| Literature DB >> 34490060 |
Lan Zhang1, Xiu Yang2, Yuan Zhou1, Jialu Sun1, Zixiang Lin1.
Abstract
In recent years, the Chinese government has issued a series of deepening reform policies around smart healthcare, established a diversified technical basis and environmental protection, and deeply excavated the derivative value of healthcare information, aiming to provide high-quality healthcare services for patients. Information interaction in the context of smart healthcare is a kind of health information interaction completed by users with smart healthcare applications as the hub. It is an application form of social behavior and has an impact on value cocreation. Based on the theory of information interaction and value cocreation, this paper systematically reviews the research on information interaction and value cocreation in the smart healthcare context, analyzes the information interaction mode and information interaction mechanism in the smart healthcare context, constructs a theoretical model of the impact of information interaction on value cocreation, and empirically tests the relationship between information interaction and value cocreation in the smart healthcare context. The research of this paper aims to provide high-quality information interaction services for smart healthcare users, promote the dimensional management of information behavior in the context of smart healthcare, and promote the continuous improvement of the operation and management of smart healthcare.Entities:
Mesh:
Year: 2021 PMID: 34490060 PMCID: PMC8418551 DOI: 10.1155/2021/8778092
Source DB: PubMed Journal: J Healthc Eng ISSN: 2040-2295 Impact factor: 2.682
Data sources for the case study.
| Type of institution | Interviewee | Type of interview | Field observation | Second-hand data |
|---|---|---|---|---|
| Policy makers and regulators | WM Health Administration | One field interview with a person in charge of the relevant department | Field visit to the relevant department | Government web page data and related news reports |
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| Medical institutions | People's Hospital of WM | Field interviews with three people in charge of relevant departments, several communications via phone calls and social media | Field inspection of the implementation of medical informatization and management of the information service platform | Official website, related reports, and internal information |
| WM Heath Service Center | One field interview with a person in charge of the relevant department | Field inspection of the degree of network informatization | Official website, related reports, and internal information | |
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| Research and development (R&D) institutions | R&D Center, School of Pharmacy, XJ University | One field interview with a person in charge | Field visits to research centers and laboratories | Research achievements and news reporting from the institution's official website |
| WM United Laboratories | One field interview with a person in charge of the R&D department | Field visits to R&D base, firm headquarters, and exhibition center | News reporting from the institution's official website, industry development reports, and annual reports | |
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| Suppliers | WM Medical Service Management Co., Ltd. | One field interview with a marketing manager | Field visit to the business department of the firm | News reporting about the firm and internal information |
| WM Pharmaceutical Co., Ltd. | One field interview with a retail store manager | Field visit to the retail store | Firm's internal reports, annual reports, and official website | |
| HT Medical Equipment Co., Ltd. | One field interview with a marketing manager | Field visit to the firm's headquarters | Firm's internal reports, annual reports, and official website | |
Figure 1Information interaction model in the context of smart healthcare treatment.
Figure 2Theoretical framework diagram of information interaction and value cocreation in the context of smart healthcare.
Information interaction willingness measure.
| Variable name | Measurement items | Coding | Source of theory |
|---|---|---|---|
| Information interaction willingness | I would like to use smart healthcare applications | W1 | Venkatesh et al. [ |
| I am willing to pay for smart healthcare applications | W2 | ||
| I would recommend others to use smart healthcare apps | W3 |
Information interaction behavior measure.
| Variable name | Measurement items | Coding | Source of theory |
|---|---|---|---|
| Variable measure | I often use smart health apps to get medical information | B1 | Venkatesh et al. [ |
| I am an active user of smart healthcare applications | B2 | ||
| I often follow the updates of smart healthcare products and applications | B3 | Mark et al. [ |
Information interaction ability measure.
| Variable name | Measurement items | Coding | Source of theory |
|---|---|---|---|
| Information interaction ability | I learned about smart healthcare first-hand from other people | A1 | Jiang Kan,etc. [ |
| I learned about smart healthcare by filling in a questionnaire offline | A2 | ||
| I have read about smart healthcare on social media | A3 |
Value cocreation measure.
| Variable name | Measurement items | Coding | Source of theory |
|---|---|---|---|
| Value cocreated | Users can easily access the smart healthcare information platform to obtain services | V1 | Taghizadeh et al. [ |
| Smart healthcare platform will actively and effectively solve problems for users | V2 | ||
| Smart healthcare platform will protect users' personal information | V3 |
Rating the impact of information interaction on value cocreation.
| Value cocreation ( | Information interaction willingness ( | Information interaction behavior ( | Information interaction ability ( | |
|---|---|---|---|---|
| A1 | 10.2 | 4 | 3 | 3 |
| A2 | 10.8 | 4 | 4 | 3 |
| A3 | 11.4 | 4 | 5 | 3 |
| A4 | 12 | 5 | 4 | 4 |
| A5 | 12.3 | 4 | 5 | 5 |
| A6 | 14.7 | 4 | 5 | 5 |
| A7 | 15 | 5 | 5 | 5 |
| A8 | 15.6 | 5 | 6 | 5 |
| A9 | 16.8 | 6 | 6 | 5 |
| A10 | 17.7 | 6 | 7 | 5 |
| A11 | 17.7 | 5 | 7 | 6 |
| A12 | 19.8 | 6 | 7 | 6 |
| A13 | 21 | 6 | 7 | 7 |
| A14 | 22.8 | 7 | 7 | 7 |
| A15 | 22.8 | 6 | 8 | 7 |
| A16 | 24 | 7 | 7 | 9 |
| A17 | 27.3 | 7 | 9 | 8 |
| A18 | 27.6 | 7 | 9 | 9 |
| A19 | 28.5 | 8 | 10 | 9 |
| A20 | 29.4 | 9 | 9 | 9 |
| A21 | 29.7 | 9 | 10 | 9 |
| A22 | 29.7 | 9 | 10 | 9 |
Data analysis results.
| Dependent variable: | ||||
| Method: least squares | ||||
| Date: 06/10/21; time: 00:01 | ||||
| Sample: 1 22 | ||||
| Included observations: 22 | ||||
| Variable | Coefficient | Std. error | Prob. | |
|
| −2.486653 | 0.706511 | −3.519626 | 0.0024 |
| 1.067627 | 0.279088 | 3.825414 | 0.0012 | |
| 1.120217 | 0.248166 | 4.513978 | 0.0003 | |
| 1.315068 | 0.237363 | 5.540322 | 0.0000 | |
| 0.986739 | Mean dependent var | 19.85455 | ||
| Adjusted | 0.984529 | SD dependent var | 6.772826 | |
| SE of regression | 0.842426 | Akaike information criterion | 2.657905 | |
| Sum squared resid | 12.77428 | Schwarz criterion | 2.856276 | |
| Log likelihood | −25.23695 | Hannan–Quinn criter. | 2.704635 | |
| 446.4534 | Durbin–Watson stat | 1.744985 | ||
| Prob ( | 0.000000 | |||