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Digital innovation evaluation: user perceptions of innovation readiness, digital confidence, innovation adoption, user experience and behaviour change.

ABSTRACT: BACKGROUND:Innovation spread is a key policy objective for health systems world-wide, but adoption success varies enormously. We have developed a set of short generic user-reported measures to help understand how and why healthcare innovations spread. This work builds on the literature and on practical experience in developing and using patient-reported outcome measures. MEASURES:The Innovation Readiness Score measures user perceptions of how much they are open to and up-to-date with new ideas, and whether their organisations are receptive to and capable of innovation. It is based on Rogers' classification of innovativeness (innovator, early adopter, early majority, etc).The Digital Confidence Score rates users' digital literacy and confidence to use digital products, with dimensions of familiarity, social pressure, support and digital self-efficacy.The Innovation Adoption Score rates the adoption process in terms of coherence and reflective thought before, during and after implementation. It is based on Normalisation Process Theory.The User Satisfaction measure assesses a digital product in terms of usefulness, ease of use, support and satisfaction.The Behaviour Change measure covers user perceptions of their capability, opportunity and motivation to change behaviour, based on the COM-B model.These measures have been mapped onto Greenhalgh's NASSS Framework (non-adoption, abandonment and challenges to scale-up, spread and sustainability of health and care technologies). CONCLUSION:These tools measure different aspects of digital health innovations and may help predict the success of innovation dissemination, diffusion and spread programmes.

SUBMITTER: Benson T

PROVIDER: S-EPMC7062319 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Digital innovation evaluation: user perceptions of innovation readiness, digital confidence, innovation adoption, user experience and behaviour change.

Benson Tim T

BMJ health & care informatics 20190401 1

<h4>Background</h4>Innovation spread is a key policy objective for health systems world-wide, but adoption success varies enormously. We have developed a set of short generic user-reported measures to help understand how and why healthcare innovations spread. This work builds on the literature and on practical experience in developing and using patient-reported outcome measures.<h4>Measures</h4>The <i>Innovation Readiness Score</i> measures user perceptions of how much they are open to and up-to ...[more]

PMID: 31039121

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Project description:BackgroundEuropean epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens.MethodsWe conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software.ResultsOur analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics.ConclusionsThe study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

Project description:BackgroundThe last few decades have witnessed significant advances in the development of digital tools and applications for mental health care. Despite growing evidence for their effectiveness, acceptance and use of these tools in clinical practice remain low. Hence, a validated and easy-to-use instrument for assessing professionals' readiness to adopt eMental health (EMH) is necessary to gain further insights into the process of EMH adoption and facilitate future research on this topic.ObjectiveThe aim of this study is to develop and validate an instrument for assessing mental health care professionals' readiness to adopt EMH.MethodsItem generation was guided by literature and inputs from mental health care professionals and experts in survey development. Exploratory factor analyses were conducted on an initial set of 29 items completed by a sample of mental health care professionals (N=432); thereafter, the scale was reduced to 15 items in an iterative process. The factor structure thus obtained was subsequently tested using a confirmatory factor analysis with a second sample of mental health care professionals (N=363). The internal consistency, convergent validity, and predictive validity of the eMental Health Adoption Readiness (eMHAR) Scale were assessed.ResultsExploratory factor analysis resulted in a 3-factor solution with 15 items. The factors were analyzed and labeled as perceived benefits and applicability of EMH, EMH proactive innovation, and EMH self-efficacy. These factors were confirmed through a confirmatory factor analysis. The total scale and subscales showed a good internal consistency (Cronbach α=.73-.88) along with acceptable convergent and predictive relationships with related constructs.ConclusionsThe constructed eMHAR Scale showed a conceptually interpretable 3-factor structure having satisfactory characteristics and relationships with relevant concepts. Its ease of use allows for quick acquisition of data that can contribute to understanding and facilitating the process of adoption of EMH by clinical professionals.

Project description:BACKGROUND:Following the successful pilot of the community-based distribution of injectable contraceptives (CBDIC) by community health extension workers (CHEWs) in Gombe, northern Nigeria in 2010, there was a policy decision to scale-up the innovation to other parts of the country. However, there is limited understanding of health system factors that may facilitate or impede the successful scale-up of this innovation beyond the pilot site. Thus, this study assessed the health system readiness to deliver CBDIC in Nigeria and how this may influence the scale-up process. METHODS:This study was conducted in two Local Government Areas in Gombe State in September 2016. Seven key informant interviews were held with purposively sampled senior officials of the ministries of health at the federal and state levels as well as NGO program managers. Also, 10 in-depth interviews were carried out with health workers. All transcripts were analyzed using the thematic framework analysis approach. RESULT:The availability of a policy framework that supports task-shifting and task-sharing, as well as application of evidence from the pilot programme and capacity building programmes for health workers provided a favourable environment for scale-up. Health system challenges for the scale-up process included insufficient community health workers, resistance to the task-shifting policy from professional health groups (who should support the CHEWs), limited funding and poor logistics management which affected commodity distribution and availability. However, there were also a number of health worker innovations which kept the scale-up going. Health workers sometimes used personal resources to make up for logistics failures and poor funding. They often modify the process in order to adapt to the realities on the ground. CONCLUSION:This study shows health system weaknesses that may undermine scale-up of CBDIC. The study also highlights what happens when scale-up is narrowly focused on the intervention without considering system context, capacity and readiness. However, agency and discretionary decision-making among frontline health workers facilitated the process of scaling up, although the sustainability of this is questionable. Benefits observed during the pilot may not be realised on a larger scale if health system challenges are not addressed.

Project description:BackgroundDigital health has the potential to support care delivery for chronic illness. Despite positive evidence from localized implementations, new technologies have proven slow to become accepted, integrated, and routinized at scale.ObjectiveThe aim of our study was to examine barriers and facilitators to implementation of digital health at scale through the evaluation of a £37m national digital health program: ‟Delivering Assisted Living Lifestyles at Scale" (dallas) from 2012-2015.MethodsThe study was a longitudinal qualitative, multi-stakeholder, implementation study. The methods included interviews (n=125) with key implementers, focus groups with consumers and patients (n=7), project meetings (n=12), field work or observation in the communities (n=16), health professional survey responses (n=48), and cross program documentary evidence on implementation (n=215). We used a sociological theory called normalization process theory (NPT) and a longitudinal (3 years) qualitative framework analysis approach. This work did not study a single intervention or population. Instead, we evaluated the processes (of designing and delivering digital health), and our outcomes were the identified barriers and facilitators to delivering and mainstreaming services and products within the mixed sector digital health ecosystem.ResultsWe identified three main levels of issues influencing readiness for digital health: macro (market, infrastructure, policy), meso (organizational), and micro (professional or public). Factors hindering implementation included: lack of information technology (IT) infrastructure, uncertainty around information governance, lack of incentives to prioritize interoperability, lack of precedence on accountability within the commercial sector, and a market perceived as difficult to navigate. Factors enabling implementation were: clinical endorsement, champions who promoted digital health, and public and professional willingness.ConclusionsAlthough there is receptiveness to digital health, barriers to mainstreaming remain. Our findings suggest greater investment in national and local infrastructure, implementation of guidelines for the safe and transparent use and assessment of digital health, incentivization of interoperability, and investment in upskilling of professionals and the public would help support the normalization of digital health. These findings will enable researchers, health care practitioners, and policy makers to understand the current landscape and the actions required in order to prepare the market and accelerate uptake, and use of digital health and wellness services in context and at scale.

Project description:BackgroundDigital harm-reduction interventions typically focus on people with severe drug-use problems, yet these interventions have moderate effectiveness on drug-users with lower levels of risk of harm. The difference in effectiveness may be explained by differences in behavioural patterns between the two groupings. Harnessing behavioural theories to understand what is at the core of drug-use behaviours and mapping the content of new interventions, may improve upon the effectiveness of interventions for lower-risk drug-users. To the best of our knowledge, this is the first study to systematically apply the Behaviour Change Wheel (BCW) approach to understand the components, influencing capabilities, opportunities, and motivations (COM-B) of higher education students to change their drug-use behaviors. It is also the first study which identifies specific patterns of behaviours that are more responsive to harm reduction practices through the use of the Theoretical Domain Framework (TDF).MethodsWe employed an explanatory sequential mix-method design. We first conducted an on-line survey and a Delphi exercise to understand the factors influencing COM-B components of higher education students to change their drug-use. Subsequently, we mapped all evidence onto the COM-B components and the TDF domains to identify clusters of behaviours to target for change, using a pattern-based discourse analysis. Finally, a series of multidisciplinary group meetings identified the intervention functions-the means by which the intervention change targeted behaviours and the Behavioural Change Techniques (BCTs) involved using the behaviour change technique taxonomy (v.1).ResultsTwenty-nine BCTs relevant to harm-reduction practices were identified and mapped across five intervention functions (education, modelling, persuasion, incentivization, and training) and five policy categories (communication/marketing, guidelines, regulation, service provision, and environmental/social planning). These BCTs were distributed across eight identified saturated clusters of behaviours MyUSE intervention attempts to change.ConclusionsThe BCTs, identified, will inform the development of a digitally delivered behaviour change intervention that focuses on increasing mindful decision-making with respect to drug-use and promotes alternatives to drug-use activities. The findings can also inform implementation scientists in applying context-specific harm-reduction practices in higher education. We present examples of how the eight identified clusters of target behaviours are mapped across the COM-B components and the TDF, along with suggestions of implementation practices for harm reduction at student population level.

Project description:Digital interventions often suffer from low usage, which may reflect insufficient attention to user experience. Moreover, the existing evaluation methods have limited applicability in the remote study of user experience of complex interventions that have expansive content and that are used over an extensive period of time. To alleviate these challenges, we describe here a novel qualitative Ecological Momentary Assessment (EMA) method: the CORTO method (Contextual, One-item, Repeated, Timely, Open-ended). We used it to gather digital intervention user experience data from Finnish adults (n = 184) who lived with interview-confirmed major depressive disorder (MDD) and took part in a randomized controlled trial (RCT) that studied the efficacy of a novel 12-week game-based digital intervention for depression. A second dataset on user experience was gathered with retrospective interviews (n = 22). We inductively coded the CORTO method and retrospective interview data, which led to four user experience categories: (1) contextual use, (2) interaction-elicited emotional experience, (3) usability, and (4) technical issues. Then, we used the created user experience categories and Template Analysis to analyze both datasets together, and reported the results qualitatively. Finally, we compared the two datasets with each other. We found that the data generated with the CORTO method offered more insights into usability and technical categories than the interview data that particularly illustrated the contextual use. The emotional valence of the interview data was more positive compared with the CORTO data. Both the CORTO and interview data detected 55 % of the micro-level categories; 20 % of micro-level categories were only detected by the CORTO data and 25 % only by the interview data. We found that the during-intervention user experience measurement with the CORTO method can provide intervention-specific insights, and thereby further the iterative user-centered intervention development. Overall, these findings highlight the impact of evaluation methods on the categories and qualities of insights acquired in intervention research.

Dataset Information

Digital innovation evaluation: user perceptions of innovation readiness, digital confidence, innovation adoption, user experience and behaviour change.

Publications

Digital innovation evaluation: user perceptions of innovation readiness, digital confidence, innovation adoption, user experience and behaviour change.

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