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Hacking health : how to make money and save lives in the healthtech world
Documents the roles and career priorities of key members of a typical HealthTech team in order to improve understanding of each team member's role; Discusses common pitfalls of HealthTech startups, including a detailed section on the regulatory processes surrounding healthcare; Examines the complex relationship between technology, entrepreneurship, and academia that are interwoven in any HealthTech venture. This book is a must-read guide for those entering the world of HealthTech startups. Author David Putrino, a veteran in the world of HealthTech and Telemedicine, details the roles, necessity, and values of key members of a typical HealthTech team, and helps readers understand the motivations and core priorities of all people involved. In ventures that typically depend upon effective communication between members from business, science, regulatory, and academic backgrounds, this book helps develop the core competencies that team members need to work harmoniously. Four detailed case studies are shared that exemplify the spectrum of HealthTech possibilities, including large corporations, tiny startups, elite athletes, and social good enterprises. Each case study shows how the success or failure of a project can hinge upon strong team dynamics, a deep understanding of the target population's needs and a strong awareness of each team member's long-term goals. This book is essential reading for entrepreneurs, scientists, clinicians, marketing and sales professionals, and all those looking to create new and previously unimagined possibilities for improving the lives of people everywhere.
Book
‘Fit-for-purpose?’ – challenges and opportunities for applications of blockchain technology in the future of healthcare
Blockchain is a shared distributed digital ledger technology that can better facilitate data management, provenance and security, and has the potential to transform healthcare. Importantly, blockchain represents a data architecture, whose application goes far beyond Bitcoin – the cryptocurrency that relies on blockchain and has popularized the technology. In the health sector, blockchain is being aggressively explored by various stakeholders to optimize business processes, lower costs, improve patient outcomes, enhance compliance, and enable better use of healthcare-related data. However, critical in assessing whether blockchain can fulfill the hype of a technology characterized as ‘revolutionary’ and ‘disruptive’, is the need to ensure that blockchain design elements consider actual healthcare needs from the diverse perspectives of consumers, patients, providers, and regulators. In addition, answering the real needs of healthcare stakeholders, blockchain approaches must also be responsive to the unique challenges faced in healthcare compared to other sectors of the economy. In this sense, ensuring that a health blockchain is ‘fit-for-purpose’ is pivotal. This concept forms the basis for this article, where we share views from a multidisciplinary group of practitioners at the forefront of blockchain conceptualization, development, and deployment.
Journal Article
Handbook of evaluation methods for health informatics
This Handbook provides a complete compendium of methods for evaluation of IT-based systems and solutions within healthcare. Emphasis is entirely on assessment of the IT-system within its organizational environment. The author provides a coherent and complete assessment of methods addressing interactions with and effects of technology at the organizational, psychological, and social levels.It offers an explanation of the terminology and theoretical foundations underlying the methodological analysis presented here. The author carefully guides the reader through the process of identifying relevant methods corresponding to specific information needs and conditions for carrying out the evaluation study. The Handbook takes a critical view by focusing on assumptions for application, tacit built-in perspectives of the methods as well as their perils and pitfalls.
*Collects a number of evaluation methods of medical informatics*Addresses metrics and measures*Includes an extensive list of anotated references, case studies, and a list of useful Web sites
eBook
Artificial intelligence of health-enabled spaces
\"Artificial Intelligence of Health-Enabled Spaces (AIoH) has made revolutionary advances in clinical studies that we know so far. Among these advances, intelligent and medical services are gaining lots of interest. Nowadays, AI-powered technologies are not only used in saving lives, but also in our daily life activities in diagnosing, controlling, and even tracking of COVID-19 patients. The AI-powered solutions are expected to communicate with cellular networks smoothly in the next generation networks (5G/6G and beyond) for more effective/critical medical applications. This will open the door for another interesting research areas. This book focuses on the development and analysis of Artificial Intelligence (AI) models applications across multi-disciplines. AI based deep learning models, fuzzy and hybrid intelligent systems, and intrinsic explainable model are also being presented in this book. Some of the fields considered in this smart health-oriented book includes AI applications in Electrical Engineering, Biomedical Engineering, Environmental Engineering, Computer Engineering, Education, Cyber Security, Chemistry, Pharmacy, Molecular Biology, and Tourism. This book is dedicated to addressing the major challenges in fighting diseases and psychological issues using AI. Challenges vary from cost and complexity to availability and accuracy. The aim of this book is hence to focus on both the design and implementation aspects of the AI-based approaches in proposed health-related solutions. Targeted readers are from varying disciplines who are interested in implementing the smart planet/environments vision via intelligent enabling technologies\"-- Provided by publisher.
Book
Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation
The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR).
The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes.
We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS.
We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]).
This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.
Journal Article
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
Background
Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice.
Methods
Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the “Principles of Biomedical Ethics” by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI.
Results
Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health.
Conclusions
To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward.
Journal Article
Alignment of Key Stakeholders’ Priorities for Patient-Facing Tools in Digital Health: Mixed Methods Study
There is widespread agreement on the promise of patient-facing digital health tools to transform health care. Yet, few tools are in widespread use or have documented clinical effectiveness.BACKGROUNDThere is widespread agreement on the promise of patient-facing digital health tools to transform health care. Yet, few tools are in widespread use or have documented clinical effectiveness.The aim of this study was to gain insight into the gap between the potential of patient-facing digital health tools and real-world uptake.OBJECTIVEThe aim of this study was to gain insight into the gap between the potential of patient-facing digital health tools and real-world uptake.We interviewed and surveyed experts (in total, n=24) across key digital health stakeholder groups-venture capitalists, digital health companies, payers, and health care system providers or leaders-guided by the Consolidated Framework for Implementation Research.METHODSWe interviewed and surveyed experts (in total, n=24) across key digital health stakeholder groups-venture capitalists, digital health companies, payers, and health care system providers or leaders-guided by the Consolidated Framework for Implementation Research.Our findings revealed that external policy, regulatory demands, internal organizational workflow, and integration needs often take priority over patient needs and patient preferences for digital health tools, which lowers patient acceptance rates. We discovered alignment, across all 4 stakeholder groups, in the desire to engage both patients and frontline health care providers in broader dissemination and evaluation of digital health tools. However, major areas of misalignment between stakeholder groups have stymied the progress of digital health tool uptake-venture capitalists and companies focused on external policy and regulatory demands, while payers and providers focused on internal organizational workflow and integration needs.RESULTSOur findings revealed that external policy, regulatory demands, internal organizational workflow, and integration needs often take priority over patient needs and patient preferences for digital health tools, which lowers patient acceptance rates. We discovered alignment, across all 4 stakeholder groups, in the desire to engage both patients and frontline health care providers in broader dissemination and evaluation of digital health tools. However, major areas of misalignment between stakeholder groups have stymied the progress of digital health tool uptake-venture capitalists and companies focused on external policy and regulatory demands, while payers and providers focused on internal organizational workflow and integration needs.Misalignment of the priorities of digital health companies and their funders with those of providers and payers requires direct attention to improve uptake of patient-facing digital health tools and platforms.CONCLUSIONSMisalignment of the priorities of digital health companies and their funders with those of providers and payers requires direct attention to improve uptake of patient-facing digital health tools and platforms.
Journal Article