Towards Rapid Quality Reviews for Digital Health Apps: A Machine Learning Approach

Maciej Hyzy; Maurice Mulvenna; RR Bond; Lu Bai; Robert Daly; Simon Leigh

Towards Rapid Quality Reviews for Digital Health Apps: A Machine Learning Approach

Maciej Hyzy
, Maurice Mulvenna
, RR Bond
, Lu Bai
, Robert Daly
, Simon Leigh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

91 Downloads (Pure)

Abstract

The Organisation for the Review of Care and Health Apps (ORCHA) is a UK based digital health compliance company. This study examines the data set of more than 2000 digital health apps assessed with ORCHA assessment tool. ORCHA assessment tool consists of approximately 300 questions and is used to assess digital health apps for quality (defined as "compliance with best practice standards"). The objective of the study was to select a small number (1-5) of important questions form the tool and determine if the app will pass or fail assessment. Accuracy was 76% with logistic regression and 78% with random forest.

Original language	English
Title of host publication	Towards Rapid Quality Reviews for Digital Health Apps: A Machine Learning Approach
Number of pages	4
Publication status	Accepted - 9 Mar 2023
Event	Workgroup on Interactive Systems in Healthcare (WISH) - Hamburg, Germany Duration: 28 Apr 2023 → 28 Apr 2023 https://sites.uci.edu/wish2023/

Conference

Conference	Workgroup on Interactive Systems in Healthcare (WISH)
Abbreviated title	WISH
Country/Territory	Germany
City	Hamburg
Period	28/04/23 → 28/04/23
Internet address	https://sites.uci.edu/wish2023/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

machine learning
quality assessment
digital health
mHealth

Access to Document

CHI_Workshop (2) (1)Author Accepted Manuscript, 416 KB

Using data science techniques to analyse quality assessment data of digital health apps
Zych, M. M. (Author), Martinez Carracedo, J. (Supervisor), Mulvenna, M. (Supervisor) & Bond, R. (Supervisor), May 2025
Student thesis: Doctoral Thesis

File

Cite this

@inproceedings{a80a2b1fd4864794846594504fd05783,

title = "Towards Rapid Quality Reviews for Digital Health Apps: A Machine Learning Approach",

abstract = "The Organisation for the Review of Care and Health Apps (ORCHA) is a UK based digital health compliance company. This study examines the data set of more than 2000 digital health apps assessed with ORCHA assessment tool. ORCHA assessment tool consists of approximately 300 questions and is used to assess digital health apps for quality (defined as {"}compliance with best practice standards{"}). The objective of the study was to select a small number (1-5) of important questions form the tool and determine if the app will pass or fail assessment. Accuracy was 76\% with logistic regression and 78\% with random forest.",

keywords = "machine learning, quality assessment, digital health, mHealth",

author = "Maciej Hyzy and Maurice Mulvenna and RR Bond and Lu Bai and Robert Daly and Simon Leigh",

year = "2023",

month = mar,

day = "9",

language = "English",

booktitle = "Towards Rapid Quality Reviews for Digital Health Apps: A Machine Learning Approach",

note = "Workgroup on Interactive Systems in Healthcare (WISH) , WISH ; Conference date: 28-04-2023 Through 28-04-2023",

url = "https://sites.uci.edu/wish2023/",

}

TY - GEN

T1 - Towards Rapid Quality Reviews for Digital Health Apps: A Machine Learning Approach

AU - Hyzy, Maciej

AU - Mulvenna, Maurice

AU - Bond, RR

AU - Bai, Lu

AU - Daly, Robert

AU - Leigh, Simon

PY - 2023/3/9

Y1 - 2023/3/9

N2 - The Organisation for the Review of Care and Health Apps (ORCHA) is a UK based digital health compliance company. This study examines the data set of more than 2000 digital health apps assessed with ORCHA assessment tool. ORCHA assessment tool consists of approximately 300 questions and is used to assess digital health apps for quality (defined as "compliance with best practice standards"). The objective of the study was to select a small number (1-5) of important questions form the tool and determine if the app will pass or fail assessment. Accuracy was 76% with logistic regression and 78% with random forest.

AB - The Organisation for the Review of Care and Health Apps (ORCHA) is a UK based digital health compliance company. This study examines the data set of more than 2000 digital health apps assessed with ORCHA assessment tool. ORCHA assessment tool consists of approximately 300 questions and is used to assess digital health apps for quality (defined as "compliance with best practice standards"). The objective of the study was to select a small number (1-5) of important questions form the tool and determine if the app will pass or fail assessment. Accuracy was 76% with logistic regression and 78% with random forest.

KW - machine learning

KW - quality assessment

KW - digital health

KW - mHealth

M3 - Conference contribution

BT - Towards Rapid Quality Reviews for Digital Health Apps: A Machine Learning Approach

T2 - Workgroup on Interactive Systems in Healthcare (WISH)

Y2 - 28 April 2023 through 28 April 2023

ER -

Towards Rapid Quality Reviews for Digital Health Apps: A Machine Learning Approach

Abstract

Conference

UN SDGs

Keywords

Access to Document

Fingerprint

Student theses

Using data science techniques to analyse quality assessment data of digital health apps

Cite this