Studies have emphasized the adoption of EHRs, 6,18–20
and a few studies have examined the relationship between EHR adoption and quality of care. 7,8
However, these studies have mostly been cross-sectional, providing snapshots of frequency and characteristics of the correlation at a particular point in time. In contrast, this study provides a longitudinal view of the relationship between EHRs and health care quality, taking into account the changes in correlates over time.
suggests in the “Hype Cycle” that the impact of new technologies can be characterized by a peak in benefits from the new technology due to hype or excitement followed by a period of decreased benefits and disappointment and an eventual plateau in outcomes. Such a pattern was not shown for EHRs' impact on quality of care using available datasets in our current study. There is no apparent or consistent pattern for physicians' performance over the time of using an EHR. We found no evidence that quality of care improved with increasing duration of EHR usage. Our findings are consistent with a previous national study by Linder, et al 7
in that, as implemented, EHR use was not associated with better quality ambulatory care. Taken together, these results suggest that simply implementing EHRs is unlikely to result in improved quality. Other strategies, such as paying more for higher quality care and ensuring that physicians are using EHRs to their full capacity through education and workflow transformation, may be essential.
Several studies have demonstrated that decision support delivered through electronic records can improve quality, 21–29
especially for specific domains such as preventive care and care of some chronic conditions like diabetes. 9,10,30
However, the level of improvement is often modest, and large gaps often persist. Furthermore, some studies have failed to show an effect of computerized clinical decision support on quality of care, especially for complex conditions. 31–34
Our study showed that the usage of decision support among EHR users was quite low, only 23.5% in 2005, compared to its availability, which was 65.0% among EHR adopters. To improve quality of care, it is becoming increasingly clear that implementing an electronic record is not sufficient and this tool needs to be coupled with other system supports such as registry functions, and care delivery transformations such as team-oriented approaches. In the UK, with the combination of near-universal EHR implementation, decision support, and substantial pay-for-performance, very high levels of quality performance have been achieved, although it is debatable which of these factors has been most important. 35–37
In the United States, a great deal of emphasis has focused on the adoption of EHRs, with the implicit assumption that improved quality will result. According to our estimates, the EHR adoption rate in Massachusetts has increased relatively rapidly in the last two decades. It was less than 1% before 1990s, around 12% in 2000, and then doubled in the succeeding five years, from 16% in 2001 to 32% in 2005. To achieve the national goal of universal EHR adoption by 2014, 38
the 2005 EHR adoption rate will need to triple in less than a decade, which represents a challenge for health care policy.
For the use of key EHR functions, our study shows that the usage of health information and results management had grown at the same pace as their availability. In contrast, the usage of decision support and communication were relatively low, as compared with the fast growth of their availability. Among physicians who had EHRs in their main practice, only about one-third used decision support and around half used communication features most of the time. Taken together, these data suggest that stronger incentives or more extensive programs to support physician office transformation may be needed.
Beyond decision support, other factors may be important with respect to improving quality. In particular, the presence of some specific features and functionalities in an EHR may have an important impact on quality. For example, the long-term users might have relatively mature EHRs and well-designed clinical decision support and order entry management, and they might be more willing to use these features. In contrast, short-term EHR users might only have rudimentary EHRs with limited functionalities. They may not use these functions even if they are available within their EHR. Our survey did not record the length of time using specific EHR features, which prevented us from being able to determine the associations between the duration of using a specific EHR feature and quality of care. This analysis should be considered in future studies.
In addition, the sample sizes for long-time users were relatively small. For example, the EHR adoption rate was low in 1995, around 2.8% according to our study; therefore, in 2004 we only had 9 physicians who used an EHR greater than or equal to 10 years. Although we applied 4 years of repeated HEDIS® measures, we were unable to interpret and report the trend for quality measures, which had wide variations, thereby limiting our ability to detect differences which might become apparent with a larger sample.
Several additional limitations of this study should be considered. First, as in any observational study, unmeasured confounding factors may have obscured true associations; our models included all available covariates to control for confounding to the greatest extent possible. Secondly, we used HEDIS® rates to measure physicians' quality performance. Most HEDIS® measures examine the provision of preventive care and care for specific diseases. Although HEDIS® measures have been widely used by researchers and other healthcare related entities, they are derived from claims data. Actual clinical data may provide a more accurate representation of the quality of physician care. Moreover, HEDIS® indicators may not be adequately sensitive to detect improvements in quality of care attributable to the adoption and use of EHR. We also note that only physicians with adequate numbers of patients for each HEDIS® category were included in this study; excluding physicians with smaller-volume practices may have introduced some potential bias. It is conceivable that for these physicians, EHRs could be particularly useful in improving their adherence to quality measures through certain EHR features, such as reminders; this is a hypothesis that should be tested. Controlling for practice size, clinical volume, and whether the practice was hospital-based may have ameliorated some of this potential bias.
A third potential limitation is that EHR adoption and usage were self-reported by physicians, and social desirability bias may have led physicians to overestimate actual usage. Two lines of reasoning support the validity of self-reported data in this context. A nationally representative survey 6
using similar self-reported measures found similar levels of EHR usage as in our statewide survey. 39
Furthermore, our follow-up statewide survey 40
demonstrated stability of usage rates of some EHR functions and considerably increased usage rates of others, suggesting that self-reported measures may adequately detect changes of usage over time. Nonetheless, future studies should consider actual measurement of EHR usage. Finally, this survey was conducted in a single state, so the generalization of our findings to the rest of the United States may be limited.