Chat GPT digests a paper on med tech innovation. The paper, Greenhalgh 2017, has been cited at least 1200 times.
Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies
Background:
Many promising technological innovations in health and social care are characterized by nonadoption or abandonment by individuals or by failed attempts to scale up locally, spread distantly, or sustain the innovation long term at the organization or system level.
Objective:
Our objective was to produce an evidence-based, theory-informed, and pragmatic framework to help predict and evaluate the success of a technology-supported health or social care program.
Methods:
The study had 2 parallel components: (1) secondary research (hermeneutic systematic review) to identify key domains, and (2) empirical case studies of technology implementation to explore, test, and refine these domains. We studied 6 technology-supported programs—video outpatient consultations, global positioning system tracking for cognitive impairment, pendant alarm services, remote biomarker monitoring for heart failure, care organizing software, and integrated case management via data sharing—using longitudinal ethnography and action research for up to 3 years across more than 20 organizations. Data were collected at micro level (individual technology users), meso level (organizational processes and systems), and macro level (national policy and wider context). Analysis and synthesis was aided by sociotechnically informed theories of individual, organizational, and system change. The draft framework was shared with colleagues who were introducing or evaluating other technology-supported health or care programs and refined in response to feedback.
Results:
The literature review identified 28 previous technology implementation frameworks, of which 14 had taken a dynamic systems approach (including 2 integrative reviews of previous work). Our empirical dataset consisted of over 400 hours of ethnographic observation, 165 semistructured interviews, and 200 documents. The final nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework included questions in 7 domains: the condition or illness, the technology, the value proposition, the adopter system (comprising professional staff, patient, and lay caregivers), the organization(s), the wider (institutional and societal) context, and the interaction and mutual adaptation between all these domains over time. Our empirical case studies raised a variety of challenges across all 7 domains, each classified as simple (straightforward, predictable, few components), complicated (multiple interacting components or issues), or complex (dynamic, unpredictable, not easily disaggregated into constituent components). Programs characterized by complicatedness proved difficult but not impossible to implement. Those characterized by complexity in multiple NASSS domains rarely, if ever, became mainstreamed. The framework showed promise when applied (both prospectively and retrospectively) to other programs.
Conclusions:
Subject to further empirical testing, NASSS could be applied across a range of technological innovations in health and social care. It has several potential uses: (1) to inform the design of a new technology; (2) to identify technological solutions that (perhaps despite policy or industry enthusiasm) have a limited chance of achieving large-scale, sustained adoption; (3) to plan the implementation, scale-up, or rollout of a technology program; and (4) to explain and learn from program failures.
GREENHALGH 2017
J Med Internet Res 2017;19(11):e367
#### CHAT
This complex and visionary paper, authored by Trisha Greenhalgh and her team, presents a comprehensive framework for understanding and evaluating the challenges associated with the adoption, abandonment, and scaling of health and care technologies. The framework, known as NASSS (Nonadoption, Abandonment, Scale-up, Spread, and Sustainability), offers a multi-dimensional perspective on the factors influencing the success or failure of technology implementations in healthcare.
Key points and contributions of this paper:
The NASSS Framework: The NASSS framework is designed to address the complexities of technology adoption in healthcare. It emphasizes the need to consider multiple interacting domains, including the technology itself, the individual users, the organization, the broader context, and the ongoing dynamic nature of the implementation.
Complexity Classification: The framework introduces the concept of classifying each domain as simple, complicated, or complex. It suggests that it is the complexity in multiple domains that poses the greatest challenge to the widespread adoption and sustainability of health and care technologies.
Applications: The authors suggest various applications of the NASSS framework, including technology design, strategic planning, implementation projects, scale-up, and retrospective analysis of program failures. The framework provides valuable insights for stakeholders involved in healthcare technology, from clinicians and managers to technology developers and policymakers.
Interdisciplinary Collaboration: The paper highlights the importance of interdisciplinary collaboration, bringing together insights from various fields, including healthcare, technology, and social sciences, to address the multifaceted challenges of technology implementation.
Real-World Case Studies: The authors draw on real-world case studies to illustrate how the NASSS framework can be applied to understand the intricacies of technology adoption and sustainability in healthcare settings.
Global Applicability: While the empirical work behind NASSS was conducted in the United Kingdom, the framework is intended to have broader applicability, encouraging research groups worldwide to explore its relevance and adapt it to their contexts.
Access and Usability: The paper acknowledges the need to make the NASSS framework accessible beyond academia. Efforts are underway to develop accessible infographic summaries, web tools, and learning opportunities to ensure that the framework can be effectively used by healthcare practitioners, technology developers, and other stakeholders.
Policy Implications: The framework has implications for health policy and technology adoption strategies, emphasizing the importance of recognizing and addressing the complexities inherent in healthcare technology implementations.
In summary, this paper provides a groundbreaking framework that addresses the intricate challenges of health and care technology adoption and sustainability. It encourages a holistic and dynamic approach to understanding the factors influencing the success or failure of technology implementations, with the ultimate goal of improving healthcare delivery and outcomes.
User
Did they invent naSSS ?
ChatGPT
The NASSS framework, which stands for "Nonadoption, Abandonment, Scale-up, Spread, and Sustainability," was developed by Trisha Greenhalgh and her colleagues. While they did not invent the concept of studying technology adoption and implementation challenges, they created the NASSS framework as a specific and comprehensive approach to understanding and evaluating the complexities involved in adopting and sustaining health and care technologies.
The framework builds on existing research in the field of technology implementation but introduces novel elements, such as classifying domains as simple, complicated, or complex, and emphasizes the dynamic and multifaceted nature of the challenges. The NASSS framework represents a significant contribution to the field of healthcare technology implementation and has been widely recognized for its utility in guiding research and practice in this area.
User
How might this apply to new genomic tests? This is the area I work in as an MD MBA consultant.
ChatGPT
The NASSS framework can be applied effectively to assess the adoption, scale-up, spread, and sustainability of new genomic tests in healthcare settings. Here's how it might apply to this specific area of work:
Nonadoption: Identify why some healthcare providers or institutions might choose not to adopt new genomic tests. Possible barriers to adoption could include high costs, lack of reimbursement, limited clinical evidence supporting their effectiveness, or concerns about privacy and ethical issues related to genomic data.
Abandonment: Examine cases where genomic tests were initially implemented but later abandoned. Reasons for abandonment might include issues with test accuracy, difficulty integrating genomic data into clinical workflows, or challenges in interpreting and communicating complex genetic information to patients and providers.
Scale-up: Assess the potential for scaling up the use of genomic tests beyond initial pilot projects. Consider the scalability of the technology, the availability of trained personnel, and the infrastructure required for widespread adoption. Also, evaluate the willingness of healthcare organizations to invest in genomic testing capabilities.
Spread: Investigate factors that facilitate or hinder the spread of genomic tests across different healthcare settings and regions. Explore the role of regulatory frameworks, clinical guidelines, and professional networks in promoting the adoption of genomic testing. Identify successful strategies for sharing knowledge and best practices.
Sustainability: Examine the long-term sustainability of genomic testing programs. This involves assessing ongoing funding mechanisms, the ability to demonstrate value and clinical utility, and the integration of genomic information into routine clinical care. Consider how changes in healthcare policies and reimbursement models may impact sustainability.
Complexity: Recognize the inherent complexity of genomic testing, both in terms of the technology itself and the associated ethical, legal, and social considerations. Genomic tests often fall into the "complex" domain within the NASSS framework due to their multidimensional nature. This complexity may require customized approaches to implementation and ongoing support.
Multiple Domains: Recognize that the successful adoption of genomic tests involves multiple domains, including technological, organizational, social, and policy-related aspects. Each of these domains may present unique challenges and opportunities that need to be addressed for effective implementation.
Adaptation: Encourage healthcare organizations to adapt and tailor their approaches to genomic testing based on their specific contexts and needs. The NASSS framework emphasizes the importance of flexibility and adaptation to local conditions.
Patient Engagement: Consider the role of patient engagement and education in the adoption of genomic tests. Genomic information often has a direct impact on patients, and their understanding and willingness to participate in testing can significantly influence the success of implementation efforts.
Collaboration: Promote collaboration among various stakeholders, including healthcare providers, genetic counselors, researchers, policymakers, and patients, to address the multifaceted challenges associated with genomic testing.
In summary, the NASSS framework provides a structured approach to understanding and addressing the complexities of adopting and sustaining genomic tests in healthcare. By systematically evaluating the factors within each domain, healthcare organizations and consultants like yourself can develop informed strategies for successful implementation and integration of genomic testing into clinical practice.
User
How might this framework apply to digital pathology? This is an area I work in as an MD MBA consultant.
ChatGPT
The NASSS framework can be effectively applied to assess the adoption, scale-up, spread, and sustainability of digital pathology in healthcare. Here's how this framework might apply to your work in the field of digital pathology:
Nonadoption:
Identify reasons why some healthcare institutions or laboratories may choose not to adopt digital pathology solutions. This could include concerns about the initial costs of digitization, resistance to change among pathologists, or uncertainties regarding the regulatory landscape for digital pathology.
Abandonment:
Investigate cases where digital pathology solutions were initially implemented but later abandoned. Reasons for abandonment might include technical issues with the digital platform, challenges in integrating digital pathology into existing workflows, or difficulties in ensuring the accuracy and security of digital slide images.
Scale-up:
Assess the potential for scaling up the use of digital pathology across a broader range of healthcare settings. Consider factors such as the scalability of digital slide scanning technology, the availability of trained pathologists and support staff, and the infrastructure required for widespread adoption.
Spread:
Examine the factors that facilitate or hinder the spread of digital pathology solutions to different healthcare institutions and regions. This might involve exploring regulatory requirements, standardization efforts, and collaborative networks that promote knowledge sharing and best practices.
Sustainability:
Evaluate the long-term sustainability of digital pathology programs. This includes assessing ongoing operational costs, demonstrating the clinical and economic benefits of digital pathology, and adapting to changes in healthcare policies and reimbursement models.
Complexity:
Recognize the inherent complexity of digital pathology, particularly in terms of the technology itself (e.g., whole-slide imaging systems) and the need for comprehensive quality assurance and data management. Digital pathology often falls into the "complex" domain within the NASSS framework.
Multiple Domains:
Acknowledge that the successful adoption of digital pathology involves multiple domains, including technology, organization, workflow, regulation, and clinical practice. Each domain may present unique challenges that need to be addressed for effective implementation.
Adaptation:
Encourage healthcare institutions to adapt digital pathology solutions to their specific needs and contexts. Flexibility and customization are essential, as different organizations may have varying levels of digital maturity and infrastructure.
Stakeholder Engagement:
Engage with a wide range of stakeholders, including pathologists, laboratory technicians, IT professionals, regulatory bodies, and healthcare administrators, to ensure a holistic approach to digital pathology implementation.
Data Privacy and Security:
Pay special attention to data privacy and security concerns, as digital pathology involves the handling of sensitive patient data. Ensuring compliance with data protection regulations and maintaining the integrity and confidentiality of digital pathology images and reports is crucial.
Training and Education:
Address the need for training and education programs to prepare pathologists and laboratory staff for the transition to digital pathology. This includes proficiency in digital slide interpretation and the use of digital pathology software.
By systematically evaluating these factors within each domain, healthcare institutions and consultants like yourself can develop informed strategies for the successful adoption and integration of digital pathology into clinical practice. This framework can help identify barriers and facilitators to digital pathology implementation and guide decision-making processes in this rapidly evolving field.
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What are key ways that the NASSS framework and the work of Greenhalgh et al. apply (1) similarly and (2) differently, to Genomics and to Digital Pathology?
ChatGPT
The NASSS framework and the work of Greenhalgh et al. can be applied to both genomics and digital pathology, but there are differences in how they apply to these two domains. Here's an overview of the key ways in which they apply similarly and differently to genomics and digital pathology:
Similarities (1) - Genomics and Digital Pathology:
Complexity:
Both genomics and digital pathology are highly complex domains within healthcare. Genomic testing involves analyzing an individual's entire genetic makeup, while digital pathology encompasses the digitization of tissue samples for diagnosis. These complexities pose challenges related to technology, data management, and clinical integration.
Multiple Domains:
Both domains involve multiple domains, such as technology, organization, regulation, clinical practice, and workflow. These domains interact and influence the adoption and implementation of genomics and digital pathology solutions.
Adaptation:
The need for adaptation is common to both domains. Healthcare institutions must adapt genomics and digital pathology solutions to their specific contexts and infrastructure. Tailoring these technologies to fit within existing healthcare workflows and practices is crucial for successful adoption.
Stakeholder Engagement:
Both genomics and digital pathology require engagement with a diverse group of stakeholders, including healthcare professionals, laboratory staff, IT experts, patients, and regulatory bodies. Collaboration and communication among these stakeholders are essential for successful implementation.
Differences (2) - Genomics and Digital Pathology:
Nature of Technology:
Genomics: Genomic testing primarily involves analyzing an individual's DNA to identify genetic variations and their implications for health. It is a data-driven field heavily reliant on bioinformatics, high-throughput sequencing, and data storage and analysis. Challenges often revolve around data management, interpretation, and the integration of genomic data into clinical decision-making.
Digital Pathology: Digital pathology involves the imaging and analysis of tissue samples, such as slides containing cells or tissues. It focuses on visual data, requiring high-quality imaging and storage systems. Challenges in digital pathology often center on image quality, standardization, and the integration of digital images into pathology practice.
Data Privacy and Security:
Genomics: Genomic data is highly sensitive and personal. Protecting patient privacy and ensuring data security are paramount. Challenges include obtaining informed consent for genetic testing, data anonymization, and complying with strict data protection regulations.
Digital Pathology: While digital pathology also involves sensitive patient data (i.e., medical images), the data privacy and security challenges are distinct from genomics. Ensuring the confidentiality and integrity of digital pathology images, as well as secure transmission and storage, is essential.
Clinical Integration:
Genomics: Integrating genomic information into clinical practice is a complex process. It often requires changes in healthcare workflows, education of healthcare providers, and establishing clinical utility and guidelines for genetic testing.
Digital Pathology: Integrating digital pathology into clinical workflows involves the adoption of digital slide viewing systems, training pathologists in digital interpretation, and optimizing diagnostic accuracy. It also requires addressing regulatory considerations for digital pathology reporting.
Regulatory Landscape:
Genomics: Genomic testing is subject to specific regulatory frameworks, including those governing genetic testing, laboratory certification, and genetic counseling. These regulations can vary by region and country.
Digital Pathology: Digital pathology has its own set of regulatory considerations, including requirements for the validation of digital systems, the use of digital images for diagnosis, and compliance with medical device regulations in some cases.
In summary, both genomics and digital pathology share similarities in terms of complexity, multiple domains, adaptation, and stakeholder engagement. However, the specific challenges, technologies, and regulatory landscapes in these two domains differ significantly. Applying the NASSS framework and the insights from Greenhalgh et al.'s work to each domain would involve tailoring the analysis to address these unique aspects while considering the common principles of adoption, scale-up, spread, and sustainability.
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Some other work of GREENHALGH
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https://scite.ai/reports/beyond-adoption-a-new-framework-68lywb1?sort=engagement
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Milbank Q
. 2023 May 23. doi: 10.1111/1468-0009.12656. Online ahead of print.
Toward a Values-Informed Approach to Complexity in Health Care: Hermeneutic Review
Trisha Greenhalgh 1, Eivind Engebretsen 2, Roland Bal 3, Sofia Kjellström 4
Affiliations expand
PMID: 37219239 DOI: 10.1111/1468-0009.12656
Abstract
Policy Points The concept of value complexity (complexity arising from differences in people's worldviews, interests, and values, leading to mistrust, misunderstanding, and conflict among stakeholders) is introduced and explained. Relevant literature from multiple disciplines is reviewed. Key theoretical themes, including power, conflict, language and framing, meaning-making, and collective deliberation, are identified.
Simple rules derived from these theoretical themes are proposed.
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Medical Masks Versus N95 Respirators for Preventing COVID-19 Among Health Care WorkersFREE
Trisha Greenhalgh, PhD, Danielle J. Cane, MSc, Matthew Oliver, MEng
Author, Article, and Disclosure Information
https://doi.org/10.7326/L23-0072
COMMENTS
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TO THE EDITOR: We read Loeb and colleagues' article (1) with interest. Some members of the evidence-based medicine community seem to assume that a randomized controlled trial, however imperfectly and illogically designed, is necessarily superior to other forms of evidence. This is not the case. Showing causality requires multiple types of evidence, including probabilistic (such as from controlled experiments) and mechanistic (that is, evidence supporting—or if causality is to be disproved, evidence refuting—the purported mechanism of action).
Masking is a complex intervention. Its mechanism of action should have been taken into account when designing randomized controlled trials. A predominantly airborne transmission route for SARS-CoV-2 has been well established by multiple studies of different kinds. The mechanism by which respirators (for example, N95s) work to filter out airborne particles is also well established (2). Ferris and associates' study in a U.K. hospital showed a dramatic reduction in health care worker infections after introduction of respirator-grade respiratory protection (3), and a meta-analysis of 8 studies (9164 participants) showed that respirator use was associated with significantly fewer viral infectious episodes for health care workers compared with use of surgical masks (4).
The airborne nature of SARS-CoV-2 means that health care workers may be exposed to the virus even when not directly caring for infected patients simply by breathing contaminated air. A valid comparison of respirators and medical masks in a hospital environment would therefore require respiratory protection to be worn continuously from entering to leaving the building. Instead, Loeb and colleagues instructed participants to wear the masks or respirators only when dealing with patients who had known or suspected COVID-19—a design that presupposes an exclusively close-contact mode of transmission (that is, contact or droplet). A plausible explanation for their findings is that health care workers became infected from the air after removing their respirators or masks. A systematic review showed that continuous wearing of N95 respirators during a health care worker's shift was associated with significant risk reduction in viral respiratory infections (5).
Loeb and colleagues' findings are heavily skewed by data from Egypt collected at the time of a local outbreak of the highly contagious Omicron variant. An alternative explanation for the high rates of infection in both groups of the Egyptian study is that many participants were infected at home or in the community rather than in the workplace. This would bias findings toward the null.
We were surprised that the editorial and peer-review process did not pick up these basic design flaws, which could explain the negative findings.
