analysis of notes from collaborative meetings and individual calls with health plans to identify key challenges and strategies for reporting.

RESULTS

Health plans used various strategies to collect the clinical data needed to report the measures, including setting up direct data exchange with providers and data aggregators and leveraging data captured in health information exchanges and case management records. Health plans noted several challenges to reporting and performance improvement: 1) lack of access to clinical data sources where the results of patient-reported tools were documented; 2) unavailability of the results of patient-reported tools in usable data fields; 3) lack of routine depression screening and ongoing assessment occurring in provider practices.

CONCLUSIONS

Our findings demonstrate ongoing challenges in collecting and using patient-reported clinical data for health plan quality measurement. Systems to track and improve outcomes for individuals with depression will require significant investments and policy support at the point of care and across the healthcare system.

Improving Physical Health Among People With Serious Mental Illness: The Role of the Specialty Mental Health Sector

People with serious mental illness die 10-20 years earlier, compared with the overall population, and the excess mortality is driven by undertreated physical health conditions. In the United States, there is growing interest in models integrating physical health care delivery, management, or coordination into specialty mental health programs, sometimes called "reverse integration." In November 2019, the Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness convened a forum of 25 experts to discuss the current state of the evidence on integrated care models based in the specialty mental health system and to identify priorities for future research, policy, and practice. This article summarizes the group's conclusions. Key research priorities include identifying the active ingredients in multicomponent integrated care models and developing and validating integration performance metrics. Key policy and practice recommendations include developing new financing mechanisms and implementing strategies to build workforce and data capacity. Forum participants also highlighted an overarching need to address socioeconomic risks contributing to excess mortality among adults with serious mental illness.

The Critical Role of Health Information Technology in the Safe Integration of Behavioral Health and Primary Care to Improve Patient Care

Integrated behavioral health and primary care is a set of related approaches to enhance care delivery, safety, and quality through closer collaboration and coordination among clinicians and care organizations in these two fields. Technology (e.g., electronic health records (EHR), telehealth, clinical decision support, and standards-based interoperability) can enable integration and improve delivery of care. Reinforcing these opportunities, virtual care and telehealth have dramatically changed care availability and delivery, especially during the COVID-19 pandemic. There are different levels of integration, ranging from minimal to full collaboration. The Partnership for Health IT Patient Safety and the HIMSS Electronic Health Record Association (EHRA) formed a workgroup to examine using information technology to facilitate integration. Taking a three-pronged approach focused on (1) screening for behavioral health issues, (2) clinician documentation, and (3) sharing data among clinicians, patients, and authorized parties, the workgroup developed action-oriented recommendations and strategies for safe use of health IT for stakeholders and policymakers seeking to advance efforts to integrate behavioral health with primary care.

Intention to Use Behavioral Health Data From a Health Information Exchange: Mixed Methods Study

BACKGROUND

Patients with co-occurring behavioral health and chronic medical conditions frequently overuse inpatient hospital services. This pattern of overuse contributes to inefficient health care spending. These patients require coordinated care to achieve optimal health outcomes. However, the poor exchange of health-related information between various clinicians renders the delivery of coordinated care challenging. Health information exchanges (HIEs) facilitate health-related information sharing and have been shown to be effective in chronic disease management; however, their effectiveness in the delivery of integrated care is less clear. It is prudent to consider new approaches to sharing both general medical and behavioral health information.

OBJECTIVE

This study aims to identify and describe factors influencing the intention to use behavioral health information that is shared through HIEs.

METHODS

We used a mixed methods design consisting of two sequential phases. A validated survey instrument was emailed to clinical and nonclinical staff in Alabama and Oklahoma. The survey captured information about the impact of predictors on the intention to use behavioral health data in clinical decision making. Follow-up interviews were conducted with a subsample of participants to elaborate on the survey results. Partial least squares structural equation modeling was used to analyze survey data. Thematic analysis was used to identify themes from the interviews.

RESULTS

A total of 62 participants completed the survey. In total, 63% (n=39) of the participants were clinicians. Performance expectancy (β=.382; P=.01) and trust (β=.539; P<.001) predicted intention to use behavioral health information shared via HIEs. The interviewees (n=5) expressed that behavioral health information could be useful in clinical decision making. However, privacy and confidentiality concerns discourage sharing this information, which is generally missing from patient records altogether. The interviewees also stated that training for HIE use was not mandatory; the training that was provided did not focus specifically on the exchange of behavioral health information.

CONCLUSIONS

Despite barriers, individuals are willing to use behavioral health information from HIEs if they believe that it will enhance job performance and if the information being transmitted is trustworthy. The findings contribute to our understanding of the role HIEs can play in delivering integrated care, particularly to vulnerable patients.

Measurement-based Data to Monitor Quality: Why Specification at the Population Level Matter?

Measurement-based care is conceptualized as a driver for quality improvement. The triple aim in the National Quality Strategy purposively muddles the population levels to provide a health policy goal that is encompassing, transactional, and will stimulate change. Specification of the population level has implications for the purpose, proposed target mechanisms that drive quality improvement, methodologic challenges, and implications for program evaluation and data interpretation. To demonstrate, population levels are conceptualized at the individual (tier 1), clinical aggregate (tier 2), and national level (tier 3).

Coordination of services for people with serious mental illness and general medical conditions: Perspectives from rural northeastern United States

OBJECTIVE

The objective of the study was to investigate providers' perspectives on how medical, mental health, and social services are coordinated for people with serious mental illnesses and general medical conditions in 2 predominantly rural states.

METHOD

To achieve multiple perspectives on service coordination, this study includes perspectives from providers employed in community mental health centers, social service agencies, and primary care settings in 2 northern rural New England states with contrasting approaches to financing and organizing services. We conducted 29 individual semistructured interviews and 1 focus group, which included administrative leaders, team leaders, primary care providers, social workers, and case managers who provide services for people with serious mental illness. Data were analyzed using qualitative thematic content analysis.

RESULTS

We identified key themes at 3 levels: (a) provider-level coordination: bridging across services; managing interprofessional communications; and contrasting perspectives on the locus of responsibility for coordination; (b) individual-level coordination: support for self-management and care navigation; trusting and continuous relationships; and the right to individual choice and autonomy; (c) system-level coordination: linking appropriate residential and care provision services, funding, recruiting and retaining staff, policy enablers, and integration solutions.

CONCLUSIONS

Three levels of provider-reported coordination themes are described for the 2 states, reflecting efforts to coordinate and integrate service delivery across medical, mental health, and social services.

IMPLICATIONS

Improvements in patient outcomes will need additional actions that target key social determinants of health. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Influences of the Industry 4.0 Revolution on the Human Capital Development and Consumer Behavior: A Systematic Review

Automation and digitalization, as long-term evolutionary processes, cause significant effects, such as the transformation of occupations and job profiles, changes to employment forms, and a more significant role for the platform economy, generating challenges for social policy. This systematic literature review aims to provide an overview of the research to date related to influences of the Industry 4.0 Revolution on human capital development and consumer behavior. A search on the Web of Science identified 160 papers that met the inclusion criteria. The major objectives aimed to identify: the main types of influences of the Industry 4.0 Revolution on human capital development and consumer behavior; the main opportunities and challenges for new directions in education associated with shifting the work environment; and the drivers for human capital development and consumer behavior through the lenses of the Industry 4.0 Revolution. The results revealed some key aspects for the development of human capital: information, new jobs, the Internet, technology, training, education, new skills, automation, communication, innovativeness, professionals, productivity, artificial intelligence, digitalization, e-recruitment, and the Internet of Things, as well as the main drivers of consumer behavior: information, e-commerce, digitalization, the Internet of Things, e-distribution, technology, digitalization, automation, personalized, performance, artificial intelligence, behavior intention, e-shopping, and data mining.

Automated ICD coding via unsupervised knowledge integration (UNITE)

OBJECTIVE

Accurate coding is critical for medical billing and electronic medical record (EMR)-based research. Recent research has been focused on developing supervised methods to automatically assign International Classification of Diseases (ICD) codes from clinical notes. However, supervised approaches rely on ICD code data stored in the hospital EMR system and is subject to bias rising from the practice and coding behavior. Consequently, portability of trained supervised algorithms to external EMR systems may suffer.

METHOD

We developed an unsupervised knowledge integration (UNITE) algorithm to automatically assign ICD codes for a specific disease by analyzing clinical narrative notes via semantic relevance assessment. The algorithm was validated using coded ICD data for 6 diseases from Partners HealthCare (PHS) Biobank and Medical Information Mart for Intensive Care (MIMIC-III). We compared the performance of UNITE against penalized logistic regression (LR), topic modeling, and neural network models within each EMR system. We additionally evaluated the portability of UNITE by training at PHS Biobank and validating at MIMIC-III, and vice versa.

RESULTS

UNITE achieved an averaged AUC of 0.91 at PHS and 0.92 at MIMIC over 6 diseases, comparable to LR and MLP. It had substantially better performance than topic models. In regards to portability, the performance of UNITE was consistent across different EMR systems, superior to LR, topic models and neural network models.

CONCLUSION

UNITE accurately assigns ICD code in EMR without requiring human labor, and has major advantages over commonly used machine learning approaches. In addition, the UNITE attained stable performance and high portability across EMRs in different institutions.

Patient Privacy Perspectives on Health Information Exchange in a Mental Health Context: Qualitative Study

BACKGROUND

The privacy of patients with mental health conditions is prominent in health information exchange (HIE) discussions, given that their potentially sensitive personal health information (PHI) may be electronically shared for various health care purposes. Currently, the patient privacy perspective in the mental health context is not well understood because of the paucity of in-depth patient privacy research; however, the evidence suggests that patient privacy perspectives are more nuanced than what has been assumed in the academic and health care community.

OBJECTIVE

This study aimed to generate an understanding on how patients with mental health conditions feel about privacy in the context of HIE in Canada. This study also sought to identify the factors underpinning their privacy perspectives and explored how their perspectives influenced their attitudes toward HIE.

METHODS

Semistructured interviews were conducted with patients at a Canadian academic hospital for addictions and mental health. Guided by the Antecedent-Privacy Concern-Outcome macro-model, interview transcripts underwent deductive and inductive thematic analyses.

RESULTS

We interviewed 14 participants. Their privacy concerns varied, depending on the participant's privacy experiences and health care perceptions. Media reports of privacy breaches and hackers had little impact on participants' privacy concerns because of a fatalistic belief that privacy breaches are a reality in the digital age. Rather, direct observations and experiences with the mistreatment of PHI in health care settings caused concern. Decisions to trust others with PHI depended on past experiences with the individual (or institution) and health care needs. Participants had little knowledge of patient privacy rights and legislation but were willing to participate in HIE because of perceived individual and societal benefits.

CONCLUSIONS

This study introduces evidence that patients with mental health conditions would support HIE. Participants were pragmatic, supporting HIE because they wanted the best care possible. They also understood that their PHI was critical in supporting the single-payer Canadian health care system. Participant health care experiences informed their privacy perspectives, trust, and PHI sharing attitudes-all accentuating the importance of the patient experience in building trust in HIE. Their lack of knowledge about patient rights and PHI uses highlights the degree of trust they have in the health care system to protect their privacy. These findings suggest that the patient privacy discourse should extend beyond the oft-cited barrier of patient privacy concerns to include discussions about building trust, communicating the benefits of HIE, and improving patient experiences. Although our findings are in the Canadian context, this study highlights the importance of engaging patients in privacy policy discussions, regardless of jurisdiction, to ensure their nuanced perspectives are reflected in policy decisions on their PHI.

Translating Research into Agile Development (TRIAD): Development of Electronic Health Record Tools for Primary Care Settings

OBJECTIVES

This article describes a method for developing electronic health record (EHR) tools for use in primary care settings.

METHODS

The "Translating Research into Agile Development" (TRIAD) method relies on the close collaboration of researchers, end users, and development teams. This five-step method for designing a tailored EHR tool includes (1) assessment, observation, and documentation; (2) structured engagement for collaboration and iterative data collection; (3) data distillation; (4) developmental feedback from clinical team members on high-priority EHR needs and input on design prototypes and EHR functionality; and (5) agile scrum sprint cycles for prototype development.

RESULTS

The TRIAD method was used to modify an existing EHR for behavioral health clinicians (BHCs) embedded with primary care teams, called the BH e-Suite. The structured engagement processes stimulated discussions on how best to automate BHC screening tools and provide goal tracking functionality over time. Data distillation procedures rendered technical documents, with information on workflow steps, tasks, and associated challenges. In the developmental feedback phase, BHCs gave input on screening assessments, scoring needs, and other functionality to inform prototype feature development. Six 2-week sprint cycles were conducted to address three domains of prototype development: assessment and documentation needs, information retrieval, and monitoring and tracking. The BH e-Suite tool resulted with eight new EHR features to accommodate BHCs' needs.

CONCLUSION

The TRIAD method can be used to develop EHR functionality to address the evolving needs of health professionals in primary care and other settings. The BH e-Suite was developed through TRIAD and was found to be acceptable, easy to use, and improved care delivery during pilot testing. The BH e-Suite was later adopted by OCHIN Inc., which provided the tool to its 640 community health centers. This suggests that the TRIAD method is a promising research and development approach.

Assessing Electronic Health Literacy in the State of Kuwait: Survey of Internet Users From an Arab State

Background

The internet and social media have become an important source for health information. In 2017, the State of Kuwait ranked first in mobile subscription penetration in the Arab world; nearly 90% of its population uses the internet. Electronic health (eHealth) literacy is important in populations that have easy and affordable access to internet resources to more effectively manage health conditions as well as improve general population health.

Objective

The aim of this study was to assess eHealth literacy levels across internet users in Kuwait and identify demographic characteristics that influence eHealth literacy. Furthermore, the study aimed to identify the reasons and type of information that people seek online. Finally, this study examined the utilization of various social media channels for accessing online health information. The social media platforms considered were as follows: WhatsApp, Twitter, Instagram, YouTube, Facebook, and Snapchat.

Methods

A cross-sectional anonymous Web-based survey was used to collect data about eHealth literacy and related information. The eHealth literacy scale (eHEALS), originally developed by Norman and Skinner, is measured using 8 Likert-type scales. A linear regression model estimates the effect of demographic variables such as age, gender, and education on eHealth literacy while controlling for participants’ perceived usefulness and importance of the internet. Participants were also surveyed about their frequency in using social media platforms for seeking health information.

Results

Kuwait’s composite eHEALS, based on a sample of 386 participants, was 28.63, which is very similar to eHEALS observed among adult populations in other developed countries. Females in Kuwait demonstrated a higher average eHEALS compared with males. Among the social media platforms, the survey results indicated that YouTube is the most frequently used to seek health information, with Facebook being the least frequently used.

Conclusions

Internet users in Kuwait appear confident in their ability to search for health-related information online compared with other populations, as indicated by aggregate eHEALS scores. Considering this finding, government and health care organizations should shift more efforts from traditional media toward online health information, focusing on the social media outlets that people in Kuwait find more useful for seeking health information.

Automation to optimise physician treatment of individual patients: examples in psychiatry

There is widespread agreement by health-care providers, medical associations, industry, and governments that automation using digital technology could improve the delivery and quality of care in psychiatry, and reduce costs. Many benefits from technology have already been realised, along with the identification of many challenges. In this Review, we discuss some of the challenges to developing effective automation for psychiatry to optimise physician treatment of individual patients. Using the perspective of automation experts in other industries, three examples of automation in the delivery of routine care are reviewed: (1) effects of electronic medical records on the patient interview; (2) effects of complex systems integration on e-prescribing; and (3) use of clinical decision support to assist with clinical decision making. An increased understanding of the experience of automation from other sectors might allow for more effective deployment of technology in psychiatry.

National Child Mental Health Quality Measures: Adherence Rates and Extent of Evidence for Clinical Validity

PURPOSE OF REVIEW

To provide an overview of the selection process and annual updates of the child mental health measures within the Child Core Set, describe national and statewide adherence rates, and summarize findings from a systematic literature review examining measure adherence rates and whether adherence is associated with improved clinical outcomes.

RECENT FINDINGS

Five national quality measures target child mental health care processes. On average, national adherence varied widely by state, and performance did not substantially improve during the past 5 years. Mean national adherence rates for the two measures related to timeliness of care were below 50%. For each measure, scientific evidence to support the association between adherence and improved clinical outcomes was scarce. Investment in academic-agency partnered research to standardize methods for publicly reporting adherence to national child mental health quality measures and validation of these measures should be a national priority for child healthcare research.

Evaluating hospital websites in Kuwait to improve consumer engagement and access to health information: a cross-sectional analytical study

BACKGROUND

Current advances in information and communication technology have made accessing and obtaining health-related information easier than ever before. Today, many hospital websites use a patient-centric approach to promote engagement and encourage learning for better health-related decision making. However, little is known about the current state of hospital websites in the State of Kuwait. This study aims to evaluate hospital websites in Kuwait and offer recommendations to improve patient engagement and access to health information.

METHODS

This study employs a cross-sectional analytical approach to evaluate hospital websites in Kuwait in 2017. The websites of hospitals that provide in-patient services were identified through a structured search. Only active websites that were available in either English or Arabic were considered. The evaluation of the websites involved a combination of automated and expert- based evaluation methods and was performed across four dimensions: Accessibility, Usability, Presence, and Content.

RESULTS

Nine hospitals met the inclusion criteria. Most of the websites fell short in all four dimensions. None of the websites passed the accessibility guidelines. The usability of websites varied between hospitals. Overall, the majority of hospitals in Kuwait have rudimentary online presence and their websites require careful reassessment with respect to design, content, and user experience. The websites focus primarily on promoting services provided by the hospital rather than engaging and communicating with patients or providing evidence-based information.

CONCLUSIONS

Healthcare organization and website developers should follow best-practices to improve their websites taking into consideration the quality, readability, objectivity, coverage and currency of the information as well as the design of their websites. Hospitals should leverage social media to gain outreach and better engagement with consumers. The websites should be offered in additional languages commonly spoken by people living in Kuwait. Efforts should be made to ensure that health information on hospital websites are evidence-based and checked by healthcare professionals.

Digital Technologies in Psychiatry: Present and Future

The digital revolution has reached the world of mental health. Prominent examples include the rapidly growing use of mobile health apps, the integration of sophisticated machine learning or artificial intelligence for clinical decision support and automated therapy, and the incorporation of virtual reality-based treatments. These diverse technologies hold the promise of addressing several important problems in mental health care, including lack of measurement, uneven access to clinicians, delay in receiving care, fragmentation of care, and negative attitudes toward psychiatry. Here, the authors summarize the current and swiftly changing state of digital mental health. Specifically, they highlight the current unmet needs that emerging technologies may be able to address; summarize what digital health can offer for assessment, treatment, and care integration; and describe some of the challenges and some new directions for innovations in this field. The review concludes with guidance for clinicians to integrate digital technologies into their work and to provide responsible and useful advice to their patients.

Prioritizing quality measure concepts at the interface of behavioral and physical healthcare

Objective

Integrated healthcare models can increase access to care, improve healthcare quality, and reduce cost for individuals with behavioral and general medical healthcare needs, yet there are few instruments for measuring the quality of integrated care. In this study, we identified and prioritized concepts that can represent the quality of integrated behavioral health and general medical care.

Design

We conducted a literature review to identify candidate measure concepts. Experts then participated in a modified Delphi process to prioritize the concepts for development into specific quality measures.

Setting

United States.

Participants

Expert behavioral health and general medical clinicians, decision-makers (policy, regulatory and administrative professionals) and patient advocates.

Main outcome measures

Panelists rated measure concepts on importance, validity and feasibility.

Results

The literature review identified 734 measures of behavioral or general medical care, which were then distilled into 43 measure concepts. Thirty-three measure concepts (including a segmentation strategy) reached a predetermined consensus threshold of importance, while 11 concepts did not. Two measure concepts were 'ready for further development' ('General medical screening and follow-up in behavioral health settings' and 'Mental health screening at general medical healthcare settings'). Among the 31 additional measure concepts that were rated as important, 7 were rated as valid (but not feasible), while the remaining 24 concepts were rated as neither valid nor feasible.

Conclusions

This study identified quality measure concepts that capture important aspects of integrated care. Researchers can use the prioritization process described in this study to guide healthcare quality measures development work.

Measuring and improving the quality of mental health care: a global perspective

Mental disorders are common worldwide, yet the quality of care for these disorders has not increased to the same extent as that for physical conditions. In this paper, we present a framework for promoting quality measurement as a tool for improving quality of mental health care. We identify key barriers to this effort, including lack of standardized information technology-based data sources, limited scientific evidence for mental health quality measures, lack of provider training and support, and cultural barriers to integrating mental health care within general health environments. We describe several innovations that are underway worldwide which can mitigate these barriers. Based on these experiences, we offer several recommendations for improving quality of mental health care. Health care payers and providers will need a portfolio of validated measures of patient-centered outcomes across a spectrum of conditions. Common data elements will have to be developed and embedded within existing electronic health records and other information technology tools. Mental health outcomes will need to be assessed more routinely, and measurement-based care should become part of the overall culture of the mental health care system. Health care systems will need a valid way to stratify quality measures, in order to address potential gaps among subpopulations and identify groups in most need of quality improvement. Much more attention should be devoted to workforce training in and capacity for quality improvement. The field of mental health quality improvement is a team sport, requiring coordination across different providers, involvement of consumer advocates, and leveraging of resources and incentives from health care payers and systems.