Data Science in Healthcare

Bhavnani, Sanjeev P.; Muñoz, Daniel; Bagai, Akshay

doi:10.1161/circoutcomes.116.003081

Cited by 19 publications

(7 citation statements)

References 19 publications

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“…With this paradigm, clinician data scientists need a fundamental understanding about health data, training on epidemiology, statistics, bioinformatics, and computer science combined with an understanding of continuous healthcare improvement frameworks, socio-technical system challenges, and advanced skills in interdisciplinary communication and collaboration. Understanding the types and characteristics of heterogenic healthcare-related data, the construction of diverse databases, and data governance (data quality, data security, ethics, and law) are essential skills for clinician data scientists to consolidate achievable scientific questions and prepare for the interdisciplinary investigations [3][4][5]. Meanwhile, training in the tools for statistical modeling, machine learning, nature language processing, knowledge management and data visualization will further position the clinician data scientists for success to design scientifically rigorous studies and apply up-to-date data analytics [6].…”

Section: Core Competenciesmentioning

confidence: 99%

Clinician Data Scientists—Preparing for the Future of Medicine in the Digital World

Wang

Moulton

et al. 2022

Health Data Sci

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Section: Core Competenciesmentioning

confidence: 99%

Clinician Data Scientists—Preparing for the Future of Medicine in the Digital World

Wang

Moulton

et al. 2022

Health Data Sci

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“…Data are essential for tracking and monitoring progress on health-related sustainable development goals (SDGs) 1–3. While data and data analytics are being used in high income countries to improve health equity, health outcomes and continuously inform healthcare systems, their use in low and middle-income countries (LMICs) is lagging 4–8. Investing in data ecosystems represents an important opportunity for monitoring and quickening progress on health-related SDGs in LMICs 1 8–10…”

Section: Introductionmentioning

confidence: 99%

Health data ecosystem in Pakistan: a multisectoral qualitative assessment of needs and opportunities

Mahmood,

Noorali,

Manji

et al. 2023

BMJ Open

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ObjectiveData are essential for tracking and monitoring of progress on health-related sustainable development goals (SDGs). But the capacity to analyse subnational and granular data is limited in low and middle-income countries. Although Pakistan lags behind on achieving several health-related SDGs, its health information capacity is nascent. Through an exploratory qualitative approach, we aimed to understand the current landscape and perceptions on data in decision-making among stakeholders of the health data ecosystem in Pakistan.DesignWe used an exploratory qualitative study design.SettingThis study was conducted at the Aga Khan University, Karachi, Pakistan.ParticipantsWe conducted semistructured, in-depth interviews with multidisciplinary and multisectoral stakeholders from academia, hospital management, government, Non-governmental organisations and other relevant private entities till thematic saturation was achieved. Interviews were recorded and transcribed, followed by thematic analysis using NVivo.ResultsThematic analysis of 15 in-depth interviews revealed three major themes: (1) institutions are collecting data but face barriers to its effective utilisation for decision-making. These include lack of collection of needs-responsive data, lack of a gender/equity in data collection efforts, inadequate digitisation, data reliability and limited analytical ability; (2) there is openness and enthusiasm for sharing data for advancing health; however, multiple barriers hinder this including appropriate regulatory frameworks, platforms for sharing data, interoperability and defined win-win scenarios; (3) there is limited capacity in the area of both human capital and infrastructure, for being able to use data to advance health, but there is appetite to improve and invest in capacity in this area.ConclusionsOur study identified key areas of focus that can contribute to orient a national health data roadmap and ecosystem in Pakistan.

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“…The convergence of information technology and medicine in our dynamic digital era has produced novel applicable solutions for clinical practice [1]. Health care is at an important turning point for the efficient and safe use of artificial intelligence (AI) technologies to transform the quality of care delivered [2].…”

Section: Introductionmentioning

confidence: 99%

Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach

Wiljer¹,

Salhia²,

Dolatabadi³

et al. 2021

JMIR Res Protoc

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Background Significant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today’s health care providers. Objective The aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system. Methods To accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature. Results The environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI. Conclusions Technologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. International Registered Report Identifier (IRRID) PRR1-10.2196/30940

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Data Science in Healthcare

Cited by 19 publications

References 19 publications

Clinician Data Scientists—Preparing for the Future of Medicine in the Digital World

Clinician Data Scientists—Preparing for the Future of Medicine in the Digital World

Health data ecosystem in Pakistan: a multisectoral qualitative assessment of needs and opportunities

Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach

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