Jessica S. Ancker, MPH, PhD

The proportion of US doctors using electronic health records (EHRs) has risen sharply in response to the federal Meaningful Use (MU) program, which incentivizes EHR adoption. To track consumer perceptions of EHRs during this period, we conducted a national telephone survey annually for 3 consecutive years, from 2011 to 2013, corresponding with the early years of MU.

The survey used random digit-dial sampling on a dual frame of landline and cell phone numbers in the continental United States, but was not otherwise stratified by geographic region, race, or other variables. Because our primary goal was to identify relationships between variables and EHR attitudes, we constructed post hoc survey weights to align all 3 samples with each other. Relationships between sociodemographics and EHR questions were assessed with logistic regression models using the survey weights. Cross-year comparisons were conducted with χ2 tests and Cochran-Armitage tests for linear trend.

Between 2011 and 2013, the proportion of respondents with a doctor who used an EHR rose from 64% to 71%. In 2011, 64% endorsed the belief that EHRs would improve healthcare quality, dropping to 62% by 2013. Simultaneously, the proportion concerned about the effects of EHRs on privacy dropped from 48% to 41%. Consumers whose doctors used EHRs were generally more likely to believe EHRs would improve healthcare quality and less concerned about privacy risks than those whose doctors did not use EHRs. 

Overall, we conclude that during the early years of the MU program, exposure to EHRs increased while confidence in the benefits of EHRs and concerns about privacy risks became less marked. The subset of people exposed to EHRs via their physicians continued to have more positive attitudes toward them than those without that exposure. These attitudinal trends may be linked to increased familiarity with health information technology.

		Between 2011 and 2013—the early years of the Meaningful Use electronic health record (EHR) incentive program&mdash;surveys found a drop in the number of consumers who believed that EHRs would improve healthcare quality, as well as in the number who expressed concern about the effects of EHRs on privacy. 

		Consumers whose doctors used EHRs were generally more likely to believe EHRs would improve healthcare and less concerned about privacy risks than those whose doctors did not use EHRs.

Since the passage of the Health Information Technology for Economic and Clinical Health Act (HITECH) in 2009, the federal government has invested billions of dollars to promote the adoption of the electronic health record (EHR) through the Meaningful Use program (MU).

 The long-term goal of this program is to improve the quality and safety of healthcare.

 Since the launch of MU, the prevalence of EHRs has increased dramatically, with about 72% of ambulatory physicians and 44% of acute care hospitals using some form of EHR as of 2012.

National and regional surveys conducted before and in the early years of MU generally suggest that consumers believe EHRs are likely to improve healthcare.

 We recently found that most consumers think that EHRs will improve healthcare, and positive opinions were even more common among those who said their doctors use EHRs.

 However, a relatively large percentage of respondents (18%) did not know whether their physicians used EHRs.

Despite this overall positive perception of EHRs, consumers express strong concerns about the security of their electronic medical data.

 Privacy concerns are generally associated with lower confidence that EHRs will improve healthcare

 and with an increased level of concern about personal medical data breaches.

 Agaku and colleagues recently reported that about 12% of patients have withheld health information from doctors out of concern about the privacy of electronic data.

 Privacy concerns are also common with regard to health information technologies (ITs) that support the electronic exchange of medical records between healthcare providers (ie, health information exchange).

 Our objective was to track national consumer perceptions of quality effects and privacy risks associated with EHRs over a 3-year period (2011-2013), corresponding with the early years of MU.

The Cornell National Social Survey is an annual random digit-dial telephone survey conducted by the Cornell Survey Research Institute. Every year, the sample size of 1000 provides a margin of error of ± 3.1 percentage points. The Cornell University Institutional Review Board approved the study and respondents provided oral consent.

Each year’s sample is a random digit-dial sampling conducted on a dual frame of landline and cell phone numbers in the continental United States, but not otherwise stratified by geographic region, race, or other variables. The proportion of cell phone numbers is calculated from county-level data on prevalence of cell phone-only households. Listed and unlisted numbers are included in the sample list, but known business, disconnected, and nonhousehold numbers are excluded. When the telephone is answered, the adult with the most recent birthday is interviewed—a technique that ensures each adult has an equal chance of selection.

Researchers may submit potential questions for inclusion in the survey, which are competitively reviewed by the Cornell Survey Research Institute. Three questions about EHRs were included for 3 consecutive years: 2011, 2012, and 2013. The questions, adapted from previous surveys

1. An electronic medical record is a computer-based version of the patient’s medical record. Does your doctor use an electronic medical record for you? (Answer options: yes/no/not sure/have no doctor.)

2. If doctors used electronic medical records instead of paper records, how do you think that would affect the quality of medical care? (Answers were based on a 5-point Likert scale from “greatly improve it” to “greatly worsen it”; midpoint at “no effect.”)

3. If doctors used electronic medical records instead of paper records, how do you think that would affect the privacy and security of medical information? (Answers were based on a 5-point Likert scale from “greatly improve it” to “greatly worsen it”; midpoint at “no effect.”)

No additional explanation was provided for the questions.

For the definition of an EHR, we deliberately used the label “electronic medical record” and wording similarly used by the California Healthcare Foundation so that our findings would be comparable with their 2010 survey, which reported that about 26% of consumers had physicians using an EHR.

The survey was conducted in English only, and data collection closed in November or December of each year. Survey administration took approximately 20 to 25 minutes.

Exploratory comparisons of our samples with national census data suggested that respondents were representative in terms of gender distribution, age, and employment status, but were somewhat more educated, affluent, white, and non-Hispanic, most likely due to differential response rates in different populations. Because our primary goal was not to estimate strength of support for EHRs nationally, but rather to identify relationships between variables and EHR attitudes, we opted not to construct post hoc survey weights to bring the sample closer to national distributions on all demographics. Instead, we constructed post hoc survey weights to align all 3 samples with each other. This ensures that year-to-year differences cannot be attributed to random variation in demographics from sample to sample. The normalized survey weights corrected for slight differences in the distribution of education and income from year to year. Two individuals were excluded for missing demographics, leaving a total sample size of 2998 over the 3 years. We report the weighted frequencies and percents. Relationships between sociodemographics and EHR questions were assessed with logistic regression models using the survey weights. Cross-year comparisons were conducted with χ

 tests and Cochran-Armitage tests for linear trend. Analyses were conducted in SAS version 9.3 (SAS Institute, Cary, North Carolina).

Each year, 67.1% to 71.4% of eligible respondents reached by telephone consented to participate (the “cooperation rate” according to American Association of Public Opinion Research definitions

). The samples were representative of the US population in terms of age, sex, and employment, but whites, non-Hispanics, and well-educated respondents were somewhat overrepresented (

The proportion reporting a doctor using an EHR rose over time (63.9% in 2011 to 71.1% in 2013; 

). The proportion endorsing the belief that EHRs would improve healthcare quality decreased modestly from 64% in 2011 to 61.5% in 2013, while the percentage of those who believed EHRs would worsen healthcare quality rose from 7% to 8.7% (

). The proportion believing EHRs would have no effect on quality rose from 25.5% to 29.6% over the same time period (test for linear trend, 

 = .009). The proportion expressing concerns that EHRs would worsen privacy and security decreased from 47.5% in 2011 to 41.2% in 2013 (test for linear trend, 

). More than one-fifth of respondents believed that EHRs would actually improve privacy and security.

In every year, the belief that EHRs would improve healthcare quality was more common among wealthier individuals, those who were employed, and those who used the Internet daily (

). In addition, this belief was significantly less common among individuals concerned that EHRs would worsen privacy and security. Respondents who reported having a doctor that uses an EHR were more likely to believe EHRs would improve healthcare quality, although the effect narrowly missed statistical significance in 2013. In the first 2 years of the survey, college graduates were less concerned about privacy and security, but in the final year they became more likely than other respondents to express concerns (

In addition to college graduates, concerns about the effect of EHRs on privacy and security were consistently less common among individuals whose physician used an EHR. However, this concern was significantly more common among individuals who identified themselves as politically conservative.

During a 3-year period in which Americans became more likely to report seeing doctors who use EHRs, there were modest but statistically significant decreases in both optimism about EHR effects on quality and concerns about EHR-related privacy and security risks. Familiarity with a doctor using an EHR was generally positively associated with the belief that EHRs would improve healthcare quality as well as with less concern about EHR risks to privacy. Our 3-year trend results show an association between EHR exposure and perception of quality benefits that is consistent with prior national surveys over shorter timelines.

As national discourse about the positive and negative aspects of EHRs continues, consumers may be forming their opinions on the basis of a variety of sources of information. Both positive and negative perspectives have been publicized in the media; in addition, physicians may talk to their patients about their enthusiasm for or frustration with EHRs. Finally, more consumers have had a personal experience with a doctor using an EHR—some of whom may have felt that the computer made the visit more impersonal (as shown in a Veterans Administration patient survey

), whereas others may have noticed improvements in physician communication skills (demonstrated in a small study of young physicians

Despite significant overall decreases in concerns about privacy and security, the perceived risks in these areas were negatively associated with the perceived benefits of EHRs. Thus, consumers who were more concerned with EHR-related privacy and security risks were less likely to perceive EHRs as likely to benefit quality of care. It is logically possible for quality benefits to be independent of privacy risks—for example, an EHR could support more personalized care, and yet it could also be hacked&mdash;but it is well-established that perceived benefits of almost any activity tend to decrease as its perceived risks grow.

 As a result, it is likely that privacy concerns may be one of the causes of lower perceived benefits and, therefore, explicitly addressing consumer privacy concerns would lead to a more positive view of EHRs overall.

Awareness of medical data breaches has likely risen since 2009 when federal regulations first mandated that large breaches be reported to news media and to the HHS.

 The number of data breaches publicly reported recently reached 1000, affecting nearly 32 million Americans.

 Contact from healthcare organizations or news stories about these breaches (eg, the 

) could be raising public awareness of threats to privacy and security. It is also noteworthy that Edward Snowden’s revelations about National Security Agency (NSA) surveillance of electronic communication

 occurred in June 2013, several months before the third year of data collection. It is possible that prominent news stories such as these could be linked to the change we observed in the opinions of college graduates, who were less likely to be concerned about EHR privacy threats in 2011 and 2012, but became significantly more likely to be concerned the following year. Additionally, a 2014 Harris poll has linked increased caution online to awareness of NSA digital surveillance.

Nevertheless, in spite of these privacy concerns by many consumers, a substantial minority of respondents (up to 21% in our data) perceived health IT as a way to improve privacy and security.

 These consumers may believe electronic protections to be stronger than the protections for paper records or they may have had negative experiences with paper records.

Random digit-dial surveys have limitations, including sampling coverage and nonresponse bias.

 One advantage of dual-frame methodology is the inclusion of cell phone households, which is particularly more inclusive of younger demographics than a landline-only survey.

 The sampling strategy was random, so we did not apply survey weights in order to match demographic characteristics of the country. As a result, although our sample was fairly representative in gender, age, and employment, our respondents were somewhat more educated and affluent, more likely to be white, and less likely to be Hispanic than the national population—most likely because of differential response rates. The questions in the survey have not been validated. The use of the term “electronic medical record” in the survey does not reflect the national consensus that “electronic health record” is the most accurate term for describing contemporary IT with certain capacities for interoperability. On the other hand, it is not clear that the general public makes a distinction between these terms, and we sought to minimize any confusion by providing a brief definition of the term&mdash;“an electronic medical record is a computer-based version of the patient’s medical record”&mdash;in the question stem. Lastly, the study design was cross-sectional, so association does not imply causation.

During the launch and early years of the MU program, Americans became more likely to report having a physician who used an EHR. Individuals whose doctors used EHRs tended to have more positive opinions of EHRs. Nevertheless, over the 3-year study time frame, both confidence in the benefits of EHRs and concerns about privacy risks became less marked overall, a pair of trends that might be attributed to increased familiarity with health IT. Opinions on electronic privacy threats remain split, with a plurality expressing privacy concerns about EHRs —a concern generally linked to lower perceived benefit&mdash;along with a substantial minority believing that EHRs will improve medical privacy and security.

 Department of Healthcare Policy and Research (JSA), Weill Cornell Medical College (JR, MS, RK), New York, NY; Stanford School of Medicine (SB), Palo Alto, CA.

 The survey was funded by the Cornell University Office of the Provost. Dr Ancker has received funding from an AHRQ grant (K01HS021531).

 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.

 Concept and design (JSA, RK, SB); acquisition of data (JSA); analysis and interpretation of data (JSA, RK, SB, JER, MS); drafting of the manuscript (JSA, RK, SB, JER, MS); critical revision of the manuscript for important intellectual content (JSA, RK, SB, JER); statistical analysis (JSA, SB, MS); and supervision (JSA).

 Jessica S Ancker, MPH, PhD, 425 E. 61st St, Ste 301, New York, NY 10065. E-mail: jsa7002@med.cornell.edu.

Articles

During the early years of the "meaningful use" program, surveys found decreases in both optimism and concerns about electronic health records.

Trends in Public Perceptions of Electronic Health Records During Early Years of Meaningful Use

Health information technology (HIT) is widely viewed as an important lever with which to improve the quality and efficiency of the healthcare system. However, there has long been debate about its financial effects.

To characterize the existing data on the financial effects of HIT and to consider the implications for the effect of HIT on healthcare spending.

 Systematic literature review. Methods: We identified articles by (1) searching PubMed using the intersection of terms related to HIT applications and terms related to financial or economic effect; and (2) reviewing the reference lists of the included articles as well as additional policy articles and literature reviews.

 A total of 57 articles met our inclusion criteria, including 43 articles (75%) reporting financial benefits to a stakeholder associated with HIT. These included 26 articles (46%) reporting cost savings, 6 articles (11%) reporting revenue gains, and 11 articles (19%) reporting a mixture of cost savings and revenue gains. Among articles with experimental study designs, 22 of 34 (65%) reported financial benefits; and among articles explicitly measuring costs and benefits, 19 of 21 (90%) reported financial benefits. The most prevalent mechanisms were savings on administrative goods and/or personnel, savings on pharmaceuticals, and revenue gains through improved billing. Overall there is a dearth of articles on this topic, especially ones with strong study designs and financial analyses.

 HIT can have financial benefits, but more research is required, especially on HIT’s effects under emerging delivery and payment reform efforts.

Am J Manag Care. 2013;19(11 Spec No.10):SP369-SP376

Although health information technology (HIT) interventions are associated with financial effects, including cost savings and revenue gains, there are few articles on this topic, especially ones with strong study designs and financial analyses.

		Benefits are consistent across different settings and technologies.

		Prevalent mechanisms for financial effects include savings on administrative goods and/or personnel, savings on pharmaceuticals, and revenue gains through improved billing.

		Among articles reporting financial benefits, providers benefited in a majority of them, and payers in a minority of them.

		It is unclear from current evidence whether HIT on its own can reduce general healthcare spending.

Health information technology (HIT) is an important lever with which to improve the quality and efficiency of the healthcare system.

 The federal government’s belief in the importance of HIT motivated a commitment of up to $30 billion in funding for HIT as part of the Medicare and Medicaid Electronic Health Record (EHR) Incentive Program and related efforts through the American Recovery and Reinvestment Act of 2009.

Despite the promise of HIT, there has long been debate about its financial effects, both on individual providers and payers on the  microeconomic level and on the US healthcare system at the macroeconomic level.

 This question has become especially significant given the great interest within the healthcare industry and among policy makers in finding ways to control growing healthcare costs. State and federal governments are the nation’s largest healthcare payers and have invested heavily in HIT. As a result, they have a strong interest in understanding its financial effects.

To date, several groups of researchers have reviewed the literature to understand the quality and efficiency effects of HIT in general or of specific types of HIT, including EHRs, computerized physician order entry (CPOE), and clinical decision support (CDS).

 Notably, Chaudhry and colleagues conducted a systematic review of articles published between 1995 and January 2004 to assess the effects of HIT on quality, efficiency, and cost.

 Two subsequent reviews, conducted by Goldzweig and colleagues

 (June 2004 to June 2007) and Buntin and colleagues

 (July 2007 to February 2010), updated that research, though each explored new themes. In 2008, the Congressional Budget Office assessed evidence on the costs and benefits of HIT to offer guidance for the federal government’s HIT strategy.

Although several of the above articles explored HIT’s effect on cost, none compiled data on the financial effects of HIT in a systematic way. In at least 2 cases the authors cited a paucity of articles addressing HIT’s effect on costs.

 In addition, previous articles have not compared the financial effects and their mechanisms across different types of clinical settings and technologies. We systematically reviewed the literature to characterize the existing data on the financial effects of HIT and considered the implications for HIT’s effect on healthcare spending.

We limited our review to articles investigating the effects of 4 types of HIT applications used by healthcare providers in the delivery of care: EHRs, CPOE, CDS, and health information exchange (HIE). These applications were chosen because they have been the subject of the bulk of the debate about the potential beneficial effects of HIT and are central to the meaningful use criteria for the Medicare and Medicaid EHR Incentive Program. We used well-established definitions for EHR, CPOE, and CDS, most notably documented in an article by Blumenthal and colleagues on HIT.

 We defined HIE as systems or applications that connect HIT systems maintained by separate healthcare providers, payers, and other stakeholders, thus allowing providers to share electronic information about common patients. 

In addition, our inclusion criteria required that studies (1) explore a financial effect as a principal outcome measure (alone or in combination with other outcomes); (2) quantify the effect in monetary terms for 1 or more stakeholders (articles reporting other related measures, such as length of stay or other types of utilization, were excluded unless the effect was explicitly measured in monetary terms); (3) present primary research rather than a compilation or review of existing literature; (4) be published in an English-language, peer-reviewed journal since 2000; and (5) be set in the United States, since we reasoned that the unique characteristics of this country’s healthcare system—specifically those impacting the financing, adoption, and use of HIT systems&mdash;would render foreign studies’ findings less relevant to our objectives. Finally, in the event authors had written more than 1 qualifying study on the effect of the same HIT application on a similar setting or population, we included only the most recent article.

Our search for candidate articles consisted of 2 phases. In the first phase, we searched PubMed in February 2012, using the intersection of 2 lists of search terms: the first related to HIT applications and the second related to financial or economic effects (

). In the second phase, we identified other relevant studies by reviewing the reference lists of the included articles as well as additional policy articles and literature reviews.

The team developed a list of data items to be extracted from the articles (

). Key data fields included the following:

Health Information Technology Application. 

We documented the primary HIT application under investigation: EHR, CPOE, CDS, HIE, or multiple. Because nearly all CPOE applications in the literature included CDS and because most CDS applications were part of a CPOE application, we merged those 2 categories into 1 category. Otherwise, when an article included more than 1 of these applications, we assigned the article a single designation based on the emphasis of the article.

 We documented the primary clinical setting for the study. These included: emergency department (ED), inpatient, outpatient, or multiple.

We classified each study according to its design. A rating of 1 indicated experimental studies, including randomized controlled trials. A rating of 2 indicated observational studies with concurrent control groups. A rating of 3 indicated observational studies with historical controls. A rating of 4 was given to case studies, case series, or other reports in which no control group was included  or experimental design described. A rating of 5 was assigned to quantitative simulations where outcomes were modeled based on inputs such as literature review, expert analysis, and projections.

 We categorized the studies’ financial outcomes as follows: (1) there was a positive effect for the stakeholder(s) of interest in the study; (2) there was a neutral or mixed effect for the stakeholder(s); or (3) there was a negative effect for the stakeholder(s). In addition, we classified  each study according to whether or not it documented the costs of the HIT intervention to stakeholders as well as the benefits.

 Finally, we documented to which stakeholder(s) the financial benefit or loss accrued. The 4 stakeholder categories were (1) provider, (2) payer, (3)  consumer, and (4) the community, society, or health system at large. It was not always clearly stated which stakeholder accrued the benefit or loss, so in those instances we used our best judgment.

Three different team members, including 2 health services researchers and a policy analyst, were responsible for reviewing and extracting the data items from each article. All extracted data were discussed and discrepancies were resolved through consensus.

We calculated counts and percentages for each category of interest and graphed the principal outcomes for all articles meeting our inclusion criteria. We also conducted 2 sensitivity analyses. In the first sensitivity analysis, we assessed whether our results would differ if we included only articles with study design ratings of 1, 2, or 3 (experimental studies or observational studies with concurrent or retrospective controls), because those studies might be considered more reliable than the others. In the second sensitivity analysis, we assessed whether our results would differ if we included only articles that documented both the cost and benefit of the HIT intervention, because we considered this documentation to be indicative of at least a basic rigor in the economic analysis.

We reviewed 4600 search results. Based on their abstracts, we selected 96 (2%) for full-text review and added 24 others that we identified from the bibliographies of other articles on  this topic. Of these 120 articles, 57 articles (48%) met our inclusion criteria and were the basis for our analysis. (

The 57 articles were very heterogeneous. For example,  they ranged from studies of single CDS rules to multifunctional EHRs; from studies set in single solo or small practices to studies of the US healthcare system; from studies lasting a few months to those covering multiyear spans; and from studies where financial effects were the only outcome under investigation to those where the financial effect was one of several outcomes.

More than half (30/57, or 53%) of the articles investigated interventions solely in outpatient settings, while 17 articles (30%) focused on interventions in the inpatient setting, and 6 articles (11%) investigated interventions in the ED setting (

). Twenty-six articles (46%) explored EHRs, 24 articles (42%) explored CPOE/CDS, and 5 articles (9%) investigated HIE.

Among the outpatient articles, there were 17 (57%) on EHRs and 13 (43%) on CPOE/CDS. Among the inpatient articles, 10 articles (59%) focused on CPOE/CDS. Among the ED articles, 4 (67%) investigated the effect of HIE.

A majority of the articles (34/57 or 60%) were either experimental studies or observational studies with concurrent or retrospective controls (ratings of 1, 2, or 3). A total of 11 articles (19%) were case studies (study rating of 4), and 12 articles (21%) were models or projections (study rating of 5) (Table).

Financial Effects of Health Information Technology

Three-fourths of the articles reported financial benefits for stakeholders (43/57 or 75%), while 10 articles (18%) reported a mixed or neutral effect, and 4 articles (7%) reported a negative effect (

). Cost savings were reported by 26 articles (46%), 6 articles (11%) reported revenue gains, and 11 articles (19%) reported a mixture of cost savings and revenue gains. Financial benefits were reported consistently across different types of setting (

) and different types of HIT applications (

). However, only a minority of articles (22/57 or 39%), including 20 of the 43 articles (47%) reporting benefits, included the costs of the intervention.

As described above, it was not always explicitly stated which stakeholders benefited from the HIT implementation. Among the 43 articles reporting positive financial outcomes, providers benefited, or appeared to benefit, in 32 articles (74%); payers benefited in 12 articles (28%); society benefited in 5 articles (12%); and consumers benefited in 1 article (2%).

Although health information technology interventions are associated with cost savings and revenue gains, there still are few articles on this topic.

Jessica S. Ancker, MPH, PhD

Articles

Trends in Public Perceptions of Electronic Health Records During Early Years of Meaningful Use

Financial Effects of Health Information Technology: A Systematic Review