Dataset Information

Which one? A suggested approach for evaluating digital health maturity models.

ABSTRACT:

Background

Digital health maturity models allow healthcare organizations to evaluate digital health capability and to develop roadmaps for improving patient care through technology. There are many models available commercially for healthcare providers to use to assess their digital health maturity. Currently, there are limited evidence-based methods to assess the quality, utility, and efficacy of maturity models to select the most appropriate model for the given context.

Objective

To develop a framework to assess digital maturity models and facilitate recommendations for digital maturity model selection.

Methods

A systematic, consultative, and iterative process was used. Literature analyses and a stakeholder needs analysis (n = 23) was conducted to develop content and design considerations. These considerations were incorporated into the initial version of the framework developed by researchers in a design workshop. External stakeholder review (n = 20) and improvements strengthened and finalized the framework.

Results

The criteria of the framework include assessment of healthcare context, feasibility, integrity, completeness and actionability. Users can compare model performance in order to select the most appropriate model for their context.

Conclusion

The framework provides healthcare stakeholders with a consistent and objective methodology to compare digital health maturity models, informing approaches to choosing a suitable model. This is a critical step as healthcare evolves towards a digital health system focused on improving the quality of care, reducing costs and improving the provider and consumer experience.

SUBMITTER: Woods L

PROVIDER: S-EPMC9731136 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Which one? A suggested approach for evaluating digital health maturity models.

Woods Leanna L Eden Rebekah R Duncan Rhona R Kodiyattu Zack Z Macklin Sophie S Sullivan Clair C

Frontiers in digital health 20221124

<h4>Background</h4>Digital health maturity models allow healthcare organizations to evaluate digital health capability and to develop roadmaps for improving patient care through technology. There are many models available commercially for healthcare providers to use to assess their digital health maturity. Currently, there are limited evidence-based methods to assess the quality, utility, and efficacy of maturity models to select the most appropriate model for the given context.<h4>Objective</h4 ...[more]

PMID: 36506845

Similar Datasets

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Project description:BackgroundOne of the essential elements of a strategic approach to improving patients' experience is to measure and report on patients' experiences in real time. Real-time feedback (RTF) is increasingly being collected using digital technology; however, there are several factors that may influence the success of the digital system.ObjectiveThe aim of this review was to evaluate the digital maturity and patient acceptability of real-time patient experience feedback systems.MethodsWe systematically searched the following databases to identify papers that used digital systems to collect RTF: The Cochrane Library, Global Health, Health Management Information Consortium, Medical Literature Analysis and Retrieval System Online, EMBASE, PsycINFO, Web of Science, and CINAHL. In addition, Google Scholar and gray literature were utilized. Studies were assessed on their digital maturity using a Digital Maturity Framework on the basis of the following 4 domains: capacity/resource, usage, interoperability, and impact. A total score of 4 indicated the highest level of digital maturity.ResultsRTF was collected primarily using touchscreens, tablets, and Web-based platforms. Implementation of digital systems showed acceptable response rates and generally positive views from patients and staff. Patient demographics according to RTF responses varied. An overrepresentation existed in females with a white predominance and in patients aged ≥65 years. Of 13 eligible studies, none had digital systems that were deemed to be of the highest level of maturity. Three studies received a score of 3, 2, and 1, respectively. Four studies scored 0 points. While 7 studies demonstrated capacity/resource, 8 demonstrated impact. None of the studies demonstrated interoperability in their digital systems.ConclusionsPatients and staff alike are willing to engage in RTF delivered using digital technology, thereby disrupting previous paper-based feedback. However, a lack of emphasis on digital maturity may lead to ineffective RTF, thwarting improvement efforts. Therefore, given the potential benefits of RTF, health care services should ensure that their digital systems deliver across the digital maturity continuum.

Project description:BackgroundProfound scientific evaluation of novel digital health technologies (DHTs) is key to enhance successful development and implementation. As such, we previously developed the eHealth evaluation cycle. The eHealth evaluation cycle contains 5 consecutive study phases: conceptual, development, feasibility, effectiveness, and implementation.ObjectiveThe aim of this study is to develop a better understanding of the daily practice of the eHealth evaluation cycle. Therefore, the objectives are to conduct a structured analysis of literature data to analyze the practice of the evaluation study phases and to determine which evaluation approaches are used in which study phase of the eHealth evaluation cycle.MethodsWe conducted a systematic literature search in PubMed including the MeSH term "telemedicine" in combination with a wide variety of evaluation approaches. Original peer-reviewed studies published in the year 2019 (pre-COVID-19 cohort) were included. Nonpatient-focused studies were excluded. Data on the following variables were extracted and systematically analyzed: journal, country, publication date, medical specialty, primary user, functionality, evaluation study phases, and evaluation approach. RStudio software was used to summarize the descriptive data and to perform statistical analyses.ResultsWe included 824 studies after 1583 titles and abstracts were screened. The majority of the evaluation studies focused on the effectiveness (impact; 304/824, 36.9%) study phase, whereas uptake (implementation; 70/824, 8.5%) received the least focus. Randomized controlled trials (RCTs; 170/899, 18.9%) were the most commonly used DHT evaluation method. Within the effectiveness (impact) study phase, RCTs were used in one-half of the studies. In the conceptual and planning phases, survey research (27/78, 35%) and interview studies (27/78, 35%) were most frequently used. The United States published the largest amount of DHT evaluation studies (304/824, 36.9%). Psychiatry and mental health (89/840, 10.6%) and cardiology (75/840, 8.9%) had the majority of studies published within the field.ConclusionsWe composed the first comprehensive overview of the actual practice of implementing consecutive DHT evaluation study phases. We found that the study phases of the eHealth evaluation cycle are unequally studied and most attention is paid to the effectiveness study phase. In addition, the majority of the studies used an RCT design. However, in order to successfully develop and implement novel DHTs, stimulating equal evaluation of the sequential study phases of DHTs and selecting the right evaluation approach that fits the iterative nature of technology might be of the utmost importance.

Project description:BackgroundDigital public health (DiPH) interventions may help us tackle substantial public health challenges and reach historically underserved populations, in addition to presenting valuable opportunities to improve and complement existing services. However, DiPH interventions are often triggered through technological advancements and opportunities rather than public health needs. To develop and evaluate interventions designed to serve public health needs, a comprehensive framework is needed that systematically covers all aspects with relevance for public health. This includes considering the complexity of the technology, the context in which the technology is supposed to operate, its implementation, and its effects on public health, including ethical, legal, and social aspects.ObjectiveWe aimed to develop such a DiPH framework with a comprehensive list of core principles to be considered throughout the development and evaluation process of any DiPH intervention.MethodsThe resulting digital public health framework (DigiPHrame) was based on a scoping review of existing digital health and public health frameworks. After extracting all assessment criteria from these frameworks, we clustered the criteria. During a series of multidisciplinary meetings with experts from the Leibniz ScienceCampus Digital Public Health, we restructured each domain to represent the complexity of DiPH. In this paper, we used a COVID-19 contact-tracing app as a use case to illustrate how DigiPHrame may be applied to assess DiPH interventions.ResultsThe current version of DigiPHrame consists of 182 questions nested under 12 domains. Domain 1 describes the current status of health needs and existing interventions; domains 2 and 3, the DiPH technology under assessment and aspects related to human-computer interaction, respectively; domains 4 and 5, structural and process aspects, respectively; and domains 6-12, contextual conditions and the outcomes of the DiPH intervention from broad perspectives. In the CWA use case, a number of questions relevant during its development but also important for assessors once the CWA was available were highlighted.ConclusionsDigiPHrame is a comprehensive framework for the development and assessment of digital technologies designed for public health purposes. It is a living framework and will, therefore, be updated regularly and as new public health needs and technological advancements emerge.

Dataset Information

Which one? A suggested approach for evaluating digital health maturity models.

Background

Objective

Methods

Results

Conclusion

Publications

Which one? A suggested approach for evaluating digital health maturity models.

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