Consumer Perspectives on Personal Health Records: A 4-Community Study

Objectives:

To characterize consumer attitudes toward personal health records (PHRs) in 4 diverse communities across New York state (NYS).

Study Design:

Combined analysis from four separate cross-sectional studies.

Methods:

We analyzed pooled data from surveys separately administered to 4 NYS communities. Results from individual communities have been previously published. However, pooling the data allowed us to conduct multivariable regression analyses that identified key factors associated with potential usage among a broad group of consumers.

Results:

We received responses from 701 consumers. A majority (74%) of respondents (n = 494) reported that they would use a PHR and the majority wanted a broad array of functionalities available. We found that potential PHR use was significantly associated with Internet use at least monthly (odds ratio [OR] = 5.8, 95% confidence interval [CI] = 3.3-10.2), a belief that PHRs may improve the security of health information (OR = 2.6, 95% CI = 1.5-4.7), and a belief that PHRs may improve quality of care (OR = 4.1, 95% CI = 2.6-6.6).

Conclusions:

As federal initiatives aim to improve healthcare, which includes making care more patient centered, PHRs will likely play an increasing role. Our results provide critical information to inform policy efforts, suggesting that PHRs must offer a broad range of patient-centered functionalities while maintaining high privacy and security standards to narrow the gap between reported interest and actual use. Ensuring widespread access to and frequent use of the internet among consumers will also be critical to avoid creating healthcare disparities through PHR use.

Am J Manag Care. 2014;20(4):287-296

In line with federal initiatives promoting electronic sharing of information between patients and providers, consumers across 4 diverse communities in New York State expressed strong interest in using personal health record (PHRs).

• Consumers are interested in using PHRs for a broad array of functionalities, particularly viewing medical records, test results, and medication lists online.

• To reduce the gap between potential and actual usage, PHRs must maintain high privacy and security standards and be perceived as improving quality of care.

• Focusing outreach efforts on individuals with poor Internet access and low usage will be critical to avoid creating healthcare disparities.

Unprecedented federal initiatives are under way to transform healthcare delivery by promoting health information technology (HIT).¹ One key focus of these initiatives is to deliver more patient-centered care. Personal health records (PHRs), which are HIT applications that allow consumers to electronically “access, manage, and share their health information… in a private, secure, and confidential environment” have potential to play a key role in these efforts.²

There are several PHR models. Tethered PHRs are integrated with an electronic health record (EHR) and receive automatically prepopulated information. Untethered PHRs are not linked and generally require users to enter and maintain data. Importantly, there are no specified standards for the content, functionalities, and architecture that should comprise a PHR, resulting in widespread variability.³

Multiple potential benefits exist with PHR use, although literature demonstrating actual benefits is limited.³ Theoretically, PHRs can allow consumers to become more actively engaged in managing their health and can improve patient and provider communication. Certain PHR features such as online appointment scheduling also should improve efficiency by allowing administrative issues to be handled electronically.⁴

Despite these benefits, PHR usage remains low, even though many nonusers endorse interest in using a PHR.⁵ Importantly, Stage 2 requirements for the EHR Incentive Program will require that providers give more than 50% of their patients online access to their health information.⁶ Furthermore, they require providers to demonstrate that 5% of their patients have accessed their health information online and that 5% have engaged in secure messaging. This will likely spur PHR adoption.

Addressing the gap between reported interest and actual usage requires more detailed information on consumer preferences for PHRs and barriers. In this way, PHRs can be better designed and marketed toward consumers broadly. Evidence already suggests that those with less access to and experience using the Internet, as well as people from certain minority groups and of lower socioeconomic status, may be less likely to use PHRs.^7-10 Finally, a detailed understanding of consumer preferences can help lead to more uniform standards for PHR products.

We therefore conducted this study to better understand consumer preferences for PHRs and to identify factors associated with potential usage. We used pooled data previously collected and published from individual communities within New York state (NYS) with HIT initiatives already under way to allow us to generate a more broadly representative assessment of consumer preferences and predictors of potential usage.^7,9,11,12

METHODS

Overview

We conducted cross-sectional surveys of consumers in 4 NYS communities to assess attitudes toward PHRs. This study was led by researchers from the Health Information Technology Evaluation Collaborative (HITEC), which comprises 4 research institutions across NYS (Weill Cornell Medical College, Columbia University, State University of New York at Albany, and University of Rochester) and is designated to evaluate NYS HIT and health information exchange (HIE) initiatives. The 4 communities, located in Buffalo, Rochester, the Hudson Valley, and Brooklyn, were all recipients of NYS grant funding to promote community-wide HIT adoption and HIE. They are geographically spread across NYS; include rural, urban and suburban areas; and are home to consumers who are socioeconomically, ethnically, and racially diverse.

Survey Design

The novel survey instrument was developed based upon literature review and consultation with experts in clinical informatics, healthcare quality, and survey methodology from Weill Cornell Medical College, Columbia University Medical Center, and the Center for Medical Consumers, a not-for-profit consumer advocacy organization. Wherever possible, survey items were drawn from previously validated questions from other national surveys.^13-16 The survey was piloted with 25 outpatients at an adult internal medicine clinic affiliated with an academic, tertiary hospital.

Survey Domains and Measurement

The survey domains included demographic characteristics, health characteristics, healthcare experience, Internet access and use, attitudes toward HIE and EHRs, and attitudes toward and preferences related to PHRs. In the survey, a PHR was described as a tool “primarily used by you to view your health information and manage your healthcare (for example, to make appointments) on the Internet.” We asked consumers about PHR preferences for content and functionalities, and questions regarding health characteristics such as health status, use of medications, treatment for chronic conditions, and caring for someone with a chronic condition. “Healthcare experience” included questions regarding satisfaction with care, interest in collaborative decision making, and problems understanding medical information. We also asked consumers about their access to and use of the Internet, concerns related to privacy and security, and attitudes toward HIE and EHRs.

Most questions were either yes/no, on a 5-point Likert-like scale, or 3-point questions (ie, improve/no effect/ worsen). The initial survey was administered in the Hudson Valley, and tailored slightly for each subsequent community. For example, in Brooklyn, “Russian” was explicitly listed for the question “What is the main language you speak at home?” whereas in the other communities, only “English, Spanish, Chinese, or Other” were answer choices. However, most questions were consistent across communities to allow for cross-community comparisons.

Survey Population and Administration

The mode of survey administration was not uniform. In the Hudson Valley, Cornell Survey Research Institute (CSRI) administered the survey via telephone from January through April 2008, using the Computer-Assisted Survey Execution System, which is a comprehensive computer-assisted interviewing system that can be used in all stages of data collection.¹¹ Survey respondents were identified using a random digit dial sample of fixed-line telephone numbers within the residential zip codes of the 8 Hudson Valley counties. Potential respondents were contacted by trained interviewers, consented using a standardized script, and offered a $10 incentive. A single phone number was called a maximum of 5 times. CSRI similarly administered the survey in Buffalo in February 2009, although no incentives were offered. Eligible adults were English-speaking residents of the 8 counties comprising the greater Buffalo region of NYS, which is served by the HEALTHeLINK regional health information organization (RHIO).

In Brooklyn, from October to November 2008, trained volunteers who were bilingual in Russian, Spanish, or Mandarin distributed the survey to consecutive adults who entered 3 ambulatory care sites and an emergency department participating in the community RHIO’s HIE pilot program. In Rochester, the survey was administered during August 2008 by research staff to consecutive adults presenting for a visit to 1 of 5 primary care practices involved in the RHIO’s HIE pilot program, which at the time included 4700 patients. For all sites, institutional review board approval was obtained.

Survey Analysis

In order to describe consumers’ potential use of PHRs, we examined responses to a survey question about the frequency of potential PHR use to view health information or manage healthcare. Except for 1 community, responses to this question were on a 5-point scale ranging from “rarely or not at all” to “about once daily.” The last community used a 5-point scale that ranged from “rarely or not at all” to “once a week.” To keep responses consistent across communities, we dichotomized respondents who indicated they would use the PHR “rarely or not at all” as nonusers while the remaining respondents were categorized as potential users. We also recategorized many of the independent variables, by either dichotomizing or reducing the number of categories, to be consistent across communities: language spoken at home, education, employment, children in the household, satisfaction with quality of care, doctor uses computer, frequency of Internet use, and each perceived benefit. Responses to age, income, health status, and understanding of health information were collapsed into 3 categories (or 4 for income) before merging community data. We also combined race and ethnicity into 1 binary variable (white and non-Hispanic vs nonwhite or Hispanic).

We used descriptive statistics to characterize respondents’ demographic and health characteristics, healthrelated experiences, Internet access and frequency of use, and attitudes toward PHRs. We examined bivariate associations between these variables and potential use of PHRs using χ² tests or Fisher’s exact tests for categorical variables. We selected those significantly associated with our outcome (P <.05) for inclusion into the multivariable model. To reduce the number of related factors included as independent variables, we considered pairwise percent agreement among the 6 questions inquiring about perceived PHR benefits.

We forced survey modality into the multivariable model to account for differences due to sampling approach and tested for interactions between survey modality and the independent predictors of PHR use. Significant interactions were included in the multivariable model. We then performed backward elimination to develop the most parsimonious model, requiring P <.05 to remain in the model. For significant interaction terms, we kept the main effects.

Data management and analyses were performed using SAS version 9.3 (SAS Institute, Inc, Cary, North Carolina).

RESULTSSample

The sample consisted of 701 respondents. Using American Association for Public Opinion Research standards, we achieved a response rate of 37% and cooperation rate of 85% in the Hudson Valley. In Buffalo the response rate was 35% and cooperation rate was 79%. Response rates and nonresponder characteristics are unavailable for Rochester and Brooklyn because individuals who refused to participate in the survey also refused to fill out the brief demographic portion of the survey.

Table 1

About 5% of the sample (n = 30) did not answer the PHR question that determined our outcome of interest; thus, they were excluded. The final sample for analysis was 671 consumers (). The majority of respondents were aged 35 to 64 years (n = 360, 54%), female (n = 422, 63%), and English speaking (n = 541, 81%). The vast majority reported having Internet access (n = 545, 81%) and using the Internet at least monthly (n = 516, 78%). Most respondents reported being in excellent or very good health (n = 343, 51%), although many reported having a chronic medical condition (n = 273, 41%). See the eAppendix for responses by community.

Current Use of PHR Functionalities

In 3 of 4 communities (the Hudson Valley, Rochester, and Buffalo) we assessed current use of functionalities associated with PHRs. Usage was universally low. Only 4% of respondents (n = 21) reported viewing test results online, 5% (n = 24) reported viewing medication lists online, 4% (n = 20) reported viewing medical records online, and 6% (n = 29) reported requesting appointments, referrals, or prescription refills online.

Interest in Using a PHR

Overall, 74% of respondents (n = 494) expressed interest in using a PHR, while only 26% (n = 177) reported they would not use a PHR.

Consumer Preferences for PHR Content and Features

Table 2

Among respondents not currently performing healthcare- related activities online, most were interested in conducting a wide variety of activities using a PHR (). Highest interest was for viewing medical records, test results, and medication lists (n = 297, 67% of respondents in the 3 communities surveyed), followed by accessing a child or parent’s medical record (n = 412, 61% of respondents in the 4 communities surveyed) and sending e-mails with medical questions (n = 404, 61% of respondents in the 4 communities surveyed). Lowest interest was for communicating with other people with similar health problems (n = 138, 34% of respondents in the 2 communities surveyed).

Perceived Potential Benefits of PHRs

Two-thirds of respondents believed that using a PHR would improve their understanding of their health (n = 420, 66%), their sense of control over their own healthcare (n = 406, 65%), and their satisfaction with their healthcare (n = 363, 58%), safety (n = 365, 59%), and quality (n = 390, 63%) (bottom, Table 1). However, among respondents who would not use a PHR, less than half perceived benefits in any category. Only 16% of nonusers (n = 27) and 38% of potential users (n = 181) believed PHR use would improve security and privacy.

Bivariate Results

Figure

In bivariate analysis, we found that potential PHR use is significantly higher among the following consumers: (1) those who perceive greater potential benefits from PHR use, particularly improved understanding of and control over one’s health; (2) younger consumers (most notably those aged 18-34 years); (3) Hispanic and other minorities; (4) those for whom English is not their primary language; (5) employed consumers; (6) consumers with children; (7) consumers with some college education or more; (8) frequent Internet users and those with Internet access; and (9) those with greater understanding of their doctor ().

There were significant interactions between survey modality and age and Internet access when predicting PHR use.

Multivariable Results

Table 3

Adjusting for survey modality, age, and the varying effects of age by survey modality, we found that potential PHR use is significantly associated with use of the Internet at least monthly (odds ratio [OR] = 5.8, 95% confidence interval [CI] = 3.3-10.2), a belief that PHRs may improve security of health information (OR = 2.6, 95% CI = 1.5- 4.7), and a belief that PHRs may improve quality of care (OR = 4.1, 95% CI = 2.6-6.6) ().

DISCUSSION

We found that most consumers surveyed in 4 diverse NYS communities were interested in using PHRs, although current usage was low. Consumers who used the Internet at least monthly, or who believed that using a PHR would improve quality of care, or who believed that the security of health information would be improved through PHR use, were significantly more likely to potentially use a PHR. This study of over 700 consumers is one of few to identify factors associated with potential PHR use and to describe in detail consumer preferences for PHR functionalities. These results can provide valuable information to inform current federal initiatives that are changing the way in which hospitals and providers give patients access to medical information.

Our finding that PHR uptake does not match reported interest, even among communities where HIT is being actively promoted, is similar to results from other studies.¹⁷ A 2008 Markle Foundation survey found that while 46.5% of respondents were interested in using a PHR, only 3% were current users.¹⁸ Two years later, usage was only 10%.¹⁹

The recent release of Stage 2 Meaningful Use criteria is likely to accelerate PHR adoption, as providers will be required to give patients online access to health information and demonstrate that at least 5% of their patients have accessed the system and used secure messaging.⁶ Results from our multivariable analysis can aid health policy makers and HIT vendors by identifying factors most strongly associated with potential PHR usage, thereby allowing targeted initiatives or public outreach campaigns focused in these areas. In our multivariable model, consumers who perceived PHRs as increasing quality of care were much more likely to endorse potential PHR usage. Understanding consumer preferences for PHRs and tailoring products accordingly can improve their perceived usefulness. This is particularly important given that many PHRs are physician- rather than patient-oriented.³

Consumers in our study desire a broad array of PHR functionalities, including the ability to view medical records and test results, as well as directly e-mail providers. However, research suggests that providers have significant concerns about how patients will interpret medical information without traditional framing by a provider.²⁰ In addition, physicians appear concerned about potential increases in their workload and responsibilities related to PHR-based communication with patients. Without focus on the needs of both patients and providers, it is unlikely that PHRs will be widely utilized.

In our multivariable model, potential PHR usage was also strongly associated with Internet use at least monthly. However, over one-fifth of the US population reports not using the Internet.²¹ Senior citizens and those with lower educational or income levels—high risk-groups in the healthcare system&mdash;are disproportionately represented among nonusers, in part due to lack of Internet access.²² This is consistent with results from our bivariate analyses, although age and employment did not achieve significance in the multivariable model due to their similarity with frequency of Internet use, which was more predictive of PHR usage. Therefore, policy makers must focus on ways to address gaps in Internet accessibility— such as through subsidies for high-speed Internet access or enhanced funding for community access centers&mdash;to promote broad-based PHR use and avoid creating a digital divide among consumers.

Finally, results from our multivariable model suggest that security issues must be prioritized. Respondents who felt that PHR use would increase health information security were almost 2.5 times more likely to use a PHR. The perception that PHRs may actually increase health information security is not generally reported, and worth further exploration. However, literature suggests that while privacy and security concerns may prove a barrier to adoption, for actual users, security is much less of a concern.^23,24 Nonetheless, a recent review of PHR privacy policies found that while 71% of PHR systems allowed users to grant and revoke access to their health information, only 38% actually permitted users to check who had accessed their data.²⁵ Creating uniform privacy and security standards for PHRs will therefore be essential tasks of health policy makers in order to encourage PHR use.

Limitations

This study has several limitations. First, slightly different versions of the survey were administered to each community. However, much of the content was identical or near-identical, allowing for comparison of answers. Second, mode of survey administration varied and in-person survey administration was associated with a higher likelihood of PHR use, indicating possible response bias. It may also be that patients who choose to receive care at practices participating in HIT initiatives are more receptive to HIT. We accounted for differences in survey modality in our multivariable model and examined interactions with other predictors, but there may still be some remaining unexplained bias in our results. Third, we cannot comment on the number and characteristics of nonrespondents in all communities, which may result in systematic biases. Lastly, our survey was administered only in NYS, limiting generalizability.

CONCLUSIONS

Our study is one of only a few large quantitative studies to provide detailed information on consumer preferences for PHR functionalities and to identify key factors associated with potential usage. Our results suggest that PHRs should offer a broad range of patient-centered functionalities while maintaining high privacy and security standards. Initiatives targeting groups with poor Internet access, low Internet use, and socioeconomic challenges will be critical to ensure that healthcare disparities are not created. Given current federal policies incentivizing PHRs, our results provide important information to inform these efforts.Author Affiliations: Department of Pediatrics, Weill Cornell Medical College, New York, NY (ELA, VP, RK); Department of Public Health, Weill Cornell Medical College, New York, NY (RLA, VP, AE, RK); NewYork-Presbyterian Hospital, New York, NY (ELA, RK); Health Information Technology Evaluation Collaborative, New York, NY (ELA, VP, AE, RK); Center for Healthcare Informatics and Policy, New York, NY (ELA, AE, RK); Department of Medicine, Weill Cornell Medical College, New York, NY (RK).

Source of Funding: None reported.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (EA, VP, RK); acquisition of data (VP); analysis and interpretation of data (EA, VP, AE, RK); drafting of the manuscript (EA); critical revision of the manuscript for important intellectual content (EA, VP, AE, RK); statistical analysis (AE); provision of study materials or patients (RK, VP); obtaining funding (RK); administrative, technical, or logistic support (RK); supervision (EA, RK).

Address correspondence to: Erika L. Abramson, MD, MS, Assistant Professor of Pediatrics and Public Health, Weill Cornell Medical College of Cornell University, 525 E 68th St, Rm M-610A, New York, NY 10065. E-mail: err9009@med.cornell.edu.1. Health Information Technology: Initial Set of Standards, Implementation Specifications, and Certification Criteria for Electronic Health Record Technology; Final Rule, 45 CFR Part 170 (2010).

2. The Markle Foundation. Connecting for Health. The personal health working group final report. Published 2003.

3. Archer N, Fevrier-Thomas U, Lokker C, McKibbon KA, Straus SE. Personal health records: a scoping review. J Am Med Inform Assoc. 2011;18(4):515-522.

4. Tang PC, Ash JS, Bates DW, Overhage JM, Sands DZ. Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption. J Am Med Inform Assoc. 2006;13(2):121-126.

5. Lake Research Partners. Consumers and Health Information Technology: A National Survey. 2010.

6. Health Information Technology: Standards, Implementation Specifications, and Certification Criteria for Electronic Health Record Technology, 2014 Edition; Revisions to the Permanent Certification Program for Health Information Technology. In: Services HaH, ed. 2012.

7. Patel VN, Abramson E, Edwards AM, Cheung MA, Dhopeshwarkar RV, Kaushal R. Consumer attitudes toward personal health records in a beacon community. Am J Manag Care. 2011;17(4):e104-e120.

8. Ancker JS, Barron Y, Rockoff ML, et al. Use of an electronic patient portal among disadvantaged populations [published online June 6, 2008]. J Gen Intern Med. 2011;26(10):1117-1123.

9. Patel VN, Dhopeshwarkar RV, Edwards, et al. Low-income, ethnically diverse consumers’ perspective on health information exchange and personal health records. Inform Health Soc Care. 2011;36(4):233-252.

10. Kim EH, Stolyar A, Lober WB, et al. Challenges to using an electronic personal health record by a low-income elderly population. J Med Internet Res. 2009;11(4):e44.

11. O’Donnell HC, Patel V, Kern LM, et al. Healthcare consumers’ attitudes toward physician and personal use of health information exchange. J Gen Intern Med. 2011;26(9):1019-1026.

12. Patel VN, Dhopeshwarkar RV, Edwards A, Barron Y, Sparenborg J, Kaushal R. Consumer support for health information exchange and personal health records: a regional health information organization survey. J Med Syst. 2012;36(3):1043-1052.

13. Agency for Healthcare Research and Quality. CAHPS Clinical & Group Survey. 2012.

14. Agency for Healthcare Research and Quality. Medical Expenditure Panel Survey.

15. Ware J Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233.

16. South Dakota e-health Collaborative. The South Dakota Electronic Health Record Assessment Survey: Healthcare Consumer. 2008.

17. Wen KY, Kreps G, Zhu F, Miller S. Consumers’ perceptions about and use of the internet for personal health records and health information exchange: analysis of the 2007 Health Information National Trends Survey. J Med Internet Res. 2010;12(4):e73.

18. Westin A. Americans Overwhelmingly Believe Electronic Personal Health Records Could Improve Their Health. The Markle Foundation; 2008.

19. The Markle Foundation. PHR Adoption On the Rise. http://www .markle.org/publications/1440-phr-adoption-rise. Published 2011. Accessed September 24, 2012.

20. Yau GL, Williams AS, Brown JB. Family physicians’ perspectives on personal health records: qualitative study. Can Fam Physician. 2011;57(5):e178-e184.

21. Zickuhr K, Smith A. Digital differences. Pew Research and Internet Project website. http://www.pewinternet.org/2012/04/13/digitaldifferences/. Published April 13, 2012.

22. Schickedanz A, Huang D, Lopez A, et al. Access, interest, and attitudes toward electronic communication for healthcare among patients in the medical safety net. J Gen Intern Med. 2013;28(7):914-920.

23. Hassol A, Walker JM, Kidder D, et al. Patient experiences and attitudes about access to a patient electronic healthcare record and linked web messaging. J Am Med Inform Assoc. 2004;11(6):505-513.

24. Emani S, Yamin CK, Peters E, et al. Patient perceptions of a personal health record: a test of the diffusion of innovation model. J Med Internet Res. 2012;14(6):e150.

25. Carrion Senor I, Fernandez-Aleman JL, Toval A. Are personal health records safe? a review of free web-accessible personal health record privacy policies. J Med Internet Res. 2012;14(4):e114.