Digital Health Data Quality Issues: Systematic Review

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

The Anonymous Data Warehouse: A Hands-On Framework for Anonymizing Data From Digital Health Applications

The digital health space is growing rapidly, and so is the interest in sharing anonymized health data. However, data anonymization techniques have yet to see much coverage in the medical literature. The purpose of this article is, therefore, to provide a practical framework for anonymization with a focus on the unique properties of data from digital health applications. Literature trends, as well as common anonymization techniques, were synthesized into a framework that considers the opportunities and challenges of digital health data. A rationale for each design decision is provided, and the advantages and disadvantages are discussed. We propose a framework based on storing data separately, anonymizing the data where the identified data is located, only exporting selected data, minimizing static attributes, ensuring k-anonymity of users and their static attributes, and preventing defined metrics from acting as quasi-identifiers by using aggregation, rounding, and capping. Data anonymization requires a pragmatic approach that preserves the utility of the data while minimizing reidentification risk. The proposed framework should be modified according to the characteristics of the respective data set.

Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews

BACKGROUND

Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data.

OBJECTIVE

This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework.

METHODS

A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English.

RESULTS

A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions.

CONCLUSIONS

The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.

Real world challenges in maintaining data integrity in electronic health records in a cancer program

Electronic Health Record (EHR) systems increase clerical workload, promote copy-paste and error propagation. Documentation error rate in cancer diagnosis and treatment was examined in 776 patient records. Fifteen percent of the charts contained an error. Modern EHR systems, patient portals and engagement tools may facilitate the maintenance of accurate information.

Digital health integration for noncommunicable diseases: Comprehensive process mapping for full-life-cycle management

AIM

To create a systematic digital health process mapping framework for full-life-cycle noncommunicable disease management grounded in key stakeholder engagement.

METHODS

A triphasic, qualitative methodology was employed to construct a process mapping framework for digital noncommunicable disease management in Shanghai, China. The first phase involved desk research to examine current guidance and practices. In the second phase, pivotal stakeholders participated in focus group discussions to identify prevalent digital touchpoints across lifetime noncommunicable disease management. In the final phase, the Delphi technique was used to refine the framework based on expert insights and obtain consensus.

RESULTS

We identified 60 digital touchpoints across five essential stages of full-life-cycle noncommunicable disease management. Most experts acknowledged the rationality and feasibility of these touchpoints.

CONCLUSIONS

This study led to the creation of a comprehensive digital health process mapping framework that encompasses the entire life cycle of noncommunicable disease management. The insights gained emphasize the importance of a systemic strategic, person-centered approach over a fragmented, purely technocentric approach. We recommend that healthcare professionals use this framework as a linchpin for efficient disease management and seamless technology incorporation in clinical practice.