R. Adams Dudley, MD, MBA

To assess clinician attitudes and experiences in Health eHearts, a quality recognition and financial incentive program using health information technology.

 A survey was administered to 140 lead clinicians at each participating practice. Survey domains included clinicians’ experiences and attitudes toward the selected clinical quality measures focused on cardiovascular care, use of electronic health records (EHRs), technical assistance visits, quality measurement reports, and incentive payments. Responses were compared across groups of practices receiving financial incentives with those in the control (no financial rewards).

Survey response rate was 74%. The majority of respondents reported receiving and reviewing the quality reports (89%), agreed with the prioritization of measures (89%), and understood the information given in the quality reports (95%). Over half of the respondents had a quality improvement visit (56%), with incentive clinicians more likely to have had a visit compared with the control group (68% vs 43%, 

 = .01). The incentive group respondents (92%) were more likely to report using clinical decision support system alerts than control group respondents (82%, 

 Clinicians in both incentive and control groups reported positive experiences with the program. No differences were detected between groups regarding agreement with selected clinical measures or their relevance to the patient population. However, clinicians in the incentive group were more likely to review quarterly performance reports and access quality improvement visits. Incentives may be used to further engage clinicians operating in small independently owned practices to participate in quality improvement activities.

Am J Manag Care. 2013;19(11 Spec No. 10):eSP12-eSP18

		With adequate technical support, small practices can be engaged in recognition and financial rewards programs.

		Clinician buy-in to the design of the program was high. A majority of the clinicians reported receiving, reviewing, and understanding the quality reports; were in agreement with the focus on cardiovascular quality measures; thought the measures were clinically meaningful; and understood the information.

		Financially incentivized clinicians were slightly more engaged and participated in quality improvement visits and trainings, such as using clinical decision support systems and other electronic health record functionalities.

Use of incentives and pay-for-performance (P4P) to realign payment to address problems of low quality of care or gaps in preventive services has had limited success in improving the quality of healthcare.

For the most part, studies on P4P have focused on large group practices.

 Small practices, where the majority of patients still receive care nationally,

historically face greater obstacles to improving care because they have lacked the scale and organizational structure to conduct quality improvement activities or participate in P4P.

It is important to assess clinician attitudes toward key program features, such as the selection of target quality measures, trust in performance reports, and relevance of quality targets. Understanding clinician motivations and opinions toward a quality improvement program may help predict the extent to which they change their clinical behavior.

 Specific program features, such as the frequency and type of performance feedback and available assistance for meeting program goals, could potentially affect clinician awareness and understanding of particular programs. Clinician skepticism about the accuracy of reports, or distrust of or lack of transparency in data used for reporting or payment, may lead to less engagement of clinicians in incentive programs or quality improvement efforts.

With widespread implementation of electronic health records (EHRs),

 EHR-enabled solo and small group practices have been  shown to be capable of responding to quality improvement (QI) initiatives, as well as programs that incentivize using quality measurement.

 It is unknown how clinicians will feel about quality measurement and pay-forperformance using EHR-derived quality measures. To address this gap in the literature, we surveyed clinicians participating in Health eHearts, a cluster-randomized trial of the effect of a financial incentive and QI assistance program on measures of cardiovascular care compared with the effect of providing quality reports and QI assistance. The Primary Care Information Project (PCIP), a bureau of the New York City Department of Health and Mental Hygiene, piloted Health eHearts in practices that recently adopted an EHR and that were receiving ongoing QI visits to improve practice work flows using health information technology. Survey domains included overall experience with the program, as well as experience with the tools supporting QI efforts. In addition, we assessed whether there were differences in experiences or attitudes and whether these attitudes differed for practices receiving incentives or not.

PCIP recruited 140 small practices to participate in Health eHearts. The program duration was April 2009 to September 2011. Practices were eligible if they have been “live” on the EHR for at least 3 months, had a minimum of 200 patients with cardiovascular diagnoses related to the quality measurement targets, and were transmitting quality measures through the EHR to PCIP. Practices agreed to be randomized into “recognition” or “rewards” groups. Rewards consisted of financial incentives for each numerator met for 4 areas of cardiovascular care: aspirin therapy, blood pressure control, cholesterol control, and smoking cessation intervention (ABCS). Incentive amounts ranged from $20 to $150 per patient with goal achieved, with higher payments for harder to treat patients (eg, comorbid diseases or lower socioeconomic status). The recognition group served as a control. Both groups (control and incentive) received quarterly quality performance reports, telephone and onsite coaching on work flow redesign, and training on documentation, and were invited to a recognition program at the end of the year. The quality reports summarized practices’ progress on the ABCS and compared their performance with other practices in Health eHearts and trends over the previous 6 months.

Health eHearts was a 2-year program, with cohort 1 enrolled at the beginning and continuing for 2 years and cohort 2 enrolled at the beginning of year 2. Practices were surveyed before and after each program year. This study focuses on the survey administered to all participating practices at the end of Health eHearts. A 33-item survey (29 items in the control group version) was administered in October 2011. A lead clinician from each practice was invited to respond to the survey first by mail, followed by at least 3 reminder phone calls to nonresponding clinicians. Survey administration continued through February 2012.

The instrument was developed in collaboration between PCIP and researchers from University of California San Francisco (UCSF) who were contracted as evaluators for the overall evaluation of the program. The instrument focused on several aspects of the Health eHearts program: clinicians’ experiences and attitudes toward the selected quality measures (ABCS), training on use of the EHR or achievement  of ABCS, QI visits, tracking patients for preventive services using the EHR, quality reports, incentive payments (incentive group only), recognition programs in general, and demographics. The survey was pretested with program staff and a clinician in PCIP. Items used in this survey were based on an earlier instrument co-developed with UCSF to assess barriers and facilitators for small practices to participate in P4P. Topics identified as barriers included: accuracy and regularity of reports relevant to the practice’s patient population, measurement targets that were meaningful to the practice population, availability of training or assistance to conduct QI activities, and use of practice tools, such as the EHR, to identify patients and document for quality measurement reports.

The survey was considered part of program evaluation activities conducted by PCIP and was deemed exempt by the Institutional Review Board at New York City Department of Health and Mental Hygiene. Clinicians in the control group were offered a $100 honorarium for participating in the survey. 

Frequencies and averages were calculated for practice characteristics stratified by whether the practice was in the incentive or control group. All items in the survey were recorded into dichotomous variables and then stratified by incentive and control groups. Significant statistical differences between the incentive and control group were determined using χ

 tests. Data were analyzed using SAS software, version 9.2 (SAS Institute, Cary, North Carolina).

Items were recoded in the following manner: Answer choices of “all of the time with all of my patients,” “all of the time with a portion of my patients,” or “some of the time with a portion of my patients” were considered use of the functionalities and a “never” response was considered nonuse of the functionalities. Clinician responses on questions about their experience or use of the quality reports were recoded as agreement with the statement (“agree/strongly agree”) or disagreement (response of “neutral,” “disagree/strongly disagree”). QI visits and training was recoded as helpful (“helpful/very helpful”) or not helpful (“not at all helpful/slightly helpful”). Responses to items regarding clinician attitude toward future intentions to perform quality improvement activities were grouped into a positive response if they selected “likely” or “very likely” and a negative response if they selected “not likely.” Responses of “don’t know,” “not applicable,” and missing values were excluded.

Of the eligible 140 clinicians (70 per group), 104 completed the survey (response rate of 74%, 54 incentive and 50 control clinicians, 

 = .18). The majority of respondents specialized in family or internal medicine (98.1%) and the average respondent had been in practice over 18 years (

). Mean length of time “live” on the EHR was 37 months, with an average of 7000 encounters per year. No statistically significant differences were observed between the incentive group and the control group for either clinician or practice-level characteristics. No statistically significant differences were observed between survey respondents and nonrespondents except for the proportion of the patient who were self-pay (3.9% for respondents and 7.0% for nonrespondents; data not shown).

Overall, clinicians reported positive experiences. Respondents reported receiving and reviewing the quality reports (89%), agreed with the prioritization of ABCS (89%), thought the ABCS were clinically meaningful for their population (87%), and understood the information given in the quality reports (95%) (

). Clinicians in the program were using the EHR tools at least some of the time (Figure).

Nearly all clinicians (95%) responded that they understood the information summarized in the reports (Figure). A majority (69%) agreed that the data in the reports accurately reflected the practice’s performance and enough information was provided to track progress toward meeting targets (77%). There were few differences between the groups, although clinicians receiving incentives were more likely to report that they received and reviewed the reports compared with control clinicians (

There were significant differences between incentive and control group in their program participation (

). Over half of the respondents had a QI visit (56%); however, more clinicians in the incentive group reported having visits compared with the control group (68% vs 43%, 

= .01). Both groups reported that the visit was helpful (85% vs 80%, 

= .57), and the incentive group was more likely to report that the PCIP staff was accessible (69% vs 43%, 

 = .02). More clinicians in the incentive group had positive responses to the training using webinars (group online workshops) and web exes (virtual “visit” using the Internet; PCIP staff can  access the participant computer terminal and “talk through” use of the EHR) compared with clinicians in the control group. Overall, respondents expressed interest in more QI visits (81%).

Tracking Patients for Preventive Services Using EHR Tools

All respondents reported some use of the EHR functionalities (Figure 1). Clinical Decision Support System (CDSS) alerts (automated alerts and reminders for preventive services) and smart forms (automated question flows that assist clinicians in taking patient histories) were the most used. Although not statistically significant, incentive clinicians were more likely to report using EHR tools with the exception of the use of order sets to identify patients in need of preventive services (83% incentive vs 59% for control, 

Intention to Continue Activities After Health eHearts

Most respondents (80%) indicated the intent to generate quality reports after the program ended and allocate staff time to focus on QI activities (70%) (Table 2). Incentive clinicians were more likely to report that that they would generate quality reports (87% incentive vs 72% control, 

= .07), track practices’ progress toward meeting quality measurement goals (91% vs 78%, 

= .09), and hold regular meetings or check-ins (71% vs 57%, 

 = .14) compared with control clinicians.

Small practice clinicians had positive experiences with the rewards and financial recognition program designed to improve the delivery of clinical preventive services. Clinicians in the incentive group were more likely than those in the control group to report participating in quality improvement activities offered by the program, such as reviewing the quality reports, using order sets, and participating in program training sessions. The high level of buy-in to the program is demonstrated by the reported usability and accuracy of the quality reports and by reported agreement with the ABCS prioritization of preventive cardiovascular care.

Past studies document instances of clinician skepticism about the validity of clinical quality measurements or accuracy of reports, leading to less engagement of clinicians in quality improvement efforts.

 In addition, because of the lack of transparency in data used for reporting or payment, some P4P programs have been seen as a threat to clinicians’ autonomy and sense of control.

 The Health eHearts program addressed issues seen in earlier studies by generating reports directly from the practices’ EHRs, offering transparency into the data used for quality measurement, and also by providing QI assistance and help with troubleshooting problem areas with the intent of improving clinician sense of control over measured performance.

Alignment of the program goals with the practice’s organizational structure and culture has been associated with successful P4P implementation.

The majority of clinicians agreed with the prioritization of the ABCS and found them to be meaningful to their practice. Positive clinician attitude has been associated with successful implementation of EHRs

 and is potentially an important contributor to continued EHR use, especially in small independently owned practices that do not have dedicated staff for quality measurement or EHR-based reporting. 

Robust EHRs can systematize and streamline work flow by allowing clinicians to use key features, such as CDSS.

 However, small practices are less likely to utilize these features.

These survey results suggest that providing QI assistance along with incentives can be effective in engaging clinicians both during a program and potentially for sustaining continued QI activities.

Our study has several limitations. As a self-reported survey, it is subject to social desirability bias whereby clinicians may be inclined to respond positively instead of with criticism. In this study, the differences between the incentive group and the control group answers were likely equally affected by this bias, implying that the differences observed in reported engagement with quality improvement activities would not be affected by this limitation, though the overall experience ratings may be higher than if respondents were not affected by this bias.

It is also possible that the overall ratings of the experience in the program are more positive than the experience for all participants in the program, since some participants did not respond. However, we received a high response rate of 74% and there were few  significant differences in practice characteristics between respondents and nonrespondents.

Articles

A study to assess clinician attitudes and experiences after participating in a New York City cardiovascular disease focused quality recognition and financial incentive program using health information technology.

Small Practices' Experience With EHR, Quality Measurement, and Incentives

To examine whether physicians' use of information technology (IT) was associated with better knowledge of drug costs.

A 2007 statewide survey of 247 primary care physicians in Hawaii regarding IT use and self-reported knowledge of formularies, copayments, and retail prices.

Approximately 8 in 10 physicians regularly used IT in clinical care: 60% Internet, 54% e-prescribing, 43% electronic health records (EHRs), and 37% personal digital assistants (PDAs). However, fewer than 1 in 5 often knew drug costs when prescribing, and more than 90% said lack of knowledge of formularies and copayments remained a barrier to considering drug costs for patients. In multivariate analyses adjusting for sex, practice size, years in practice, number of formularies, and use of clinical resources (eg, pharmacists), use of the Internet—but not e-prescribing, EHRs, or PDAs&mdash;was associated with physicians reporting slightly better knowledge of copayments (adjusted predicted percentage of 23% vs 11%; P = .04). No type of IT was associated with better knowledge of formularies or retail prices.

 Despite high rates of IT use, there was only a modest association between physicians’ use of IT and better knowledge of drug costs. Future investments in health IT should consider how IT design can be improved to make it easier for physicians to access cost information at the point of care.

In this statewide survey, high rates of health information technology use by physicians were only modestly associated with better knowledge of drug costs.

Approximately 8 in 10 physicians regularly used health information technology in clinical care; however, only 1 in 4 often knew drug costs.

In multivariate analyses, Internet use (but not e-prescribing, electronic health records, or personal digital assistants) was associated with slightly better knowledge of copayments (adjusted predicted percentage of 23% vs 11%; P = .04).

Future investments in health information technology should consider how to improve its design so that physicians can easily access cost information at the point of care.

The United States is preparing to invest billions of dollars in a major reform of its healthcare system.

 A critical step is increasing the use of health information technology (IT).

 Although prior studies have focused on the ability of IT to make clinical information (eg, medical history, labs) available at the point of care,

 IT systems also can be designed to facilitate physicians’ and patients’ access to accurate and timely cost information.

 This access is especially important as policymakers, insurers, physicians, and patients alike grow increasingly concerned with containing healthcare costs.

In this study, we examined the association between physicians’ use of IT and their knowledge of drug costs. Nationally, 26% of patients report nonadherence to medications because of cost.

 Although physicians want to help, they often are hampered by their lack of awareness of drug costs.

 Health IT can improve physicians’ access to cost information. A 2003 survey found that computerized prescription order entry was associated with physicians having greater knowledge of drug costs,

 but since then information regarding formularies, copayments, and retail prices has increasingly become available via other forms of IT such as the Internet and personal digital assistants (PDAs)10 (see www.epocrates.com, www.uptodate.com, www.medicalletter.com, www.medicare.gov, www.drugstore.com, www.costco.com, and www.walmart.com). Understanding whether different types of IT are currently associated with better knowledge of drug costs can highlight if physicians are already successfully obtaining cost information using health IT, or if further improvements in IT design are needed.

The study was a 2007 cross-sectional survey of community physicians in Hawaii who provided adult primary care, regularly saw patients, and were likely to face more than 1 formulary in their clinical practice. We initially identified 819 general internists, family physicians, and general practitioners through the Hawaii Medical Association (which records all licensed Hawaii physicians) and local health plans’ lists of participating physicians. We called physicians’ offices to exclude specialists, Kaiser and military physicians (ie, those employed by staff model institutions with a single formulary), hospitalists, academic teaching faculty, residents in training, retired physicians, and physicians without accurate contact information based on phone directory and Internet searches. A total of 460 physicians remained, similar to the number identified in a separate unpublished study using claims from Hawaii’s largest insurer, Hawaii Medical Services Association, which captures approximately 60% of Hawaii’s residents and contracts with the vast majority of local physicians. (These independent claims analyses identified 415 adult primary care physicians who were the main providers for >20 Hawaii Medical Services Association members with diabetes.) We mailed these physicians a 10-minute, 27-item survey with no financial incentives, followed by 3 mailings to nonrespondents. Of 281 participants (61% response rate), 34 were ineligible (eg, saw <20 patients per week); therefore, final analyses were based on 247 participants. The institutional review board at the University of Hawaii approved this study.

Physicians were asked, “Do you regularly use the following IT in clinical care (yes/no)?” for the Internet, electronic health records (EHRs), e-prescribing, and PDAs.

 This question referred to both software (Internet, EHRs, e-prescribing) and hardware (PDAs) forms of IT often used by physicians in clinical settings. Because potential overlaps can exist between different IT types, we allowed physicians to answer yes to all functionalities that applied. For example, physicians who used EHRs with e-prescribing functionality could respond yes to use of both EHR and e-prescribing. We did not ask physicians about the specific functionalities of the IT types that they reported using.

To assess physicians’ use of clinical resources when prescribing, we asked, “Do you regularly use any of these resources to help you choose a medication for your patient (yes/no): peers/consultants, health plans, pharmaceutical representatives, pharmacists, and medical journals?” 

 With respect to knowledge of drug costs, physicians reported how often they knew copayments, formularies, or retail prices, and how often they checked formularies when unsure of coverage or discussed drug costs with patients (almost always, most of the time, some of the time, rarely/never). Physicians also were asked, “How important is it for you to consider the cost of a medication to your patient?” and “How many of your patients do you think have difficulty paying for their medications?” Lastly, physicians reported demographic (age, sex, specialty) and practice (eg, number of formularies, solo vs group practice) characteristics.

 The survey was tested for ease of understanding and question comprehension in a convenience sample of 5 physicians.

To examine the association between different types of IT use and drug cost knowledge, we used multivariate logistic regression to predict 3 main outcomes: (1) usually or always knew copayments, (2) usually or always knew retail prices, and (3) had no difficulty in determining formulary coverage. We used the 4 separate IT types as our main predictors: Internet, EHR, e-prescribing, and PDA. In sensitivity analyses, we created a composite IT score by assigning 1 point for each type of IT used and summing these points. The composite score (0 to 4) was not statistically significant in predicting any of the 3 outcomes, and detailed results are not presented.

We adjusted our model for sex, solo versus group practice, years in practice, number of formularies, and use of clinical resources. We excluded age and practice setting because they were highly correlated (r >0.40) with years in practice and solo versus group practice. We excluded provider type because it did not approach significance in bivariate analyses. Results are presented as adjusted predicted percentages with 95% confidence intervals for greater ease of understanding.

Most of the 247 respondents were general internists (55%) and family physicians (36%) in solo (43%) or small practices (48%), and 70% faced 6 or more formularies (

). Nearly all reported that their patients had difficulty paying for medications (99%) and that considering drug costs for patients was important (98%). The majority of respondents (65%) said they discussed medication costs with patients most of the time (49%) or always (16%).  The remainder said they discussed these costs sometimes (31%) or rarely (4%).

Approximately 8 in 10 physicians used at least 1 type of IT regularly in clinical care (Table 1): Internet (60%), e-prescribing (54%), EHRs (43%), and PDAs (37%). Nearly two-thirds (62%) regularly used 2 or more IT types. With respect to using clinical resources when choosing medications, physicians reported using information from pharmacists (69%), followed by peers (56%), pharmaceutical representatives (48%), medical journals (46%), and health plans (45%).

Fewer than 1 in 5 physicians often knew copayments or retail prices when prescribing, and 68% reported difficulty knowing formulary coverage (

). Only 1 in 4 physicians regularly checked formularies when unsure whether a drug was covered. Nearly all said that difficulty knowing formulary coverage (94%) and copayments (91%) were significant barriers to considering drug costs for patients.

In analyses adjusting for sex, solo versus group practice, years out from training, number of formularies, and use of clinical resources, physicians who regularly used the Internet in clinical care were slightly more likely to report that they usually knew copayments (adjusted predicted percentage of 23% vs 11%; P = .04) (

). Neither EHR, e-prescribing, nor PDA use was significantly associated with physicians reporting better knowledge of copayments. For formulary coverage and retail prices, none of the 4 types of IT were significantly associated with better knowledge.

Physicians who faced 6 or more formularies in clinical care were less likely to know copayments (15% vs 26%; P = .048) and formulary coverage (26% vs 46%; P = .006). However, physicians who used information from health plans reported better knowledge of copayments (25% vs 13%; P = .01) and formulary coverage (40% vs 26%; P =.03). With respect to retail prices, the use of pharmacists approached but did not reach statistical significance in predicting physicians having better knowledge of prices (24% vs 12%; P = .07).

In this statewide survey of primary care physicians, we were surprised that although 8 in 10 participants said they regularly used at least 1 type of health IT in clinical care, we detected only a modest association between IT use and better knowledge of drug costs. Use of the Internet was associated with slightly better knowledge of copayments (23% vs 11%; P = .04). However, even among those who regularly used the Internet, the majority of physicians still reported poor knowledge of drug costs.

We are aware of only 1 prior study that assessed multiple types of IT use and physicians’ knowledge of drug costs. In this 2003 study, Shrank et al surveyed physicians (49.6% response rate) identified from the California Medical Association, whose members represented one-third of California physicians.

 In contrast with the results from our more recent survey, the authors found that “computer order entry” (not further defined) but not Internet use was associated with greater awareness of drug costs.

 Neither that study nor ours represent national samples, but our findings should alert policymakers, insurers, and IT providers to reevaluate, as additional money is spent on health IT,

 the ability of current and future IT systems to successfully provide cost information to physicians at the point of care.

Our findings should not discount the potential future usefulness of certain types of IT in increasing physicians’ access to drug cost information.

 Although our participants who reported regularly using e-prescribing did not report better knowledge of drug costs, the integration of formulary information support into prescribing software has been shown to decrease both out-of-pocket and total drug costs.

 Fischer et al analyzed claims for 2 Massachusetts insurers 18 months after the implementation of an e-prescribing system with formulary decision support, and found that physicians were more likely to prescribe lower-cost drugs.

 McMullin et al found that use of an e-prescribing system with integrated clinical and cost information decreased the likelihood of physicians prescribing higher-cost drugs from 35.8% (control group) to 17.5% (intervention group).

At the time of our study, an informal review indicated that several health plans in Hawaii made copayment and formulary information available via the Internet, but not necessarily via EHR, e-prescribing, or PDA. A highly promoted type of e-prescribing software in Hawaii contained formulary information only for the single health plan that sponsored its adoption.

 Thus, we speculate that this may have been why use of the Internet, but not other IT types, was associated with slightly better knowledge of copayments.

This study indicates that improving physicians’ knowledge of drug costs will require more than simply increasing physicians’ use of health IT. Given the financial burden of drug costs on patients, there is a critical need to determine if the problem is that drug cost information is not adequately available via various types of IT, physicians are unaware of its availability, or physicians find such cost information too difficult to use. We did not ask physicians who reported using health IT why they still experienced difficulty accessing drug cost information. However, other studies indicate the issue may be the variability among formularies and out-of-pocket costs from patient to patient, as well as the need for further availability of drug cost information via IT.

 Nearly all of our participants recognized the financial burden of drug costs on patients and the importance of considering out-of-pocket drug costs. However, unless health IT is designed to make the costs of drugs (and other medical services) automatically available at the point of care, physicians and patients will likely continue to be hampered in obtaining healthcare that is appropriate from both a cost and a quality perspective.

Among our participants, no single type of IT was used by more than 60% of physicians. National estimates of physicians’ use of different types of IT for clinical care are generally lower than 50%: 41% use the Internet for computerized decision support,

 Thus, cost information will need to be accessible via multiple IT types to achieve a wider impact in improving physicians’ knowledge of drug costs.

There are several important limitations to this study. We sampled physicians from a single state, and the impact of IT use on physicians’ knowledge of drug costs will vary depending on how well cost information is integrated into local IT systems. Our findings, however, show that in at least 1 state, even physicians with very high levels of IT use still experience substantial problems accessing drug costs, and this issue warrants policy intervention. Our results are based on self-report, and physicians could have overreported or unnderreported their actual use of IT. Physicians reported only whether they regularly used IT in general clinical care; we did not ask specifically about their use of IT to retrieve drug cost information. Further studies are needed to determine whether physicians use IT specifically to access drug cost information and if so, how. If they do not use IT to access drug cost information, the reasons why must be investigated. Physicians also self-reported their knowledge of drug costs, and actual knowledge is likely to be even poorer.

 We focused on busy, community-based primary care physicians, and our results cannot be generalized to specialists or physicians who see fewer patients.

Despite high rates of health IT use in clinical care, there was only a modest association between IT use and physicians reporting better knowledge of drug costs. Policymakers and insurers should examine the integration of cost information into future health IT systems and its usability by community physicians at the point of care.

We would like to thank Ms Allison Imamura for her invaluable help with the project.

From the Department of Family Medicine and Community Health (CWT, ALH), University of Hawaii, Honolulu, HI; Pacific Health Research Institute (CWT, RC), Honolulu, HI; RAND Corporation (RHB, EBK, CMM), Santa Monica, CA; Departments of Medicine and Health Services (RHB, CMM), UCLA, Los Angeles, CA; Department of Medicine (GCA), University of Chicago, Chicago, IL; Department of Pharmacy Practice (GCA), University of Illinois at Chicago, Chicago, IL; Department of Family Medicine (EAJ), University of Connecticut, Hartford, CT; and Department of Medicine (RAD), University of California, San Francisco, CA.

Dr Tseng’s work on this study was funded by the Robert Wood Johnson Foundation Generalist Physician Faculty Scholars Program. Dr Dudley’s work was funded by an Investigator Award in Health Policy from the Robert Wood Johnson Foundation. Dr Mangione received support from the Resource Centers for Minority Aging Research/Center for Health Improvement of Minority Elderly (RCMAR/CHIME) funded by the National Institutes of Health/National Institute on Aging (P30 AG021684). The sponsors had no role in study conduct, data analyses, or manuscript preparation.

The authors (CWT, RHB, GCA, ALH, EBK, CMM, RC, EAJ, RAD) 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 (CWT, ALH, RAD); acquisition of data (CWT); analysis and interpretation of data (CWT, RHB, GCA, ALH, EBK, RC, RAD); drafting of the manuscript (CWT, RHB, RC, RAD); critical revision of the manuscript for important intellectual content (CWT, RHB, GCA, ALH, EBK, CMM, RC, EAJ, RAD); statistical analysis (CWT, EBK, RC); obtaining funding (CWT, CMM); administrative, technical, or logistic support (CWT, EAJ); and supervision (RHB, CMM).

High rates of health information technology use by physicians were only modestly associated with better knowledge of drug costs.


R. Adams Dudley, MD, MBA

Articles

Small Practices' Experience With EHR, Quality Measurement, and Incentives

Health Information Technology and Physicians' Knowledge of Drug Costs