Currently Viewing:
The American Journal of Managed Care August 2012
Racial Disparities in African Americans With Diabetes: Process and Outcome Mismatch
John B. Bulger, DO; Jay H. Shubrook, DO; and Richard Snow, DO, MPH
Cardiac Risk Is Not Associated With Hypertension Treatment Intensification
Jeremy B. Sussman, MD, MS; Donna M. Zulman, MD, MS; Rodney Hayward, MD; Timothy P. Hofer, MD, MS; and Eve A. Kerr, MD, MPH
A Technology Solution for the High-Tech Diagnostic Imaging Conundrum
Leif I. Solberg, MD; Cally Vinz, MBA, RN; and Jim E. Trevis, BA
Primary Nonadherence to Medications in an Integrated Healthcare Setting
Janet Shin, PharmD; Jeffrey S. McCombs, PhD; Robert J. Sanchez, RPh, PhD; Margarita Udall, MPH; Michael C. Deminski, MS, RPh; and T. Craig Cheetham, PharmD, MS
Slowing Medicare Spending Growth: Reaching for Common Ground
Michael E. Chernew, PhD; Richard G. Frank, PhD; and Stephen T. Parente, PhD
Patient Medical Group Continuity and Healthcare Utilization
Louise H. Anderson, PhD; Thomas J. Flottemesch, PhD; Patricia Fontaine, MD, MS; Leif I. Solberg, MD; and Stephen E. Asche, MA
Cost-Sharing and Initiation of Disease-Modifying Therapy for Multiple Sclerosis
John Romley, PhD; Dana Goldman, PhD; Michael Eber, BSE; Homa Dastani, PhD; Edward Kim, MD, MBA; and Swetha Raparla, BPharm, MS
Currently Reading
Which Components of Health Information Technology Will Drive Financial Value?
Lisa M. Kern, MD, MPH; Adam Wilcox, PhD; Jason Shapiro, MD; Rina V. Dhopeshwarkar, MPH; and Rainu Kaushal, MD, MPH
Home Care Program for Patients at High Risk of Hospitalization
Stuart Levine, MD, MHA; Bernard A. Steinman, PhD; Karol Attaway, MHA; Tyler Jung, MD; and Susan Enguidanos, PhD
Questioning the Widely Publicized Savings Reported for North Carolina Medicaid
Al Lewis, JD
Measuring Migraine-Related Quality of Care Across 10 Health Plans
Valerie P. Pracilio, MPH; Stephen Silberstein, MD; Joseph Couto, PharmD, MBA; Jon Bumbaugh, MA; Mary Hopkins, RN; Daisy Ng-Mak, PhD; Cary Sennett, MD, PhD; and Neil I. Goldfarb, BA
Impact of Point-of-Care Case Management on Readmissions and Costs
Andrew Kolbasovsky, PsyD, MBA; Joseph Zeitlin, MD; and William Gillespie, MD
Impact of a Program Encouraging the Use of Generic Antipsychotics
Eric D. A. Hermes, MD; Michael Sernyak, MD; and Robert Rosenheck, MD

Which Components of Health Information Technology Will Drive Financial Value?

Lisa M. Kern, MD, MPH; Adam Wilcox, PhD; Jason Shapiro, MD; Rina V. Dhopeshwarkar, MPH; and Rainu Kaushal, MD, MPH
This framework suggests new meaningful use measures and guidance for prioritizing implementation of different health information technology functionalities, based on their expected financial effects.
Objectives: The financial effects of electronic health records (EHRs) and health information exchange (HIE) are largely unknown, despite unprecedented federal incentives for their use. We sought to understand which components of EHRs and HIE are most likely to drive financial savings in the ambulatory, inpatient, and emergency department settings.

Study Design: Framework development and a national expert panel.

Methods: We searched the literature to identify functionalities enabled by EHRs and HIE across the 3 healthcare settings. We rated each of 233 functionality-setting combinations on their likelihood of having a positive financial effect. We validated the top-scoring functionalities with a panel of 28 national experts, and we compared the high-scoring functionalities with Stage 1 meaningful use criteria.

Results: We identified 54 high-scoring functionality- setting combinations, 27 for EHRs and 27 for HIE. Examples of high-scoring functionalities included providing alerts for expensive medications, providing alerts for redundant lab orders, sending and receiving imaging reports, and enabling structured medication reconciliation. Of the 54 high-scoring functionalities, 25 (46%) are represented in Stage 1 meaningful use. Many of the functionalities not yet represented in meaningful use correspond with functionalities that focus directly on healthcare utilization and costs rather than on healthcare quality per se.

Conclusions: This work can inform the development and selection of future meaningful use measures; inform implementation efforts, as cliniciansand hospitals choose from among a “menu” of measures for meaningful use; and inform evaluation efforts, as investigators seek to measure the actual financial impact of EHRs and HIE.

(Am J Manag Care. 2012;18(8):438-445)
The financial effects of health information technology (HIT) are uncertain, in part because HIT is inherently heterogeneous, with numerous functionalities that are variably implemented, configured, and used.

  • We developed a framework for rating the potential financial effects of different functionalities enabled by HIT.

  •  The results suggest specific measures for Stages 2 and 3 of the federal “meaningful use” program that would focus directly on healthcare utilization and costs.

  •  The results also suggest which Stage 1 meaningful use Menu Set measures are most likely to have a positive financial effect, thereby providing guidance for implementation.
Through the American Recovery and Reinvestment Act of 2009, the federal government is investing up to $27 billion in health information technology (HIT).1 One of the rationales for this investment is the expectation that adoption and meaningful use of HIT will reduce healthcare costs.2 However, a report by the Congressional Budget Office in 2008 highlighted substantial uncertainty about the actual financial effect of HIT, saying that healthcare costs could decrease, stay the same, or increase.3 Costs could decrease if HIT reduces unnecessary utilization and reduces expensive adverse events. Costs could stay the same if HIT changes care but not in ways that introduce efficiencies. Costs could increase if HIT actually slows down providers, decreasing efficiency; leads to a more expensive, computersavvy workforce; or leads to higher utilization of medical services.3

Previous work in this area has largely modeled the financial effects of whole HIT applications, assuming that the effects of those applications were similar across different contexts.4,5 However, this assumption may not be true, because HIT is an inherently heterogeneous intervention. Electronic health records (EHRs) and health information exchange (HIE), 2 dominant forms of HIT, are themselves applications composed of numerous functionalities that are variably implemented, configured, and/or used. This heterogeneity exists despite federal efforts to standardize the functionalities of “certified” EHRs.6

We sought to develop a framework that would describe more precisely the specific functionalities enabled by EHRs and HIE that may be expected to mediate any financial effects. We also sought to rank the relative importance of these functionalities for their expected financial effects, with input from national experts. Developing such a framework would have 3 main applications. First, the rankings could inform the selection of measures for Stages 2 and 3 of the federal EHR Incentive Program to promote “meaningful use.”1 Second, the rankings could inform implementation efforts, as clinicians and hospitals choose among a “menu” of meaningful use measures.1 Third, the rankings could inform evaluation efforts, as investigators seek to measure the actual financial impact of EHRs and HIE.



Our methods consisted of 8 steps: 1) choosing technologies and healthcare settings, 2) identifying functionalities enabled by EHRs and HIE, 3) conducting internal ratings, 4) presenting a preliminary framework to a national expert panel, 5) modifying the ratings, 6) identifying top-scoring functionalities, 7) comparing these with the final Stage 1 meaningful use criteria, and 8) final validation.

Choosing Technologies and Healthcare Settings

We considered 2 types of health information technology: EHRs and HIE. For the purposes of this study, we considered EHRs and HIE to be distinct. We considered EHRs to be non-interoperable, that is, not including data from external sources. We considered HIE to be the electronic delivery of data from external sources, whether that delivery is through a freestanding portal or delivery into an EHR. We considered 3 healthcare settings: ambulatory, inpatient, and emergency department (ED) care. We then developed 6 technology-setting combinations (2 technologies  3 settings).

Identifying Functionalities Enabled by EHRs and HIE

We conducted a literature search to identify functionalities contained in EHRs and HIE applications. An example of such a functionality is the availability of alerts for drugdrug interactions in the context of electronic prescribing. We specifically sought functionalities that would be used by clinicians for the medical decision making that would drive healthcare costs. We included lists of functionalities generated by the Commission for the Certification of Health Information Technology (CCHIT)6 and the Institute of Medicine.7 We supplemented the literature review with functionalities encountered in the authors’ clinical, informatics, and research experiences. We populated each technology-setting combination with all relevant functionalities (eAppendix available at

Conducting Internal Ratings

We developed a set of 3 domains upon which the functionalities would be rated: 1) probability of achieving a benefit, or the probability that the functionality will result in the desired effect in the real world for a given patient; 2) time to achieve a benefit, or the time from the “go live” date to the occurrence of the desired effect; and 3) probability of measuring a benefit, or the probability of being able to capture through research a statistically significant effect size, given available data and resources. Each domain was matched with a 3-point Likert scale, where the most desirable value had a value of 3 points. Four of the authors developed an initial set of ratings for each functionality in each technology-setting combination. The scores reflected what the raters estimated could be implemented in the next few years, rather than an assessment of what is currently implemented.

For each functionality, we created a simple sum across domains. We then selected the top 10 functionalities in each technology-setting combination, allowing more than 10 if there were ties.

Presenting a Preliminary Framework to a National Expert Panel

We convened a panel of 28 national experts from the fields of health information technology, health information exchange, health services research, healthcare economics, and healthcare policy (see Acknowledgments section). We held an in-person meeting in New York City in April 2007. The panel approved the methodology that had been used to date, added a small number of additional functionalities, and suggested the 3 additional domains: 4) complexity of implementation, or how difficult it is to “turn on the switch;” 5) likelihood of usage, or the probability that providers will actually use the functionality; and 6) expected magnitude of the financial impact, or the expected magnitude of cost savings from the payer perspective. The payer perspective was chosen, because this most closely aligns with healthcare expenditures, like those that the federal EHR Incentive Program is designed in part to address.

Modifying the Ratings

We added internal ratings for the additional functionalities and additional domains. We also had 7 national experts review the ratings in detail and suggest possible edits. Suggestions were reviewed and reconciled through consensus by 3 authors.

Identifying Top-Scoring Functionalities

For each functionality, we summed scores across the 6 domains, for a possible score of 6 to 18 points. We determined the distribution of scores for each technology-setting combination. None of the distributions were clearly bimodal with obvious cut-points for the highest scoring functionalities. Thus, we selected and applied the cutoff that would yield approximately 10 high-scoring functionalities for each technology-setting combination. We also used 2-tailed t tests to compare the average score for EHR functionalities with the average score for HIE functionalities. We used analysis of variance (ANOVA) to compare the average scores across healthcare settings.

Comparing With the Final Stage 1 Meaningful Use Criteria

We compared the top-scoring functionalities with the final Stage 1 meaningful use criteria. Stage 1 includes 15 “Core” measures that are required for all eligible providers and hospitals, such as “record patient demographics,” “record vital signs and chart changes,” and “use computerized order entry for medication orders.”1 In addition, Stage 1 includes 12 “Menu” measures, from which eligible providers and hospitals are expected to choose 5.1 Examples of Menu measures include “implement drug formulary checks” and “perform medication reconciliation between healthcare settings.” We calculated the percentage of top-scoring functionalities from our framework that are part of meaningful use and analyzed the content of those that are not yet part of meaningful use, in order to identify opportunities for future meaningful use measures.

Final Validation

We presented the top-scoring functionalities to experts again in August 2011. They validated the final set and recommended no changes, as they believed that it was consistent with and went beyond Stage 1 meaningful use.


We identified 105 unique functionalities enabled by EHRs and HIE and 233 functionality-setting combinations (Appendix). We identified a total of 84 functionalities for ambulatory care, 80 for inpatient care, and 69 for ED care. We identified a total of 160 functionality-setting combinations for EHRs and 73 for HIE. Overall and within each setting, there were more functionalities for EHRs than HIE.

Overall, the average summary score for each functionality in each setting was 12.5 (median 13, standard deviation [SD] 2.6) on a scale from 6 to 18, in which higher scores represented a higher likelihood of having a measurable positive financial effect. The average functionality for EHRs scored significantly higher than the average functionality for HIE (13.0 vs 11.3, P <.0001). There were no differences in average scores across healthcare settings (P = .33).

The distribution of scores is shown in Figure 1 for EHRs and Figure 2 for HIE. The cut-point that yielded approximately 10 high-scoring functionalities per technology-setting combination was a score of >16 for EHRs and >13 for HIE. Using this threshold, there were a total of 31 unique high-scoring functionalities and a total of 54 high-scoring functionality-setting combinations (Table). For EHRs, the high-scoring functionalities had scores ranging from 16 to 18, with a mean of 16.5 (SD 0.7). For HIE, the high-scoring functionalities had scores ranging from 13 to 16, with a mean of 13.9 (SD 1.0).

For EHRs in particular, there were 15 unique high-scoring functionalities and 27 high-scoring functionality-setting combinations (Table). Examples of high-scoring EHR functionalities included: providing alerts for expensive medications (ambulatory and inpatient care), providing alerts for redundant lab orders (inpatient and ED care), and displaying imaging results (ED). For HIE, there were 16 unique high-scoring functionalities and 27 high-scoring functionality-setting combinations (Table). Examples of high-scoring HIE functionalities included: sending and receiving imaging reports (ambulatory, inpatient, and emergency care), receiving laboratory results (ambulatory and emergency care), and enabling structured medication reconciliation.

Copyright AJMC 2006-2019 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up