	Less than 3 years into the passage of the Health Information Technology for Economic and Clinical Health (HITECH) provisions of the American Recovery and Reinvestment Act of 2009, measurable results are emerging. For example, in the first 11 months during which healthcare providers (“eligible professionals”) and acute care hospitals (“eligible hospitals”) had the opportunity to demonstrate stage 1 “Meaningful Use” of Healthcare Information Technology (HIT), more than 20,000 “eligible professionals” and 750 “eligible hospitals” have done so. In the current issue of The American Journal of Managed Care, we showcase examples of HITECH’s potential impact, as well as illustrate the opportunities and challenges ahead.

	Two studies in this issue illustrate how HIT can improve the capacity of our healthcare system to manage chronic illnesses. The study by Vollmer et al describes how an interactive voice recognition system can improve adherence to inhaled corticosteroids among individuals with asthma in a large health maintenance organization. Shelley’s study shows that the combination of electronic medical records, clinical decision support, and performance feedback can improve the rate of blood pressure control in patients with hypertension who receive care in community health centers. Together, these studies provide hope that the nation’s investment in HIT could one day yield clinical dividends.

	Three other studies in this issue suggest that success for HIT will require attention to both technological and sociological factors. The study by Millery et al attributes the success of an HIT-based intervention to a multi-faceted approach that involves a combination of decision support tools, systematic provider feedback, implementation support, and leadership. Results from Abramson’s study suggest that the full error-reduction potential of e-prescribing may only be reached with the combination of on-line clinical decision support and support for clinicians. The study by Dimitropoulos et al further illustrates that a broad spectrum of healthcare consumers needs to be engaged to allay their privacy and security concerns about Health Information Exchange.

	To be sure, the challenges that lie ahead remain formidable. The survey results reported by Jha et al warn us that in spite of the broad-based enthusiasm for the HITECH program among US hospitals, certain required functionalities remain challenging to implement. Attainment of the HITECH objectives is by no means certain. The HITECH legislation represents a once-in-a-generation opportunity to deploy tools that could address many of the problems underlying the US healthcare system. While the healthcare community appears to be making good progress toward making these tools available to clinicians, these tools will mean little unless we leverage them to redesign healthcare to be safe, effective, patient-centered, timely, efficient, and equitable.

From Office of Economic Analysis, Evaluation and Modeling, Office of the National Coordinator for Health IT, Department of Health and Human Services (MFF), Washington, DC; Brigham and Women’s Hospital (EP), Harvard Medical School, Boston, MA.

Michael F. Furukawa, PhD, Acting Director, Office of Economic Analysis, Evaluation and Modeling, Office of the National Coordinator for Health IT, Department of Health and Human Services, 200 Independence Ave, SW, Washington, DC 20201. E-mail: Michael.Furukawa@hhs.gov.

Articles

SPECIAL ISSUE

The authors discuss the wide-reaching impact of legislation spurring the adoption of health information technology and provide an overview of the content found in this special issue.

Meaningful Use of Health Information Technology: Evidence Suggests Benefits and Challenges Lie Ahead

To evaluate the effectiveness of an intervention based on health information technology (HIT) that used speech recognition software to promote adherence to inhaled corticosteroids (ICS) among individuals with asthma who were members of a large health maintenance organization.

 Adults with asthma enrolled in a large managed care organization (N = 8517) were randomized to receive either usual care or an interactive voice recognition (IVR) intervention designed to prompt medication refills and improve ICS adherence. The primary outcome was ICS adherence as measured by modified medication possession ratio calculated from the electronic medical record (EMR). Secondary measures included survey- and EMR-based measures of asthma morbidity.

 Our primary analyses found that ICS adherence increased modestly but significantly for participants in the intervention group relative to those in the usual care group (Δ = 0.02, 95% confidence interval 0.01-0.03), with a baseline adherence of 0.42 in both groups. No difference was observed in asthma morbidity measures. In post hoc analyses of participants receiving 2 or more direct IVR contacts or detailed messages, the intervention effect was more marked. The overall effect was triple that observed in the primary analyses (0.06 vs 0.02), and significant differences were observed between groups in asthma control.

An HIT-based adherence intervention shows potential for supporting medication adherence in patients with chronic diseases such as asthma. However, additional research is needed to determine how best to enhance the reach and effectiveness of such interventions.

(Am J Manag Care. 2011;17(12 Spec No.):SP79-SP87)

	An intervention based on health information technology (HIT) that utilized automated refill reminders resulted in a small but significant overall improvement in asthma medication adherence among managed care patients.

		 For that subset of patients who accepted the reminder phone calls, the benefit for medication adherence was larger and resulted in an improvement in asthma control.

		 Future HIT strategies that more fully integrate physicians, pharmacists, and other clinical staff into adherence promotion systems have the potential to address more complex barriers to adherence and assist patients in the successful long-term management of chronic disease.

	Despite the proven efficacy of anti-inflammatory therapy in the management of asthma, patient nonadherence is common.

The clinical implications of this nonadherence include treatment failure; unnecessary and dangerous intensification of therapy; and excess healthcare costs, hospitalizations, and deaths.

	Relatively few studies have examined strategies to improve adherenc with respiratory medications.

 A review of primarily adult-focused adherence interventions stressed the need for innovative approaches to assist patients in following chronic medication regimens,

 while others have called for strategies that leverage health information technologies (HITs) to promote and sustain medication adherence.

	Interactive voice recognition (IVR) technology has been widely used to deliver automated health education via telephone and to remind patients about appointments or health screening activities.

 Such applications have been shown to have a significant effect on both behavioral and clinical outcomes.

 The use of speech recognition software can further enhance the acceptance and effectiveness of this form of telephonebased interaction.

 Linking IVR applications with electronic medical records (EMRs) offers additional opportunities to provide personalized adherence messages triggered by a patient’s own refill patterns. A lowcost, HIT-based adherence intervention, if successful, would have immediate application for improving chronic disease management across a wide range of medical conditions.

	We report the main results of a randomized clinical trial designed to test the effectiveness of an HIT-based intervention using speech recognition software to promote adherence to inhaled corticosteroids (ICS) among adults with asthma.

 among patients receiving care in a routine clinical setting in which 8517 adults with asthma were randomized to receive either usual care or an IVR intervention designed to improve ICS adherence. The study was approved by the institutional review boards of each participating institution.

	Kaiser Permanente (KP) is a group-model HMO that provides comprehensive, prepaid healthcare service to about 450,000 members of the Northwest region (KPNW) and 230,000 members of the Hawaii region (KPH). KPNW serves a population that is largely Caucasian (≈91%), while the KPH population includes about 27% Caucasians, 33% Asians, 12% native Hawaiians or Pacific Islanders, and about 24% of mixed heritage. Both KPNW and KPH utilize an EMR that includes pharmacy dispensings.

	The target patient population consisted of adult KPNW and KPH members with asthma who met the eligibility criteria in 

. To assure maximum generalizability, we did not exclude individuals with comorbid physical or mental health conditions.

	For research-related logistical reasons (eg, to eliminate the need for multiple rounds of introductory mailings, to simplify the programming that would be required with a rolling enrollment) we “prerandomized” a patient pool who made up our potential sample. However, in keeping with how the intervention would be used in clinical practice, our analysis protocol prespecified an inclusion criterion that only those individuals who ever received (or for usual care participants, who would have qualified for) intervention calls were included in the analysis samples. Thus, while included in the randomization pool, patients who never qualified for a call did not receive an intervention and were not part of our primary (intention-to-treat) analysis; this design does not introduce bias because prerandomized control group patients were handled in the same way.

	In order to be able to study adherence among both new ICS users and preexisting ICS users, we included in the target population members without an ICS dispensing prior to randomization. This article focuses on adherence among preexisting ICS users, who were the primary focus of the grant and were defined as having at least 1 ICS dispensing in the 12 months prior to their qualifying ICS dispensing or order. We also briefly describe findings for new ICS users.

	Of 15,164 individuals who were sent an invitation letter, 1100 (7.3%) opted out of the study and the remaining 14,064 were randomized to either the intervention or the usual-care arm, with randomization stratified by region and the clinic facility to which each patient was paneled. Over the 18 months of intervention calling, 8517 individuals qualified for 1 or more calls, of whom 6905 were preexisting ICS users and 1612 were new ICS users. The primary reasons for not qualifying for a call were the lack of a triggering ICS dispensing or order among potential new ICS users (52%) and perfect adherence to a monthly ICS regimen (33%). The Figure shows how the study sample was chosen.

	The intervention included 3 basic IVR call types, each of which typically lasted 2 to 3 minutes: a refill reminder call, a tardy refill call, and an initiator/restart call. Each month, we scanned the EMR to determine who was eligible for which type of call.

	The refill reminder call went to participants whose last ICS dispensing was at least 1 month ago and who had fewer than 30 days of supply left, assuming appropriate use. The call reminded patients that they were due for a refill and offered a transfer to the automated pharmacy refill line and/or information about KP’s online refill service. The tardy refill call went to individuals who were more than 1 month past their projected refill date. It not only reminded patients that they were due for an ICS refill, but also assessed asthma control, explored ICS adherence barriers, and provided tailored educational messages. Patients in poor control who declined to be transferred to the automated pharmacy refill line were offered the option to speak to a live pharmacist. Finally, the initiator/ restart call was designed to provide support to patients who were either starting ICS for the first time (new users) or were lapsed users. These calls went to individuals with an ICS order or dispensing in the previous month and no other ICS dispensing in the previous 6 months, and were similar to the tardy refill calls in that they included probes for asthma control and adherence barriers and offered tailored educational messages.

	When possible, the calling program left messages on answering machines or with another household member if the target participant was not at home. As part of the first direct contact with each participant, the scripted IVR call asked for permission to leave detailed messages that included the name of the target asthma medication. Lacking this, the phone messages simply noted it was the Breathe Easy Medication Reminder Program calling and asked the participant to call back on a toll-free number.

	We surveyed 2000 randomly selected individuals prior to randomization and again at the end of the study. Additional data, including our primary adherence measures, were derived from the EMR

 We used a modification of the medication possession ratio (MPR)

 as our primary outcome measure. The MPR is computed as the days of supply of medication during a given time window divided by the time between the first dispensing in the window and the end of the window. Our modified MPR (mMPR) also accounted for medication that was on hand at the start of the window and ignored any days of supply that would extend beyond the end of the window. We also assumed that medications were used as directed and that a new ICS canister was not started until any medication on hand was exhausted.

 Since all participants were prior ICS users or had an ICS order prior to randomization, we assumed they met criteria for persistent asthma and hence should have been taking ICS throughout their entire intervention period. 

 This enabled us to calculate an mMPR for everyone, not just for those with at least 1 dispensing. Finally, since the implied adherence associated with medication on hand at the start of randomization could not be related to intervention status, we ignored this initial period in our calculations. A baseline mMPR was computed for the 12 months prior to the start of call eligibility in a similar manner. In computing the mMPR, we treated all ICS medications, including combination agents containing an ICS, as equivalent. The mMPR was an indication of the proportion of days during our observation window that patients had ICS medication available. Thus, an mMPR of 1.0 suggests that a patient was dispensed enough medication to cover all the days during the window, while an mMPR of 0.5 suggests that a patient was dispensed enough ICS medication to cover half of the window of observation.

We used data from the EMR for the year prior to qualifying for calling (the baseline year) to capture the following information: age, race, sex, smoking status, comorbid chronic obstructive pulmonary disease (COPD; defined as either a visit coded 490, 491, 492, or 496 during the baseline year or the presence of 1 of these codes on the patient’s problem list), acute asthma healthcare utilization (urgent care, emergency department care, or hospitalization), oral steroid use, short-acting beta agonist (SABA) use, and total number of distinct medications filled in the past 12 months. We classified smoking status based on the most recently available EMR data prior to the start of call eligibility.

 The baseline and follow-up surveys captured information related to race and asthma-specific health status (ie, the Juniper mini-Asthma Quality of Life Questionnaire [AQLQ]).

The follow-up survey also included the Asthma Therapy Assessment Questionnaire (ATAQ)asthma control index

 and a series of questions (for those randomized to the active intervention arm) related to satisfaction with the intervention.

 For each call we captured the date and type of the call, participant responses during the call, and information on transfers.

We used self-reported race from the surveys if available, and otherwise relied on EMR-based race information where available. We used geocoding to map zip code information to block-level income data as a proxy for household income.

	For our primary analysis, we excluded 474 preexisting users (6.9% of those who qualified for calls) who either appeared to be daily users of oral steroids (n = 215) or had fewer than 3 months of follow-up data (n = 259). We excluded users with daily oral steroid use because these individuals would be unlikely to gain additional benefit from ICS for preventive purposes and users with fewer than 3 months of follow-up because we thought that the mMPR would be too unreliable in this group.

The proportions excluded were very similar in the 2 intervention groups (7.0% vs 6.7%, P = .68). A sensitivity analysis that included these individuals gave similar results.

	For the analysis of mMPR scores we used a general linear model (with assumed normal errors) that adjusted for performance site (KPH vs KPNW), sex, age (18-45, 46-60, 

61 years), baseline SABA use (0, 1-2, 3 canisters), comorbid COPD status (any diagnosis with code 492/496, any diagnosis with code 490/491 but not code 492/496, or no COPD), and approximate tertiles of baseline mMPR (<0.20, 0.20-0.55, 

0.55). We used duration of follow-up as a weighting variable to reflect the fact that these adherence measures become more accurate and reliable with longer follow-up. Results are also expressed for subgroups defined by age, sex, tertiles of baseline mMPR, baseline SABA usage, and comorbid COPD status. Appropriate subgroup-by-treatment interaction terms were used to formally test whether treatment effects differed across subgroups. We also conducted logistic regression analyses of the proportion of individuals with good adherence (defined as mMPR 

	For the analysis of SABA use and acute asthma healthcare utilization, we used overdispersed Poisson regression analysis to analyze the rate of use (canisters per year and acute visits per year, respectively) while adjusting for the same set of baseline covariates. However, we restricted analysis of SABA use to those patients without comorbid COPD because SABA use is very common in this latter group and hence might not be affected by the intervention.

Automated telephone reminders resulted in a small but significant increase in adherence to inhaled corticosteroids among adult asthma patients in a large managed care organization.

Use of Health Information Technology to Improve Medication Adherence

The increasingly widespread adoption of electronic health records (EHRs) is substantially changing the American healthcare delivery system. Differences in the actual effectiveness of EHRs and their component applications, including electronic prescribing (e-prescribing), is not well understood. We compared the effects of 2 types of e-prescribing systems on medication safety as an example of how comparative effectiveness research (CER) can be applied to the study of healthcare delivery.

We previously conducted 2 non-randomized, prospective studies with pre—post controls comparing prescribing errors among: (1) providers who adopted a standalone e-prescribing system with robust technical and clinical decision support (CDS) and (2) providers who adopted an EHR with integrated e-prescribing with less robust available CDS and technical support. Both studies evaluated small groups of ambulatory care providers in the same New York community using identical methodology including prescription and chart reviews. We undertook this comparative effectiveness study to directly compare prescribing error rates among the 2 groups of e-prescribing adopters.

 The stand-alone system reduced error rates from 42.5 to 6.6 errors per 100 prescriptions (

 <.001). The integrated system reduced error rates from 26.0 to 16.0 per 100 prescriptions (

= .07). After adjusting for baseline differences, stand-alone users had a 4-fold lower rate of errors at 1 year (

 Despite improved work flow integration, the integrated e-prescribing application performed less well, likely due to differences in available CDS and technical resources. Results from this small study highlight the importance of CER that directly compares components of healthcare delivery.

(Am J Manag Care. 2011;17(12 Spec No.):SP88-SP94)

	Comparative effectiveness research (CER) can be expanded beyond its usual focus on treatment or intervention options to evaluate healthcare delivery systems, including electronic health records (EHRs) and electronic prescribing (e-prescribing).

		 Our small CER study of ambulatory providers found that use of a stand-alone e-prescribing application led to a greater reduction in prescribing errors than use of an integrated EHR with e-prescribing.

		 System features, as well as implementation and training resources, likely contributed to these findings.

		 Given the large national investment in EHRs, future CER research should be conducted to understand the actual effects of EHR systems in active use.

	The Health Information Technology for Economic and Clinical Health (HITECH) Act is providing up to $30 billion for the use of interoperable electronic health records (EHRs) in meaningful ways that improve healthcare quality, safety, and efficiency.

Specific measures will be used to determine if a provider meets criteria for incentives, including electronic prescribing (e-prescribing).

 To decrease intersystem variability, providers must use a certified EHR, including eprescribing that meets certain standards. However, even among certified EHRs, differences in performance arise in actual system use.

	Comparative effectiveness research (CER) is designed to inform healthcare decisions by providing evidence on the effectiveness, benefits, and harms of different treatment options and interventions.

Traditionally, CER has directly compared the outcomes of 2 therapeutic interventions, most commonly medications, using randomized controlled trials. To date, there has been little CER evaluating different health information technology (HIT) in actual use.

	Previously, using identical methodology, we conducted 2 separate prospective studies comparing the effects of 2 types of e-prescribing systems on medication safety.

The systems were implemented and used by ambulatory providers in a single community but had important differences. The first was a stand-alone system with advanced clinical decision support (CDS) and extensive technical support. The second was an e-prescribing application integrated within an EHR with less robust available CDS and more limited technical support. These studies provided an ideal opportunity to perform a novel comparative effectiveness study of these 2 system types. Our hypothesis was that despite technical and CDS limitations, the integrated application would better improve medication safety due to improved work flow integration and increased diversity of patient data from the EHR available to the prescriber at the point of care.

	In this study, we compare the effectiveness of a commercially available stand-alone e-prescribing system to an e-prescribing system integrated within an EHR by analyzing paper prescriptions at baseline and e-prescriptions 1 year later for 21 ambulatory care providers in the same New York community. The data were obtained from 2 previous pre—post studies evaluating prescribing safety among e-prescribing adopters compared with control providers who used handwritten prescriptions. 

 The same group of 15 non-adopters served as concurrent controls for both original studies and, in each case, prescribing errors were high at baseline for all study participants but were significantly higher for non-adopters at 1 year. We obtained institutional review board approval from Weill Cornell Medical College, and providers gave written informed consent.

	The Institute of Medicine classifies medication errors as any error in the medication use process (prescribing, transcribing, dispensing, administering, and monitoring).

We focused only on prescribing errors, such as omitting quantity to be dispensed. Near misses were prescribing errors with potential for harm that were either intercepted or reached the patient but did not cause harm. An example was prescribing penicillin for a patient with a known allergy who did not receive the medication because of pharmacist error detection. Adverse drug events (ADEs) were injuries from a medication, a subset of which was preventable. Rule violations were departures from strict standards of prescribing that were well understood and unlikely to cause harm, such as failure to write “po” for a medication only taken orally. These were not included in error rates but were counted, as they can result in significant rework.

	We studied 11 adult primary care practices in a predominantly rural and suburban region of New York State between September 2005 and July 2008. Fifteen providers from 6 different practices adopted a stand-alone system and 6 providers from 5 practices adopted an integrated system. Physicians were members of the not-for-profit independent practice association. All members were sent a letter in May 2005 detailing incentives for adopting e-prescribing and inviting them to participate in a research study. Discounts on EHR licenses were provided as an incentive. All practices ranged in size from 1 to 7 providers and none were academically affiliated.

	The stand-alone system was a Web-based, commercially available system. Providers had access to an electronic reference guide for dosing recommendations, medication lists, and allergies, if this information had been entered. The system provided CDS alerts for drug allergies, drug—drug interactions, duplicate drug therapies, incorrect drug frequencies, incorrect dosing, and pregnancy and breast-feeding contraindications. The system checked for insurance eligibility and formulary compliance. Prescriptions could be sent to pharmacies electronically. This system was ultimately not certified, as it was not integrated within an EHR. Providers performed other clinical documentation on paper.

E-prescribing System Integrated Within an EHR

	The integrated system was a commercially available system fully integrated within an EHR. Providers had electronic access to all information in the EHR including patient history, medications, allergies, diagnoses, and laboratory and demographic data. The system provided the same types of alerts as the stand-alone system but additionally provided drug—disease interactions and disease-specific drug recommendations. Patient insurance eligibility and formulary compliance were also checked and prescriptions could be sent electronically to pharmacies. Notably, the e-prescribing module was a more immature module due to limited configuration of the CDS at the time of the study. CDS for e-prescribing was fully configured after the study’s completion.

	A for-profit Health Information Service Provider (HSP) provided implementation and ongoing technical support, including routine monitoring of e-prescribing compliance to encourage 100% use.8 Because the HSP was new to providing implementation services at the time the integrated practices went live, those providers received far less training initially (on average 1 hour) compared with stand-alone users (on average 40 hours), who went live later.

 We collected carbon copies at baseline and electronic downloads of all prescriptions written by providers during a 2-week period at 1 year. During both time periods, we obtained a minimum of 75 prescriptions on at least 25 patients per provider, extending data collection if necessary, and limited review to 3 prescriptions per patient to minimize clustering of errors. Non-duplicate prescription pads were removed at baseline to ensure compliance with use of the duplicate prescription pads.

 A research nurse reviewed each prescription in an identical manner guided by extensively utilized, standardized methodology.

Training included review of error definitions, legibility assessments, and review of test and actual cases. Two data collectors jointly reviewed cases, after which reviewers worked independently. Reviewers classified prescribing errors, and rule violations, and evaluated the use of ADE-trigger drugs. Inappropriate abbreviation errors were from the Joint Commission on Accreditation of Healthcare Organizations’ “Do Not Use” list, established to denote abbreviations with great potential to cause medical errors.

	We determined interrater reliability by having 2 reviewers examine the same random sample of 2% of the data. Interrater agreement for overall and type of error was 1.0, indicating excellent agreement.

An ambulatory chart review was performed for suspected near misses or when a drug often used to treat an ADE was prescribed.

Two physicians blinded to the providers’ prescribing method independently reviewed all suspected near misses and ADEs. Interrater agreement for the presence of prescribing errors and near misses was 0.96 and 0.93, indicating excellent agreement.

 We compared error rates per 100 prescriptions of (1) stand-alone and integrated adopters at baseline, (2) stand-alone adopters at baseline and 1 year, (3) integrated adopters at baseline and 1 year, and (4) between stand-alone and integrated adopters at 1 year using mixedeffects Poisson regression models that included e-prescribing system type, study time, and an interaction term system type and study time. We adjusted for clustering at the provider level and assumed an independent correlation structure for these Poisson models. We calculated 95% Poisson confidence intervals (CIs) with cluster robust standard errors for the rates. We used SAS for PC version 9.2 (SAS Institute Inc, Cary, North Carolina) to estimate Kappa statistics, 

 tests, and Stata 11 (StataCorp, College Station, Texas) to

	estimate mixed-effects Poisson regression models and to calculate 95% Poisson CIs with clustered robust standard errors.

	There was no significant difference between provider groups (

). All physicians were board certified in internal or family medicine. The mean number of prescriptions written per provider and patients seen per provider were similar at baseline (0.65 and 0.26, respectively). At 1 year, integrated adopters saw significantly more patients and wrote significantly more prescriptions during the study period (0.002 and 0.002, respectively).

	For stand-alone adopters, 1273 unique patients seen at baseline and 1598 unique patients seen at 1 year received prescriptions (

). For integrated adopters, 481 unique patients seen at baseline and 368 unique patients seen at 1 year received prescriptions. At baseline, patients of stand-alone adopters were significantly older (56 vs 53 years, 

 <.001) and at 1 year more were female (60% vs 54%, 

	We reviewed 2550 prescriptions at baseline, 1901 of which were written by stand-alone adopters and 649 of which were from integrated adopters (

	Rates of baseline prescribing errors for both groups were high, with planned adopters of integrated e-prescribing 1.6- fold less likely to have prescribing errors (

 = .03). Rule violations were also high at baseline for both groups, but with no statistical differences. Near misses were detected infrequently and without statistical differences.

Rates of Errors for Stand-alone E-prescribing Adopters at Baseline and 1 Year

This is a comparative effectiveness study that evaluates the safety effects of 2 types of commercially available electronic prescribing systems.

The Comparative Effectiveness of 2 Electronic Prescribing Systems

 This study sought to examine the implementation of an electronic health record— based intervention to improve quality of hypertension care in community health centers. The primary goal was to use qualitative analysis to explain how different components of the intervention contributed to positive patient-level outcomes.

 The intervention included alerts, order sets, templates, clinical reminder algorithms, and provider performance feedback. Semi-structured interviews were conducted with primary care providers before (n = 16) and after (n = 16) intervention, and with key staff and leadership involved in the implementation (n = 6). The research team applied an iterative systematic qualitative coding process to identify salient themes. Several constructs from IT implementation theories guided the analysis.

 The analysis focused on: (1) satisfaction and perceived usefulness of intervention components, (2) perceived proximal changes resulting from intervention, and (3) perceived facilitators of change. Different participants found different components useful. Proximal impact manifested in multiple ways (eg, more aggressive follow-up

	appointments and prescribing) and in increased overall attention to hypertension. Facilitators of success included leadership, organizational culture, provider engagement, rigorous implementation process, framing of intervention as quality improvement (QI), and health center capacity to process data.

 We attribute the success of the intervention to a multifaceted approach where the combination of multiple intervention components resulted in across the-board change in hypertension care practices. In contrast with research that attempts to isolate the impact of circumscribed health information technology (HIT) tools, our experience suggests that HIT can achieve success in patient outcomes when rigorously implemented as a multifaceted intervention and framed as QI activity.

(Am J Manag Care. 2011;17(12 Spec No.):SP95-SP102)

	Qualitative process evaluation of a multifaceted technology-driven hypertension intervention in community health centers resulted in the following key findings and conclusions:

		 Multiple intervention components contributed to changes in hypertension care practices, supporting the multifaceted approach to health information technology implementation.

		 Framing the technology-driven intervention as quality improvement was a facilitator of success.

		 Other facilitators included leadership, organizational culture, provider engagement, rigorous implementation process, and health center capacity to process electronic health record data.

		 Qualitative process evaluation was found to be a feasible and useful method for investigating reasons for the success of a multifaceted technology-based intervention.

	Hypertension affects one-third of the American adult population.

 Appropriate management of hypertension presents a significant opportunity to improve cardiovascular health outcomes through interventions in primary care settings. The costs of healthcare, medication, and missed days of work from hypertensionrelated symptoms and complications were $76.6 billion in 2010.

 Improved control of blood pressure among those diagnosed with hypertension would pay off in lowered incidence of stroke and heart disease.

	Health information technologies (HITs) are a promising tool for improving quality of care in primary care settings, including underserved settings such as community health centers (CHCs).

Much of HIT research tests the efficacy of isolated technology components, such as a decision support tool or computerized order entry. Less is known about the effectiveness of HIT-based interventions in community-based settings that combine multiple intervention components in a comprehensive effort to improve quality-of-care outcomes.

 Evidence of effectiveness of HIT to improve hypertension outcomes is mixed.

	Effective interventions to improve community health outcomes call for complex multi-level and multi-component approaches.

However, it is challenging to design studies that can attribute quantitative health outcomes to particular intervention components.

Instead, the outcomes indicate success of the entire “package.” Process evaluation is an avenue to explore questions about the effective “ingredients” of such interventions. One important purpose of process evaluation is to explain the results of an outcome evaluation,

 also called “interpretive evaluation.”

Process evaluation using qualitative methods may be particularly wellsuited for understanding why a complex intervention led to its outcomes.

	In the domain of information technology (IT) implementation, several models posit factors that explain success of IT. The Technology Acceptance Model (TAM)

builds on general social-behavioral theories and proposes constructs such as perceived usefulness and perceived ease of use as predictors of end-user acceptance and use of IT. The DeLone & McLean Information System Success Model

 predicts organizational and individual impact of IT systems from system use, user satisfaction, system quality, and information quality. A third model proposed by Ovretveit et al

 identifies factors that help or hinder HIT implementation, including characteristics of the HIT system, the implementation process, leadership, resources, and organization culture and climate. These models are helpful in categorizing and labeling factors of importance in HIT implementation research.

	This qualitative study examined a multi-component quality improvement (QI) intervention in a CHC with several practice sites in New York that included clinical decision support within an electronic health record (EHR) and provider feedback. The intervention had a positive impact on provider adherence to hypertension guidelines

 and hypertension control among patients.

 The goal of the qualitative analysis was to explain how different facets of the multi-component intervention contributed to the positive impact of improved hypertension control.

	The study setting was Open Door Family Medical Centers, a federally qualifying CHC with 4 primary care sites located in suburban communities in New York. The CHC provides primary care to approximately 40,000 patients annually. It is a safety-net provider with a patient population approximately 74% Hispanic, 15% non-Hispanic white, and 9% non-Hispanic black; 35% of the patients have Medicaid, 4% Medicare, 4% private insurance, and 57% no insurance. In May 2007, the CHC started using an EHR system (eClinicalWorks). The leadership of the CHC has a strong interest in systematic QI activities and was eager to use the new EHR as a QI tool. At baseline of the study, 14% of all CHC patients were diagnosed with hypertension and an additional 5% were found to have undiagnosed hypertension. About half of hypertensives had controlled blood pressure at their last visit.

	The CHC leadership had identified hypertension as a QI target because it was the most prevalent adult chronic condition, a significant contributor to leading causes of mortality, and appeared feasible to improve through appropriate management. The CHC had recently engaged in a successful QI diabetes program and wanted to now turn their attention to hypertension. The intervention was designed by a collaborative team that included the administrative and medical staff from the CHC and public health researchers. Components of the intervention were informed by pre-intervention interviews with all providers practicing at the 4 study sites. Several key components of the intervention were implemented in the CHC’s EHR system, including alerts of high blood pressure readings, and templates, order sets, and clinical reminder algorithms for hypertension management. The templates and order sets offered standard sets of items to be included in hypertension visits while the clinical reminder algorithm prompted specific actions based on patient characteristics. Providers also received trainings and quarterly report cards (feedback) indicating levels of hypertension control among their panel of patients. The report cards were discussed in group meetings, followed up by one-on-one meetings with the CHC Medical Director for providers who performed below average. Additional details of all components of the intervention are described elsewhere.

	The outcome evaluation design compared repeated measures of hypertension control before and after the intervention. The proportion of visits in which hypertension was controlled was 51% at baseline and increased significantly to 61% in the post-intervention period.

 The process evaluation included quantitative surveys of providers and qualitative interviews of providers and key informants. The quantitative surveys were primarily used to measure provider attitudes preintervention and to inform intervention design. This paper reports results based on the qualitative interviews.

	All providers practicing at the 4 health centers (n = 16) were interviewed about 6 months prior to and at 3 to 4 months following the launch of the intervention. The interview questions focused on perceptions and experiences regarding hypertension care, hypertension guidelines, EHRs, organizational environment, intervention components, and intervention implementation. Key informants (n = 6) representing leadership and staff actively involved in implementation of the intervention were interviewed about the implementation experience about 5 to 6 months after the intervention began. The semistructured interviews were conducted by the evaluators and lasted about 30 to 45 minutes. They were audio-recorded and transcribed for analysis. Human subject participation was approved by the Institutional Review Boards at New York University and Columbia University Medical Center. Informed consent was obtained from all interview participants.

	A team of 4 researchers conducted an iterative process of identifying a set of thematic codes and then applying them systematically to all interview transcripts. The team first reviewed key concepts in the 3 theoretical models (TAM, De- Lone & McLean’s model, and Øvretveit’s model). They then read all interview transcripts, identifying salient themes and notable quotations, and collaboratively drafted a hierarchically organized list of thematic codes. The final coding manual included a total of 78 themes and sub-themes. Using Atlas.ti qualitative analysis software, the researchers applied the codes systematically to all transcripts. To verify agreement, 10% of transcripts were coded by 2 researchers. For this paper, the codes were further reorganized under key process evaluation questions, as described below and shown in the 

	The findings were organized into 3 main domains that emerged as we examined the data in light of process evaluation research questions and implementation theories:

	1. Satisfaction and perceived usefulness of intervention components

	2. Perceived proximal changes resulting from the intervention

	3. Perceived facilitators of change Each of the 3 domains contributes answers to why the intervention had a successful outcome. The Table shows how the 3 domains relate to key functions of process evaluation and to major constructs of the 3 theoretical models used to guide the interpretation of the data.

1. Satisfaction and Perceived Usefulness of Intervention Components

	Assessing satisfaction among participants (or users) is a staple of process evaluation. The TAM construct of “perceived usefulness,” which is widely used in IT research,11 broadens an assessment of satisfaction to include reasons why the person likes an IT tool, and in particular how it facilitates work activities. When asked 

why they liked the different intervention components

, the providers phrased their answers in terms of perceived usefulness. The interviews prompted respondents about their satisfaction with each of the 5 major intervention components: alert, order set, template, clinical reminders, and provider report card. In addition, respondents reported on their satisfaction with the training associated with the intervention and the development of hypertension guidelines tailored for the CHC.

Having elevated blood pressure readings listed in red was generally well accepted and considered highly effective in bringing attention to blood pressure.

	“It does help. Sometimes you may just glance over, but when it’s red it does stand out much more. Especially when the blood pressure was just, say, 130 over 90. A lot of times we would have just ignored it, but having that red does help.”

 The hypertension order set was not universally endorsed, but 9 of the 16 providers discussed ways they found it to be useful. It was reported as helpful for remembering recommended orders and validating the treatment plan:

	“I sometimes try to remember to use that order set because I do find that that helps me track when labs were done. That to me is the most helpful.”

	“I like to be validated in what I do, and since this is not my typical patient that I see, I like to see that. The little hint for the labs, the immunizations, and the appointments are all the pros of it.”

A qualitative process evaluation attributes the success of a technology-driven hypertension intervention to the combination of multiple intervention components framed as quality improvement.

Qualitative Evaluation to Explain Success of Multifaceted Technology-Driven Hypertension Intervention

 To assess the impact of an electronic medical record (EMR) with clinical decision support (CDS) and performance feedback on provider adherence to guideline-recommended care and blood pressure (BP) control compared with a standard EMR alone.

Quasi-experimental with repeated measures.

The study was conducted in a 4-site, federally qualified health center, Open Door Family Medical Centers, located in New York. The research team, Open Door leadership, providers, and staff developed and implemented a tailored multicomponent CDS system, which included a BP alert, a hypertension (HTN) order set, an HTN template, and clinical reminders. We extracted patient-level data for each encounter 17 months prior to implementation of the intervention (June 2007-October 2008) and 15 months post-intervention (April 2009-June 2010), from the EMR’s data tables for all adult nonobstetric patients with a diagnosis of HTN (N = 3636).

Rates of HTN control were significantly greater in the post-intervention period compared with the baseline period (50.9% vs 60.8%; 

 <.001). Process measures, derived from the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure Guidelines, also improved significantly. Logistic regression with generalized estimating equations showed that patients were 1.5 times more likely to have controlled BP post-intervention than pre-intervention. Correlates of poor BP control were black race, higher body mass index, diabetes, female gender, income, and a greater number of prescribed antihypertensive medications.

Our findings suggest that health information technology that is implemented as part of a multicomponent quality improvement initiative can lead to improvements in HTN care and outcomes.

(Am J Manag Care. 2011;17(12 Spec No.):SP103-SP110)

An analysis of a multifaceted, technology-driven intervention to improve hypertension control in community health centers resulted in the following key findings:

 A theory-driven approach to tailoring health information technology (HIT) to local context, through user input and an iterative testing process, can facilitate its adoption.

 Clinical decision support systems should incorporate features that have been shown to affect quality of care.

 When implemented as part of a multifaceted quality improvement initiative, HIT can play a central role in assessing performance and improving adherence to care standards.

 An HIT-driven intervention can also improve hypertension-related patient outcomes.

Hypertension (HTN) is the most prevalent modifiable risk factor for cardiovascular disease among US adults, and it is among the most common reasons for an outpatient medical visit.

 Despite the availability of effective medications and published guidelines for the treatment of HTN, half of US adults who have been diagnosed with HTN have poorly controlled blood pressure (BP).

 Several recent literature reviews support a variety of implementation strategies aimed at increasing provider adherence to guideline-recommended care processes, including clinical reminder systems and performance feedback.

Numerous studies have also examined the efficacy of clinical decision support systems (CDSS) in improving the quality of preventive care.

 Yet, despite the theoretical and intuitive benefits of such technologies, the existing literature has demonstrated mixed empirical results.

 Moreover, few studies have examined the deployment of CDSS to improve the quality of HTN management.

 Finally, most studies of CDSS have been conducted in academic health centers or the Veterans Administration system.

We are aware of only 1 study of CDSS conducted in community health centers (CHCs).

 Yet, problems with quality of care and disparities are a particular concern for CHCs, which provide care for more than 15 million Americans, many of whom are most at risk for cardiovascular disease—related morbidity and mortality.

 Hicks et al found that among CHCs participating in Health Resources Service Administration Collaboratives, those with CDSS provided better quality of care than those without this technology.

 With primary care practices, including CHCs, responding to federal “Meaningful Use” requirements to adopt health information technology (HIT) by 2014, there is a need for greater understanding of technology-driven quality improvement strategies.

 The primary aim of this study was to compare the impact of a multicomponent intervention, which included an electronic medical record (EMR) with CDSS and registry-linked performance feedback, with a standard EMR alone on provider adherence to care recommendations and BP control as defined by the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7 Guidelines).

We used a quasi-experimental design with repeated measures. For data analysis, BP measures were collected for each patient encounter for 17 months pre-intervention (June 2007-October 2008) and 15 months (April 2009-June 2010) post-intervention. The 5-month period from November 2008 to March 2009 was the intervention adoption period.

The study was conducted at a 4-site federally qualified CHC, Open Door Family Medical Centers (Open Door), located in New York. The health centers provide primary care to approximately 40,000 patients annually, its population being primarily Hispanic (73.5%), with 60% foreign born. Thirty-five percent of patients had Medicaid and 58% were uninsured. In May 2007, Open Door installed eClinical-Works (eCW), an EMR and practice management system, at all 4 sites. Prior to the intervention, HTN control was part of the quarterly provider report card, which included 15 quality improvement measures, that was e-mailed to providers. However, the measures were not benchmarked against other providers or a target outcome. The EMR did not include CDSS components related to HTN.

We created a longitudinal database from EMRs for all adult nonobstetric patients with a diagnosis of HTN who had at least 1 visit during the study period. Hypertensive patients were defined as those with a documented 

International Classification of Diseases, Ninth Revision

The study team, which included Open Door leadership, providers and staff (ie, Chief Medical Officer [CMO], Director of Performance Improvement [DPI], Executive Director, and a physician representative), and staff from Primary Care Development Corporation, a non-profit organization with expertise in practice change and improvement, developed a set of measurable clinical goals related to HTN management that were based on JNC 7 Guidelines (

 The goals were agreed to by the clinic’s Quality Improvement Committee, and became the basis for the development of the intervention and the set of data to be extracted from the EMR for analysis.

As part of the intervention development, we conducted quantitative and qualitative interviews with all clinical staff and administrative leadership. The results established a baseline for evaluating any changes in attitudes as well as informed the study team about what tools the providers wanted from the EMR, their facility in using the EMR, training requirements, and other workflow and support issues. The main intervention components included CDSS, provider performance feedback, and provider training. Based on the pre-intervention interviews, consultation with the study team, and review of the literature, several features for the CDSS were implemented.10,12 These included:

, to present the provider with information to be obtained from the patient related to his/her HTN, and facilitate documentation;

, to prompt clinical support staff to ask patients about taking their medications and document the responses;

 focused on HTN, allowing the provider to access a single screen when ordering tests or treatment (

, to prompt providers to screen for tobacco use and/or update indicated tests (eg, lipid profile).

We provide more detail about the CDSS features in 

. In brief, the system was integrated with charting and order entry processes to support overall HTN-care workflow. For example, CDSS features appeared at the time and location of decision making (eg, when ordering medication), rather than just providing assessments, and occurred in multiple locations in the EMR.

The individual performance feedback was designed to enhance the existing report card system. The DPI ran quarterly reports on performance measures consistent with the main study outcomes, such as the percentage of HTN patients with visits in the previous quarter, whose BP was controlled. The CMO presented the overall performance of each site compared with Open Door’s target BP control at a regular quarterly staff meeting. Provider-level performance reports, which were benchmarked to predetermined targets were also e-mailed quarterly to providers, who could access their personal report through the EMR.

For clinical staff (physician assistants, nurse practitioners, medical doctors), the CMO conducted two 2-hour training sessions. The first included a review of JNC 7 HTN Guidelines and an overview of the HTN quality initiative, including clinical goals and objectives, as well as available baseline data. At the second training session, the CMO and research team demonstrated the new CDSS features, distributed a customized user manual dedicated to HTN CDSS features, and reviewed new policies and procedure guidelines for HTN management that were developed by the study team. Clinical support staff, including medical assistants, registered nurses, and licensed practical nurses, were trained together in a single 45-minute session using a curriculum customized to their roles (eg, importance of recording height and weight, asking about medication adherence, and using CDSS to record assessments).

The primary outcome was BP control before and after the intervention, as defined by JNC 7 criteria (BP <130/80 mm Hg for patients with diabetes or kidney disease and <140/90 mm Hg for all other patients). This was analyzed at the patient level and the encounter level. At the patient level, we report pre- and post-intervention mean systolic and diastolic blood pressures (SBP, DBP) and the proportion of patients with BP controlled at the last visit at both time periods (

). The process measures included the proportion of patients who had a documented body mass index (BMI), electrocardiogram (ECG), lipid profile, and nutrition visit in each study period, timeliness of followup visits when BP was out of normal range, and medication intensification. Medication intensification was defined as the percentage of visits with a new medication added, titration of the original antihypertensive medication, or both, among visits with an elevated BP.

We describe the process for extracting encounter-level data from eCW’s back-end database in a previous article.21 Briefly, data from eCW’s underlying tables were extracted using BridgeIT, a data management utility that mapped Open Door’s EMR data into easily accessed and queried Microsoft Access software. Study variables were extracted from the data warehouse for each patient encounter, saved in a Microsoft Excel file, and transmitted securely to the Data Coordinating Center at Columbia University.

After excluding patients of ethnicities other than black, white, or Hispanic and those with more than 30 visits, 3636 patients (28,263 encounters) were included in the final analysis. Data collected during the intervention adoption period were excluded from all analyses except for the time series in the 

The distribution of sample characteristics (

) among black, white, and Hispanic ethnicities was analyzed using analysis of variance (ANOVA) tests and 

 analysis. A preand post-intervention comparison on BP outcomes, as well as clinical process measures, was conducted using ANOVA tests and Fisher exact tests (Table 1). We also graphed changes in BP control over time using aggregated monthly encounter level data (Figure 1) and conducted an ANOVA test for significance of the BP trends over the 36-month study period.

) for BP control at any visit was developed using logistic regression considering the following covariates: age, BMI, gender, income level, race/ethnicity, diabetes status, and the number of HTN-related medications. Treatment effect was captured by an indicator that takes on a value of 1 if the particular visit occurred during the post-intervention period and 0 if the visit occurred during the baseline period. Generalized estimating equations

Health information technology that is implemented as part of a multifaceted quality improvement initiative can lead to improvements in hypertension care and outcomes.

Special Issue: Health Information Technology - Guest Editors: Michael F. Furukawa, PhD; and Eric Poon, MD, MPH

Meaningful Use of Health Information Technology: Evidence Suggests Benefits and Challenges Lie Ahead

Use of Health Information Technology to Improve Medication Adherence

The Comparative Effectiveness of 2 Electronic Prescribing Systems

Qualitative Evaluation to Explain Success of Multifaceted Technology-Driven Hypertension Intervention

Technology-Driven Intervention to Improve Hypertension Outcomes in Community Health Centers

Public Attitudes Toward Health Information Exchange: Perceived Benefits and Concerns

Progress Toward Meaningful Use: Hospitals' Adoption of Electronic Health Records