Hospitals’ Adoption of Mobile-Based Personal Health Record Systems and Patients’ Characteristics: A Cross-Sectional Study Analyzing National Healthcare Big Data

Abstract

Insufficient information exists on the associations between hospitals’ adoption of mobile-based personal health record (mPHR) systems and patients’ characteristics. This study explored the associations between patients’ characteristics and hospitals’ adoption of mPHR systems in Korea. This cross-sectional study used 316 hospitals with 100 or more beds as the unit of analysis. Previously collected data on mPHR adoption from May 1 to June 30, 2020 were analyzed. National health insurance claims data for 2019 were also used to analyze patients’ characteristics. The dependent variable was mPHR system adoption (0 vs 1) and the main independent variables were the number of patients, age distribution, and proportions of patients with cancer, diabetes, and hypertension among inpatients and outpatients. The number of inpatients was significantly associated with mPHR adoption (adjusted odds ratio [aOR]: 1.174; 1.117-1.233, P < .001), as was the number of outpatients (aOR: 1.041; 1.028-1.054, P < .001). The proportion of inpatients aged 31 to 60 years to those aged 31 years and older was also associated with hospital mPHR adoption (aOR: 1.053; 1.022-1.085, P = .001). mPHR system adoption was significantly associated with the proportion of inpatients (aOR: 1.089; 1.012-1.172, P = .024) and outpatients (aOR: 1.138; 1.026-1.263, P = .015) with cancer and outpatients (aOR: 1.271; 1.101-1.466, P = .001) with hypertension. Although mPHR systems are useful for the management of chronic diseases such as diabetes and hypertension, the number of patients, younger age distribution, and the proportion of cancer patients were closely associated with hospitals’ introduction of mPHR systems.

Keywords

personal health records patient portals mHealth electronic health records personal health information

What do we already know about this topic?

• Sufficient funding is important in accelerating the adoption of information and communication technology. Mobile-based personal health record (mPHR) system users are younger patients, and these systems are useful for chronic disease management.

• However, little was previously known regarding the relationship between hospitals’ adoption of mPHR systems and patients’ characteristics.

What does your research contribute to the field?

• The study findings contribute to the expansion of knowledge regarding medical informatics and healthcare organizations. Hospitals with stable resources measured by the number of patients and a high proportion of patients with cancer are more likely to adopt mPHR systems.

What are your research’s implications toward theory, practice, or policy?

• The study results provide a practical basis for understanding how hospitals behave regarding the adoption of mPHR systems within the scope of their healthcare provision. A potential funding resource, such as a high number of patients, is important, and mPHR systems may be more useful for managing patients with cancer than those with other chronic diseases.

Introduction

Personal health record (PHR) systems are electronic applications through which patients and authorized personnel can securely, privately, and confidentially manage patient health information.¹ PHR systems accessible through mobile and wearable devices are called mobile-based personal health record (mPHR) systems.^2,3 One of the central phenomena in the world regarding PHR systems is the rapid adoption of mPHR systems into clinical practice settings.⁴ This phenomenon is occurring on all continents,^5,6 and Korean hospitals are also rapidly adopting mPHR systems into clinical practice.^7,8

According to a 2018 study that investigated the number of prescription renewals or appointment requests made through PHR systems at primary care clinics, the adoption rates in the European Union ranged from 22% to 24%.⁹ Meanwhile, in a 2016 American study focusing on the individual as a unit of analysis, the adoption rate was predicted to reach approximately 75.0% by 2020.¹⁰

Although the implementation of mPHR systems has been widespread, few studies on mPHR adoption have specifically focused on patient factors at the hospital level. Based on a comprehensive literature review, this study proposes the following 3 related factors as predictors of mPHR system adoption in hospitals.

First, many studies have pointed out that insufficient funding is a critical barrier to information and communication technology (ICT) investment.^11,12 The financial status of a healthcare organization is closely related to technological investment.¹³ Investing in mPHR requires a large amount of fixed and operating costs. Thus, hospitals with potential funding resources would be more likely to adopt mPHR systems.

Second, several studies on the adoption of ICT in healthcare have shown a close relationship with the users’ age. Research on the factors affecting the use of digital devices and electronic platforms showed that adults aged 18 to 34 years and 35 to 49 years were, respectively, 3.5 and 2.0 times more likely to seek health information through the internet than adults aged 65 years and older.¹⁴ Other studies presented similar findings.^15,16 These results suggest that hospitals with a younger patient age distribution are more likely to adopt mPHR systems at the organizational level.

Finally, several previous studies have demonstrated that ICT systems, such as electronic PHR, electronic health (eHealth) tools, and internet-based resources, are effective for the management of chronic diseases.^17-19 Mobile applications are useful for the management of patients with cancer.^20,21 A Korean study on the use of mobile devices in the outpatient department of a university hospital showed that their use and an internet intervention were effective in controlling glycated hemoglobin levels in patients with diabetes.²² Therefore, this study predicted that hospitals would be more likely to adopt mPHR systems as their proportion of patients with chronic diseases increases.

Although these previous reports provided tangible evidence of ICT adoption, eHealth, and mPHR systems, most utilized the individual as the unit of analysis. Thus, it is unknown whether the adoption of an mPHR system by a hospital is associated with the number of patients, their age, and their chronic disease status at the organizational level. It is important to clarify whether stable and potential funding resources, the proportion of younger patients, and the proportion of patients with chronic diseases are related to the adoption of mPHR systems.

Thus, this study aimed to explore the associations between the adoption of mPHR systems by hospitals in Korea and patients’ characteristics, such as the number of patients, their age distribution, and their chronic disease status. Considering the available evidence, we hypothesized that the likelihood of adoption of mPHR systems by hospitals would increase with the number of patients, the proportion of younger patients, and the proportion of patients with chronic diseases.

Methods

Study Design

This was a cross-sectional study. The utilized units of analysis were general hospitals in Korea with 100 or more beds. The targeted sample of this study comprised all hospitals with 100 or more beds in Korea because there was no reason to limit the sample size due to issues with data availability or collection. There were 316 hospitals, and the final sample size was 304 hospitals after data cleaning. We defined an mPHR system as one that can be accessed by patients using a smartphone or mobile application, requires patients to have a login ID and password, and has functional dashboards (eg, allows patients to check scheduled visits, sends reminders, includes medical information such as prescriptions, provides laboratory test results, and contains mobile billing information). Most hospitals in Korea, generally speaking, are developing and using mPHR systems for both inpatients and outpatients. This study was approved by the institutional review board on March 12, 2021 (IRB approval number: 2021-022-001). All authors declare that there is no potential conflict of interest relevant to this manuscript.

Data Collection Procedure

Two data sources were used. The first was a survey conducted by a university hospital that investigated the use of mPHR systems through vendors from May 1, 2020, to June 30, 2020. In that investigation, a researcher asked vendors for the names of the hospitals to which their products had been supplied. For hospitals not listed as adopters of mPHR systems, the researcher directly investigated their adoption status on Google and Naver using their names and addresses. The researcher then confirmed this information by downloading the required applications from Google Play and the App Store. Further details on data collection methods can be found in the previous study.^8,23

The second data source was the Health Insurance Review & Assessment Services (HIRA). It is a governmental agency that operates the national health insurance program. It provides professional health insurance review and assessment services for healthcare organizations and works independently as a third-party administrator. Data from a complete year (2019) were used to analyze patients’ characteristics. Figure 1 presents the details of the hospital selection process.

Figure 1.

Study hospital selection process. mPHR = mobile-based personal health record; HIRA = health insurance review and assessment service.

Our study sample comprised 316 hospitals with 100 or more beds as of March 31, 2019. We excluded clinics and smaller hospitals with fewer than 100 beds because only a few of them had adopted mPHR systems. We also excluded 9 hospitals with missing insurance claims data and information on general characteristics, as well as 3 with only iOS-based mPHR systems. The study hospitals were categorized into case and control groups. The case group included hospitals that adopted mPHR systems provided by external vendors or developed by the hospitals. The control group comprised other hospitals.

Outcome and Predictor Variables

The primary outcome variable was the adoption or non-adoption of mPHR systems by hospitals. This variable was obtained from the first data source mentioned above.

The independent variables were analyzed using data extracted from the HIRA data warehouse (DW) system. This study had 10 main independent variables: the number of inpatients and outpatients, the patient age proportion (patients aged between 31 and 60 years among those aged 31 years or older) among inpatients and outpatients, and the proportion of patients with cancer, diabetes, and hypertension among inpatients and outpatients. Several data extraction stages and calculation steps were carried out to extract the final independent variables. First, this study directly extracted the number of inpatients and outpatients for each hospital from the HIRA DW system. This variable was used to investigate the relationship between the introduction of mPHR systems and the number of patients. Second, this study extracted the number of inpatients and outpatients between the ages of 31 and 60 for each hospital from the HIRA DW system. Third, by using the data from the first and the second stages, the proportion of inpatients and outpatients between the ages of 31 and 60 was calculated. This variable was used to investigate the relationship between the proportion of patients aged 31 and 60 years and hospitals’ adoption of mPHR systems. Fourth, the number of cancer, diabetes, and hypertension inpatients and outpatients aged 31 to 60 years for each hospital was extracted, respectively. Fifth, by using data from the first and fourth stages, the proportion of cancer, diabetes, and hypertension inpatients and outpatients aged 31 to 60 years for each hospital was calculated. This variable was used to explore the relationship between patients’ chronic disease status and hospitals’ adoption of mPHR systems. Age was analyzed by calculating the percentage of i (i = inpatient or outpatient) patient groups aged 31 to 60 years among those aged 31 and older. Chronic disease was assessed by calculating the percentage of ij (i = inpatient or outpatients; j = cancer, diabetes, or hypertension) patients aged 31 to 60 years among all patients aged 31 and older, and then multiplying the result by 100.

Chronic diseases were identified using the diagnostic codes outlined in the 10th revision of the International Classification of Diseases (C00-C97 for cancer, E10-E14 for diabetes, and I10-I15 for hypertension). Primary diagnosis data were extracted from the health insurance claims diagnosis table of the HIRA.

Several variables were added to the model to control their effects based on findings of previous studies on the adoption of ICT in healthcare.^24-26 The variables included hospital ownership (private vs public), facility’s location (Seoul + mega-metro cities vs the others), tertiary hospital status (representing teaching hospitals vs non-teaching hospitals), and years of operation.

Data Analysis

First, the descriptive statistics were analyzed, and the case and control groups were compared. Then, all independent variables were cross-tabulated, independence was tested using the chi-square test, and mean differences were tested using the t-test. Multi-collinearity issues were resolved by removing highly correlated variables after conducting correlation analysis among independent variables. This study used a correlation matrix for correlation analysis. Variables such as the number of nurses and physicians were excluded based on high correlations of independent variables. We also observed a high correlation between the variables of the inpatient and outpatient groups. Thus, we introduced 2 separate models for the inpatient and outpatient groups, respectively. Logistic regression analysis was conducted to investigate the relationship between the adoption of mPHR systems and patients’ characteristics at the hospital level after controlling all independent variables. Finally, this study reported the adjusted odds ratio (aOR), meaning the regression coefficient of logistic regression after including or controlling all the variables in the model. All statistical analyses were performed using SAS/STAT, version 9.4 (SAS Institute, Cary, NC).

Results

General Characteristics of the Study Hospitals

Table 1 presents the general characteristics of the study hospitals. Hospitals that adopted mPHR systems were more likely to be located in the mega-metro cities (53.0% vs 30.4%; P = .001) and to be tertiary hospitals (32.0% vs 4.9%; P < .001) than those that did not. Hospitals that adopted mPHR systems had a larger number of inpatients and outpatients (P < .001) and a younger inpatient population (P = .004). Hospitals that adopted mPHR systems also had a higher proportion of patients with cancer (P < .001), a lower proportion of inpatients with diabetes (P = .037), a higher proportion of outpatients with diabetes (P = .023), and a higher proportion of outpatients with hypertension (P < .001).

Table 1.

General Characteristics of the Study Hospitals by mPHR Adoption Status.

Variables^*		Adoption of mPHR systems in hospitals		All (N = 304)	P-value
Variables^*		Yes (N = 100)	No (N = 204)	All (N = 304)	P-value
Control variables	Foundation (%)				.698
	- For profit	79.0	80.9	80.3
	- None-profit	21.0	19.1	19.7
	Location (%)				.001
	- Seoul + mega-metro cities	53.0	30.4	37.8
	- The others	47.0	69.6	62.2
	Tertiary status (%)				<.001
	- Tertiary hospitals	32.0	4.9	13.8
	- General hospitals	68.0	95.1	86.2
	Years of operation (std.)	29.9 (15.4)	26.7 (12.3)	27.8 (13.5)	.076
Main independent variables	No. of INPs (std.) (unit: 1000)	19.4 (14.8)	7.5 (6.0)	11.4 (11.3)	<.001
	No. of OUTPs (std.) (unit: 1000)	97.3 (89.9)	36.4 (23.2)	56.4 (61.8)	<.001
	% of INPs aged 31-60 (std.)	51.2 (10.1)	47.3 (11.6)	48.6 (11.3)	.004
	% of OUTPs aged 31-60 (std.)	57.5 (8.7)	57.6 (9.7)	57.6 (9.3)	.912
	% of INPs with CA aged 31-60 (std.)	4.9 (5.9)	1.9 (3.5)	2.9 (4.6)	<.001
	% of OUTPs with CA aged 31-60 (std.)	4.0 (5.0)	1.3 (2.6)	2.2 (3.8)	<.001
	% of INPs with DIAB aged 31-60 (std.)	5.5 (3.5)	6.4 (3.6)	6.1 (3.6)	.037
	% of OUTPs with DIAB aged 31-60 (std.)	4.5 (1.7)	4.0 (1.7)	4.2 (1.7)	.023
	% of INPs with HP aged 31-60 (std.)	5.6 (2.7)	5.0 (2.7)	5.2 (2.7)	.064
	% of OUTPs with HP aged 31-60 (std.)	6.4 (2.0)	5.3 (1.9)	5.7 (2.0)	<.001

std. = standard deviation; mPHR = mobile personal health record; CA = cancer; DIAB = diabetes; HP = hypertension.

INPs and OUTPs stand for inpatient and outpatient groups, respectively.

Number of Patients and Hospitals’ Adoption of mPHR Systems

Table 2 shows the results of the analysis of the relationship between the number of inpatients and outpatients and hospitals’ adoption of mPHR systems. The odds of adopting mPHR systems significantly increased as the number of patients increased—both for inpatients (aOR: 1.174; 1.117-1.233, P < .001) and outpatients (aOR: 1.041; 1.028-1.054, P < .001).

Table 2.

Number of Patients and Adoption of mPHR Systems by Hospitals.

Variables	Inpatient departments (N = 304)				Outpatient departments (N = 304)
	aOR¹	95% CI²		P-value	aOR¹	95% CI		P-value
	aOR¹	LL³	UL⁴	P-value	aOR¹	LL	UL	P-value
Private hospital (ref = public)	0.662	0.298	1.470	.310	0.698	0.310	1.569	.384
Mega-metro city (ref = others)	2.650	1.446	4.856	.002	2.071	1.132	3.790	.018
Tertiary hospitals (ref = general hospitals)	0.553	0.182	1.678	.296	0.620	0.207	1.860	.394
Years of operation	0.991	0.968	1.013	.407	0.988	0.966	1.011	.316
No. of patients²	1.174	1.117	1.233	<.0001	1.041	1.028	1.054	<.0001

mPHR = mobile personal health record.

aOR = adjusted odds ratio in the model after including all the variables.

CI = confidence interval.

LL = lower limit.

UL = upper limit.

Proportion of Patients Aged 31 to 60 Years and Hospitals’ Adoption of mPHR Systems

Table 3 shows the results of the analysis of the relationship between the adoption of mPHR systems and the proportion of patients aged 31 to 60 years among those aged 31 years and older. For inpatients, the proportion was significantly associated with hospitals’ adoption of mPHR systems (aOR: 1.053; 1.022-1.085, P = .001). The odds of mPHR adoption increased by 5.3% as the proportion of patients aged 31 to 60 years among those aged 31 and older increased by 1 unit.

Table 3.

Proportion of Patients Aged 31 to 60 years and Adoption of mPHR Systems by Hospitals.

Variables	Inpatient departments (N = 304)				Outpatient departments (N = 304)
	aOR¹	95% CI²		P-value	aOR¹	95% CI		P-value
	aOR¹	LL³	UL⁴	P-value	aOR¹	LL	UL	P-value
Private hospital (ref = public)	0.577	0.272	1.226	.153	0.812	0.395	1.671	.573
Mega-metro city (ref = others)	2.024	1.166	3.513	.012	2.170	1.266	3.722	.005
Tertiary hospitals (ref = general hospitals)	7.551	3.415	16.69	<.0001	8.383	3.785	18.56	<.0001
Years of operation	1.014	0.992	1.037	.210	1.007	0.985	1.030	.534
Percentage of patients aged 31-60⁵	1.053	1.022	1.085	.001	1.024	0.990	1.058	.164

mPHR: mobile personal health record.

aOR = adjusted odds ratio in the model after including all the variables.

CI = confidence interval.

LL = lower limit.

UL = upper limit.

Proportion of patients aged 31 to 60 years among those aged 31 years and older.

Patients’ Chronic Disease Status and Hospitals’ Adoption of mPHR Systems

Table 4 shows the associations between hospitals’ adoption of mPHR systems and the proportion of patients aged 31 to 60 years with cancer, diabetes, and hypertension. The proportion of patients with cancer was associated with the adoption of mPHR systems, for both inpatients (aOR: 1.089; 1.012-1.172, P = .024) and outpatients (aOR: 1.138, 1.026-1.263, P = .015). The likelihood of adoption was significantly associated with the proportion of outpatients with hypertension (aOR: 1.271, 1.101-1.466, P = .001).

Table 4.

Chronic Disease Status of Patients and Adoption of mPHR Systems by Hospitals.

Category	Variables	Inpatient departments (N = 304)				Outpatient departments (N = 304)
		aOR¹	95% CI²		P-value	aOR	95% CI		P-value
		aOR¹	LL³	UL⁴	P-value	aOR	LL	UL	P-value
Cancer	Private hospital (ref = public)	1.266	0.607	2.640	.530	1.261	0.605	2.627	.536
	Mega-metro city (ref = others)	1.906	1.098	3.309	.022	1.920	1.106	3.331	.020
	Tertiary hospitals (ref = general hospitals)	5.151	2.152	12.330	.001	4.107	1.598	10.554	.003
	Years of operation	1.005	0.984	1.026	.660	1.004	0.984	1.025	.697
	Percentage of patients aged 31-60⁵	1.089	1.012	1.172	.024	1.138	1.026	1.263	.015
Diabetes	Private hospital (ref = public)	1.182	0.581	2.406	.645	0.851	0.425	1.705	.650
	Urban area (ref = rural)	2.093	1.219	3.595	.007	2.302	1.335	3.968	.003
	Tertiary hospitals (ref = general hospitals)	8.303	3.740	18.432	<.0001	7.372	3.327	16.336	<.0001
	Years of operation	0.999	0.979	1.020	.940	0.999	0.979	1.020	.920
	Percentage of patients aged 31-60⁵	0.916	0.834	1.005	.065	1.151	0.989	1.340	.069
Hypertension	Private hospital (ref = public)	0.968	0.479	1.957	.928	0.703	0.343	1.441	.336
	Mega-metro city (ref = others)	2.180	1.273	3.733	.005	2.497	1.430	4.359	.001
	Tertiary hospitals (ref = general hospitals)	8.047	3.514	18.431	<.0001	5.352	2.352	12.179	<.0001
	Years of operation	1.001	0.981	1.022	.924	0.997	0.977	1.018	.800
	Percentage of patients aged 31-60⁵	0.995	0.898	1.102	.920	1.271	1.101	1.466	.001

mPHR: mobile personal health record.

aOR = adjusted odds ratio in the model after including all the variables.

CI = confidence interval.

LL = lower limit.

UL = upper limit.

Proportion of patients aged 31 to 60 years among those aged 31 years and older.

Discussion

This study examined the associations between the adoption of mPHR systems by hospitals and patients’ characteristics at the hospital level. We observed that the adoption of mPHR systems was significantly associated with the number of both inpatients and outpatients. However, the adoption of mPHR systems was partially associated with a younger age distribution in inpatients, with cancer in both inpatients and outpatients, and hypertension in outpatients.

As a potential funding resource for investment in mPHR systems, the number of patients was significantly associated with mPHR system adoption. This study result is aligned with previous studies wherein stable funding was identified as important for ICT investment.^11-13 A large number of patients may mean earning more revenue or profit, and those hospitals may be more likely to have room or opportunity to invest in the adoption of mPHR systems than those with fewer patients.

Regarding the younger age distribution, our finding indicated that mPHR system adoption was only significantly associated with a high proportion of younger inpatients. However, this finding was not significant for outpatients. The findings of this study are partially consistent with those of previous reports showing that younger individuals or patients used ICT more than older ones.^14-16 Conducting a further analysis with a dynamic combination of 3 disease categories may be necessary to understand why patients’ age showed a close association with mPHR system adoption only for inpatients.

Furthermore, we found that the adoption of mPHR systems was closely associated with the proportion of inpatients and outpatients with cancer and outpatients with hypertension. No relationship was observed concerning diabetes. Regarding patients with cancer, our finding is consistent with other studies wherein many mobile applications were used for patients with cancer.^20,21 These applications were considered effective for self-adherence and support during chemotherapy treatments for cancer.²⁰ Cancer is the first and second leading cause of death in Korea and the United States, respectively.^27,28 Moreover, various screening programs exist for diagnosing cancer.^29,30 These etiological features may cause patients to visit the outpatient departments of hospitals more often. However, cancer treatment often entails hospitalization³¹ and stays at inpatient facilities (eg, hospices).³² Thus, patients with cancer visit hospitals more often than those with other types of chronic diseases. These complex etiological features of cancer may lead patients to use mPHR applications, and hospitals with a large number of patients with cancer might be more likely to adopt these systems for the predominant users in both inpatient and outpatient settings.

In contrast, diabetes and hypertension are generally treated in outpatient settings.^33,34 Although some studies have found that mPHR applications were useful for the management of chronic diseases such as diabetes^17,22 and hypertension, they may not be valuable for chronic diseases at the macro or comprehensive organizational level. Nonetheless, the implementation of detailed and dedicated areas for chronic disease management in hospitals could be beneficial.

This study has some limitations. First, the mPHR adoption data were collected through mPHR vendors and extracted from a single national study. Thus, notably, underestimation of mPHR adoption may have occurred because only one researcher verified mPHR adoption status. However, data verification was conducted using Google Play, the App Store, and various web search engines (eg, Google and Naver) to mitigate this underestimation effect. Second, this study analyzed 3 diseases separately and could not conduct a more dynamic analysis. For example, patients with both diabetes and hypertension, as compared to patients with either condition separately, should have been considered if the intention was a more systematic data analysis. The reason for the absence of significant associations for the proportion of patients with diabetes and inpatients with hypertension could have been due to the lack of this systematic approach. Third, this study lacked patient-level granular data. Using patient-level data would be more appropriate to investigate the relationship between PHR adoption and patients’ characteristics in the design step. Finally, the interpretation of study results may be limited to hospitals in Korea. However, the study design may have research value for comparisons with countries that have similar healthcare systems to that of Korea.

Our study has several strengths compared to previous studies. First, we used national health insurance claims data for 1 year (2019), which would bring high validity through increasing statistical power with a large sample size. Second, this is the first study investigating the relationship between mPHR adoption and patient-related hospital characteristics. Third, this study was conducted in a very competitive market environment. Therefore, we observed our variables of interest without having any preoccupied intentions or plans.

Conclusion

This study confirmed that the number of patients, younger inpatient age distribution, and the proportion of inpatients and outpatients with cancer and outpatients with hypertension were significantly associated with mPHR system adoption by hospitals in Korea. Since previous studies have identified a younger age distribution and chronic disease status (such as diabetes and hypertension) as factors affecting mPHR adoption, and mPHR systems are useful for chronic disease management, the variables with slightly discordant study results, such as inpatients with hypertension and diabetes, need further study. Currently, many healthcare organizations and governmental agencies are attempting to develop various strategies to promote PHR adoption nationwide. Although the study results have limited generalizability, we believe that the findings can provide decision-makers with useful information and ideas to accelerate the adoption of mPHR systems in hospitals.

Footnotes

Acknowledgements

We also deeply thank Dr. Dong Hwan Kim, M.D., Department of Neurosurgery, Pusan National University Hospital and College of Medicine, Korea, for conducting the PHR survey and giving us excellent comments.

Author Contribution

YTP and BKC conceived the research. The mPHR investigation was carried out by BKC. YTP obtained institutional review board approval and collected data on healthcare administration from the HIRA. Data analysis, including the interpretation of output, was conducted by YTP, HAP, and BKC. YTP, HAP, and JML wrote the manuscript draft. HAP and JML significantly also contributed to the improvement of the manuscript. All authors read and approved the final manuscript.

Availability of Data and Materials

The datasets and underlying research materials analyzed during the current study are available from the corresponding author upon reasonable request.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported for two years by the Pusan National University (Grant number: 201908280004).

The study was approved by the HIRA Institutional Review Board on March 12, 2021 (Approval number, 2021-022-001).

ORCID iD

Byung Kwan Choi

References

The Office of the National Coordinator for Health Information Technology (ONC). What is a personal health record? 2016. Accessed July 3, 2021. https://www.healthit.gov/faq/what-personal-health-record-0

Smith

Magnani

. New technologies, new disparities: the intersection of electronic health and digital health literacy. Int J Cardiol. 2019;292:280-282.

Lee

Park

Shin

, et al. Which users should be the focus of mobile personal health records? Analysis of user characteristics influencing usage of a tethered mobile personal health record. Telemed J E Health. 2016;22(5):419-428.

Harahap

Handayani

Hidayanto

. Functionalities and issues in the implementation of personal health records: systematic review. J Med Internet Res. 2021;23(7):e26236. doi:10.2196/26236

Johnson

Richwine

Patel

. Individuals’ access and use of patient portals and smartphone health apps, 2020. ONC Data Brief No.57. 2021;1-14. Accessed July 10, 2022. https://www.healthit.gov/data/data-briefs/individuals-access-and-use-patient-portals-and-smartphone-health-apps-2020

InteropEHRate. Unleashing personal health data for care and research: The InteropEHRate approach. The European Union; August 2021.

Kim

Ryu

Cho

, et al. Impact of personal health records and wearables on health outcomes and patient response: three-arm randomized controlled trial. JMIR Mhealth Uhealth. 2019;7(1):e12070.

Choi

Park

Kwon

Kim

. Analysis of platforms and functions of mobile-based personal health record systems. Healthc Inform Res. 2020;26(4):311-320.

Lupiáñez-Villanueva

Devaux

Valverde-Albacete

. Benchmarking deployment of eHealth among general practitioners. Publications Office of the European Union; 2018.

10.

Ford

Hesse

Huerta

. Personal health record use in the United States: forecasting future adoption levels. J Med Internet Res. 2016;18(3):e73.

11.

Cherry

Carter

Owen

Lockhart

. Factors affecting electronic health record adoption in long-term care facilities. J Healthc Qual. 2008;30(2):37-47. doi:10.1111/j.1945-1474.2008.tb01133.x

12.

Kapadia

Ariani

Ray

. Emerging ICT implementation issues in aged care. Int J Med Inform. 2015;84(11):892-900. doi:10.1016/j.ijmedinf.2015.07.002

13.

Meiling

Yahya

Waqas

Shaohua

Ali

Hania

. Boosting sustainability in healthcare sector through Fintech: analyzing the moderating role of financial and ICT development. Inquiry. 2021;58:1-11.

14.

Kontos

Blake

Chou

Prestin

. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012. J Med Internet Res. 2014;16(7):e172.

15.

Krist

Woolf

Bello

, et al. Engaging primary care patients to use a patient-centered personal health record. Ann Fam Med. 2014;12(5):418-426.

16.

Baumann

Czerwinski

Reifegerste

. Gender-specific determinants and patterns of online health information seeking: results from a representative German Health Survey. J Med Internet Res. 2017;19(4):e92.

17.

Graetz

Huang

Brand

Hsu

Reed

. Mobile-accessible personal health records increase the frequency and timeliness of PHR use for patients with diabetes. J Am Med Inform Assoc. 2019;26(1):50-54.

18.

Niazkhani

Toni

Cheshmekaboodi

Georgiou

Pirnejad

. Barriers to patient, provider, and caregiver adoption and use of electronic personal health records in chronic care: a systematic review. BMC Med Inform Decis Mak. 2020;20(1):153.

19.

Gee

Greenwood

Paterniti

Ward

Miller

. The eHealth enhanced chronic care model: a theory derivation approach. J Med Internet Res. 2015;17(4):e86.

20.

Prashad

. The role of personal health record systems in chronic disease management. Stud Health Technol Inform. 2017;234:275-279.

21.

Magalhães

Fernandes

Martinez-Galiano

Santos

. Exploring the use of mobile applications by cancer patients undergoing chemotherapy: a scoping review. Int J Med Inform. 2020;144:104293.

22.

Kim

. Effectiveness of mobile and internet intervention in patients with obese type 2 diabetes. Int J Med Inform. 2008;77(6):399-404.

23.

Choi

Park

Lane

Kang

. Factors of quality of care and their association with smartphone based PHR adoption in South Korean hospitals. BMC Med Inform Decis Mak. 2021;21(1):296. doi:10.1186/s12911-021-01666-9

24.

Simon

Kaushal

Cleary

, et al. Correlates of electronic health record adoption in office practices: a statewide survey. J Am Med Inform Assoc. 2007;14(1):110-117.

25.

Jamoom

Beatty

Bercovitz

Woodwell

Palso

Rechtsteiner

. Physician adoption of electronic health record systems: United States, 2011. NCHS Data Brief, No 98. 2012;1-8.

26.

Kruse

Kothman

Anerobi

Abanaka

. Adoption factors of the electronic health record: a systematic review. JMIR Med Inform. 2016;4(2):e19.

27.

Yonhap News. Cancer still leading cause of death in S. Korea. 2020. Accessed July 10, 2021. http://www.koreaherald.com/view.php?ud=20200922000692

28.

Kochanek

Arias

. Mortality in the United States, 2019. NCHS Data Brief, No 395. 2020;1-8.

29.

Shieh

Eklund

Sawaya

Black

Kramer

Esserman

. Population-based screening for cancer: hope and hype. Nat Rev Clin Oncol. 2016;13(9):550-565.

30.

Loud

Murphy

. Cancer screening and early detection in the 21st century. Semin Oncol Nurs. 2017;33(2):121-128.

31.

Feliciana Silva

Macedo da Silva Bonfante

Reis

André da Rocha

Pereira Lana

Leal Cherchiglia

. Hospitalizations and length of stay of cancer patients: a cohort study in the Brazilian Public Health System. PLoS One. 2020;15(5):e0233293.

32.

Furuno

Noble

McPherson

, et al. Variation in hospice patient and admission characteristics by referral location. Med Care. 2020;58(12):1069-1074.

33.

Axon

Turner

Buckley

. An update on inpatient hypertension management. Curr Cardiol Rep. 2015;17(11):94.

34.

Shan

Sarkar

Martin

. Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia. 2019;62(6):877-887.