Patient-Reported Outcome Measures of Utilizing Person-Generated Health Data in the Case of Simulated Stroke Rehabilitation: Development Method

The Association of Learning Health System Practicing Hospitals and other Health Information Interested Hospitals with Patient-Generated Health Data Uptake

Patient generated health data (PGHD) has been described as a necessary addition to provider-generated information for improving care processes in US hospitals. This study evaluated the distribution of Health Information Interested (HII) US hospitals that are more likely to capture or use PGHD. The literature suggests that HII hospitals are more likely to capture and use PGHD. Cross-sectional analysis of the 2018 American Hospital Association's (AHA) health-IT-supplement and other supporting datasets showed that HII hospitals collectively and majority of HII hospital subcategories evaluated were associated with increased PGHD capture and use. The full Learning Health System (LHS) hospital subcategory had the highest association and hospitals in the meaningful use stage three compliant (MU3) and PCORI funded subcategory also had higher rates of PGHD capture or use when in combination with LHS hospitals. Hence, being LHS appears to be the strongest practice and policy lever to increase PGHD capture and use.

Patient-facing cancer mobile apps that enable patient reported outcome data to be collected: A systematic review of content, functionality, quality, and ability to integrate with electronic health records

PURPOSE

Enabling cancer patients to self-manage symptoms through mobile applications can result in more informed, autonomous patients who are partners in their care, consequently reducing the burden on health services. Electronic patient reported outcomes completed before a clinical review can increase the frequency and quality of holistic assessments, while integration into electronic health records can maximise clinical utility. The ability of apps to integrate with electronic health records is key to providing a real-time interface between patient reports and healthcare response. This review identifies patient-facing cancer apps which can record patient reported outcomes, and explores their purpose, functionality, quality, and ability to integrate with electronic health records.

METHODS

A systematic app review and content synthesis was conducted on patient-facing cancer apps available in the United Kingdom. Where applicable, the review aligned with the Preferred Reporting Items for Systematic Reviews and meta-Analysis. Two validated scales assessed functionality and quality: The IMS Institute for Healthcare Informatics functionality score and the Mobile App Rating Scale. Flesch-Kincaid metrics explored readability.

RESULTS

Apple App and Google Play stores identified 405 apps, of which 12 met the eligibility criteria. All were free to download, 1 (8%) had in-app purchases/subscriptions. Nine (75%) were affiliated with a professional health body/charity. Six (50%) analysed inputted data and provided medical advice based on answers. The average Flesch Reading Ease score was 42.7 out of 100. The apps had an average of 7.3 functions each and a mean MARS score of 4/5. None integrated with electronic health records.

CONCLUSION

While many cancer apps exist, few enable patient reported outcomes to be recorded and shared with clinicians in real-time. Further research is warranted to explore the feasibility of integrating with electronic health records, as this function can improve patient experience and outcomes, and increase efficiency of hospital resources through more proactive care.

The Evolution and Typology of Learning Health System Hospitals and other Health Information Interested Hospitals in the US

This study identifies the type, distribution, and interactions of US hospitals that identify as electronic-data-driven, patient-centric, and learning-focused. Such facilities, termed Health Information Interested (HII) hospitals in this study, meet the defining criteria for one or more of the following designations: learning health systems (LHS), Health Information Technology for Economic and Clinical Health (HITECH) meaningful use stage three compliant (MU3), Patient-Centered Outcomes Research Institute (PCORI) funded, or medical home/safety net (MH/SN) hospital. The American Hospital Association (AHA) IT supplemental survey and other supporting data spanning 2013 to 2018 were used to identify HII hospitals. HII hospitals increased from 19.9 percent to 62.4 percent of AHA reporting hospitals from 2013 to 2018. HII subcategories in 2018 such as the full LHS (37.2 percent) and MU3 (46.9 percent) were dominant, with 33.2 percent having both designations. This indicates increased interest in patient-centric, learning-focused care using electronic health data. This information can enable health information management (HIM) professionals to be aware of programs or approaches that can facilitate learning-focused, patient-centric care using electronic health data within health systems.

Digital Health Interventions in Physiotherapy: Development of Client and Health Care Provider Survey Instruments

BACKGROUND

The advancement of digital health has widened the scope of technology use across multiple frontiers of health care services, including personalized therapeutics, mobile health, eHealth record management, and telehealth consultations. The World Health Organization (WHO) responded to this in 2018 by publishing an inaugural broad classification framework of digital health interventions (DHIs) used to address contemporary health system needs.

OBJECTIVE

This study aims to describe the systematic development of dual survey instruments (clinician and patient) to support data collection, administered in a physiotherapy setting, about perceptions toward DHIs. This is achieved by adapting the WHO framework classification for DHIs for application in real-world research.

METHODS

Using a qualitative item review approach, WHO DHI descriptors were adapted and refined systematically to be used in a survey form. This approach was designed to align with the processes of delivering and receiving care in clinical practice, using musculoskeletal physiotherapy as a practical case scenario.

RESULTS

Complementary survey instruments (for health care providers and clients) were developed by adapting descriptor items. These instruments will be used in a larger study exploring the willingness of physiotherapists and patients to use digital technologies in the management of musculoskeletal conditions.

CONCLUSIONS

This study builds on the WHO-standardized DHI framework. We developed dual novel survey instruments by adapting and refining the functions of DHIs. These may be deployed to explore the perceived usefulness and application of DHIs for different clinical care functions. Researchers may wish to use these survey instruments to examine digital health use systematically in a variety of clinical fields or technology scenarios in a way that is standardized and generalizable.

'We are very individual': anticipated effects on stroke survivors of using their person-generated health data

BACKGROUND

Person-generated health data (PGHD) are produced by people when they use health information technologies. People who use PGHD may experience changes in their health and care process, such as engagement with their own healthcare, and their sense of social support and connectedness. Research into evaluating those reported effects has not kept up; thus, a method for measuring PGHD outcomes was previously designed and applied to the exemplar case of Kinect-based stroke rehabilitation systems. A key step of the method ensures that the patient's voice is included. Allowing stroke survivors to participate in the development and evaluation of health services and treatment can inform healthcare providers on decisions about stroke care, and thereby improve health outcomes.

OBJECTIVE

This paper presents the perspectives of stroke survivors and clinicians on the anticipated effects of stroke survivors' use of PGHD from a poststroke simulated rehabilitation technology.

METHODS

This study gathered the perspectives of stroke survivors and clinicians through three focus groups and three interviews, recruited for convenience. Participants were also asked questions intended to encourage them to comment on the initial items of the patient-reported outcome measure-PGHD. Deductive thematic analysis was performed.

RESULTS

This paper has further demonstrated that outcomes of using PGHD can be measured. For instance, stroke survivors described that using PGHD could result in positive, negative and nil effects on their health behaviours. Survivors and clinicians had varying perspectives in three of the six themes presented, and emphasise the importance of allowing stroke survivors to participate in the evaluation of digital health services.

Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics

BACKGROUND

An established and well-known method for usability assessment of various human-computer interaction technologies is called heuristic evaluation (HE). HE has been adopted for evaluations in a wide variety of specialized contexts and with objectives that go beyond usability. A set of heuristics to evaluate how health information technologies (HITs) incorporate features that enable effective patient use of person-generated health data (PGHD) is needed in an era where there is a growing demand and variety of PGHD-enabled technologies in health care and where a number of remote patient-monitoring technologies do not yet enable patient use of PGHD. Such a set of heuristics would improve the likelihood of positive effects from patients' use of PGHD and lower the risk of negative effects.

OBJECTIVE

This study aims to describe the development of a set of heuristics for the design and evaluation of how well remote patient therapeutic technologies enable patients to use PGHD (PGHD enablement). We used the case of Kinect-based stroke rehabilitation systems (K-SRS) in this study.

METHODS

The development of a set of heuristics to enable better use of PGHD was primarily guided by the R3C methodology. Closer inspection of the methodology reveals that neither its development nor its application to a case study were described in detail. Thus, where relevant, each step was grounded through best practice activities in the literature and by using Nielsen's heuristics as a basis for determining the new set of heuristics. As such, this study builds on the R3C methodology, and the implementation of a mixed process is intended to result in a robust and credible set of heuristics.

RESULTS

A total of 8 new heuristics for PGHD enablement in K-SRS were created. A systematic and detailed process was applied in each step of heuristic development, which bridged the gaps described earlier. It is hoped that this would aid future developers of specialized heuristics, who could apply the detailed process of heuristic development for other domains of technology, and additionally for the case of PGHD enablement for other health conditions. The R3C methodology was also augmented through the use of qualitative studies with target users and domain experts, and it is intended to result in a robust and credible set of heuristics, before validation and refinement.

CONCLUSIONS

This study is the first to develop a new set of specialized heuristics to evaluate how HITs incorporate features that enable effective patient use of PGHD, with K-SRS as a key case study. In addition, it is the first to describe how the identification of initial HIT features and concepts to enable PGHD could lead to the development of a specialized set of heuristics.