The winding road of requesting healthcare data for analytics purposes: using the one-interview mental model method for improving services of health data governance and big data request processes

Figures & data

Table 1. Summary table for the literature.

#	Identified gap in healthcare big data management	Literature
1	Need for data governance policies for health data privacy and security	Raghupathi & Raghupathi, 2014; Patil & Seshadri, 2014; Tse et al., 2018; Wang et al., 2018
2	Need for organisation specific robust healthcare data governance framework, e.g., Honest Broker services	Al-Badi et al., 2018; Alofaysan et al., 2014; Dhir et al., 2008; Tse et al., 2018; Valluripally et al., 2019; Williams & Colomb, 2020
3	Need for a sociotechnical perspective to develop appropriate data governance frameworks	Ancker et al., 2012; Colijn et al., 2017
4	Need for useful, user-friendly, and comprehendible healthcare big data analytics for easy and fast access to health information	Raghupathi & Raghupathi, 2014; Groves et al., 2013; Jagdish et al., 2014; Okal & Loice, 2019; Valluripally et al., 2019, January, Valluripally et al., 2019; Wang et al., 2018;
6	Need for developing expertise in big data analytics skills for appropriate interpretation of results	Wang et al., 2018;

Figure 1. Mental models (inspired from D. A. Norman, 1988).

Table 2. Demographic details of the participants.

Phase 1: Narrative approach using mental model method with data provider and requester
Participant #	Gender	Role	Age	Profession
P1	Male	Data Provider	45	Assistant Professor
P2	Male	Data Requester	50	Post-doctoral Fellow
Phase 2: Usability study of the new prototype
P3	Female	Data Requester	39	Associate Professor
P4	Male	Data Requester	25	Medical Student
P5	Female	Data Requester	28	Fellow Resident
P6	Female	Data Requester	39	Associate Professor
P7	Male	Data Requester	32	Fellow Resident
P8	Female	Data Requester	42	Research Consultant
P9	Female	Data Provider	26	Software Engineer
P10	Female	Data Requester	35	Assistant Professor
P11	Female	Data Requester	64	Assistant Director of Health Library
P12	Male	Data Provider	45	Assistant Professor
P13	Male	Data Requester	50	Post-doctoral Fellow
P14	Male	Data Requester	58	Assistant Professor

Figure 2. Requesting data queries and extraction through the data broker service by submitting the research data request form. (RDOC = Research Data Oversight Committee).

Figure 3. Data requester mental model of the data governance process.

Figure 4. Simulated model displaying the request form for users.

Figure 5. Dashboard displaying status (Approved/Denied/Manual Approval Required) of submitted requests.

Table 3. Descriptive statistics for quantitative usability data.

	Task 1 (Create & submit a data request)			Task 2 (Review the status of your submitted request)
Participant	Time on Task (minutes)	SEQ*	Observer rating**	Time on Task (minutes)	SEQ*	Observer rating**
P3	18.53	4	1	3	5	0
P4	22.02	6	1	3.5	5	0
P5	12.17	4	1	5.23	2	0
P6	32.77	4	1	3.5	5	0
P7	15.82	6	1	5.05	6	0
P8	14.92	7	1	4.35	4	0
P9	22.05	7	1	9.42	5	0
P10	21.03	7	1	2.9	7	0
P11	18.93	2	1	5.72	1	0
P12	24.02	3	1	6.15	1	0
P13	18.43	7	1	4.28	6	0
P14	12.13	7	2	4.37	7	0
Mean	19:40	5.33	1.08	4:79	4.5	0
Median	18.73	6	1	4.36	5	0

Note: SEQ* (Single Ease Question): 1 – very difficult; 7 – very easy

**Observer rating of task difficulty: 0 – Failed to complete; 1 – Completed with difficulty; 2 – Completed with ease

Table 4. Usability problems detected via participant responses.

Problem types identified through think-aloud, interviews, and observation
Problem Type	Examples
Layout	a) Participants cannot locate the ID of their submitted request to check status b) Participants found the order of fields in the request form to be random and redundant
Terminology	a) Participants did not understand the terminologies used in the request form (e.g., “Datasetname”) b) Participants were confused between “how long do you need access to the data” and “how soon do you need access to the data”
Data entry	a) Participants were not sure if the information they entered in request form was correct b) Drop down options provided for data entry did not match participants’ requirements
Comprehensiveness	a) Help feature was missing b) Participants expected to see examples of filled forms
Feedback	a) No error notification or alert when participants entered incorrect information or failed to enter information b) No edit/review/cancel options for submitted request

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