To examine the prevalence of high out-of-pocket burdens and self-perceived financial barriers to care among patients receiving hypertension treatment.

Persons 18 to 64 years receiving treatment for hypertension from a nationally representative sample of the US population from the 2007 to 2009 Medical Expenditure Panel Survey.

The proportion of persons living in families with high a out-ofpocket burden associated with medical spending relative to income, defining high healthcare burden as spending on healthcare greater than 20% of income and high total burden as spending on healthcare and insurance premiums greater than 20% of income.

The prevalence of high total burdens was significantly greater for persons receiving treatment for hypertension (13.1%) compared with other chronically ill (10.5%) and well patients (5.3%). Among hypertension patients with high total burdens, 15.7% said they were unable to get care and 13.6% said they delayed care due to financial reasons. Self-perceived financial barriers were highest among the uninsured and those with public coverage: 35.2% among the uninsured and 23.9% among those with public coverage said they were unable to get care due to financial reasons.

High burdens may deter patients from getting needed care. Our findings have 2 distinct policy implications. First, raising awareness among providers regarding the prevalence of high out-ofpocket burdens and financial barriers to care may encourage physicians to discuss healthcare coverage and associated costs with their patients. To the extent that patients’ perceptions about their ability to pay are incorrect, physicians can help patients overcome barriers to treatment. Second, health plans could reduce patient cost sharing on drugs for which there is a strong body of evidence documenting cost-saving treatment such as antihypertensive medication. Addressing financial barriers to care may improve treatment adherence among patients with hypertension.

		Among nonelderly adults with hypertension treatment, 13.1% had high total burdens (ie, healthcare expenditures-healthcare and insurance premiums), accounted for more than 20% of their income. Those with private non-group insurance were the most likely to have high total burdens (49.9%), followed by the uninsured (21.0%), those with public insurance (18.6%), and those with private group insurance (9.3%).

		Among hypertension patients with high total burdens, 15.7% said they were unable to get care and 13.6% said they delayed care due to financial reasons.

		Raising awareness among providers regarding the prevalence of high out-of-pocket burdens and self-perceived financial barriers to care may encourage providers to discuss health care coverage and associated costs with their patients. To the extent that patients’ perceptions about their ability to pay are incorrect, physicians can change these perceptions.

		Furthermore, health plans could reduce patient cost-sharing on drugs for which there is a strong body of evidence documenting cost-saving treatment such as antihypertensive medication. Addressing financial barriers to care may improve treatment adherence among patients with hypertension.

Affecting more than 65 million Americans and a leading modifiable risk factor for heart disease and stroke, hypertension represents a costly public health challenge in terms of mortality and healthcare expenditures.

 According to the World Health Organization, hypertension contributes to approximately two-thirds of cerebrovascular burdens and half of the coronary heart disease burdens in the world.

 In 2010, heart disease and stroke represented the first and third-leading causes of death in the United States.

Total direct costs of cardiovascular disease care were $273 billion and indirect costs were $172 billion in 2010 (calculated in 2008 US dollars).

Furthermore, disparities in hypertension prevalence exist between US racial and ethnic groups. While the 2009 to 2010 US prevalence of hypertension (adult, all races) equaled 28.6%, the non-Hispanic black population experienced a significantly higher prevalence of hypertension than the non-Hispanic white and Mexican American populations (age-adjusted rates of 40.4% vs 27.4% and 26.1%, respectively). 

 For these reasons, addressing the effective prevention, detection, treatment, and control of hypertension all remain essential goals of public health policy and initiatives such as Healthy People 2020, the nation’s list of 10-year objectives for improving the health of all Americans; the National High Blood Pressure Education Program of the National Institutes of Health; and the National Quality Strategy.

A recent study estimated that benefit-to-cost ratio of antihypertensive therapy is 10:1 for men and 6:1 for women.

 Given the high societal return on investment for antihypertensive therapy, the question is why we do not do better at controlling hypertension. Medications that control hypertension have proven to reduce significantly the incidence of associated poor health outcomes such as renal failure, congestive heart failure, stroke, and ischemic heart disease, regardless of gender, age, ethnicity, or race

; however, in spite of improved control rates over recent years, the total number of patients with uncontrolled hypertension has increased.

 The importance of antihypertensive control through medical management is noted in Healthy People 2020’s Heart Disease and Stroke Objective 11.

 This objective seeks to increase the proportion of adults with hypertension who are taking a prescribed medicine to lower their blood pressure by 10%— from 63.2% during the period of 2005 through 2008 to 69.5% in 2020 (age-adjusted to the year 2000 standard population). National Center for Health Statistics data indicate that the proportion of people with hypertension who are treated has increased from 63.2% during the period of 2005 through 2008 to 76.4% during the period of 2009 through 2010.

 This trend is consistent with Healthy People 2020’s Heart Disease and Stroke Objective 11. But despite the increase in the proportion of people with hypertension who are being treated, studies indicate that approximately 50% of people who started on antihypertensive therapies discontinue their use within 6 to 12 months of initiation.

Patient treatment adherence is influenced by a number of patient-related, provider-related, and healthcare delivery— related factors.

Nonadherence can lead to higher long-term costs due to complications and avoidable hospitalizations. 

 Thus, it is important to identify factors that are associated with nonadherence.

One potential reason patients delay or go without needed care is a high out-of-pocket burden. Thus, it is important to examine health-related financial burdens among patients receiving treatment for chronic conditions such as hypertension. Many studies focus on expenditures for the treatment of hypertension only.

 Our study also considers the impact of total health-related expense, including the cost of hypertension treatment, for the individual and the family. We do this because delaying treatment or going without treatment altogether may be a result not only of hypertension-specific costs but of total health-related expenses both for the individual and the family. We examine healthcare burden relative to income, including expenditures on health insurance premiums in the total burden measure to provide a more accurate picture of health-related financial strain. The burden measure is the ratio of healthcare cost to income. In this measure, both healthcare cost and income can play a primary role. We do this because a given level of healthrelated expenditure is likely to be more burdensome for families with lower incomes. First, we examine the vartion in burdens among patients with hypertension treatment by insurance status and other sociodemographic characteristics. Second, we examine person-level spending on hypertension treatment versus other conditions. Third, we examine self-perceived financial barriers to care among patients receiving hypertension treatment.

The data are from the Medical Expenditure Panel Survey-Household Component (MEPS-HC), sponsored by the Agency for Healthcare Research & Quality (AHRQ). Every year, the MEPS-HC panel is selected from a sample of households among those that participated in the prior year’s National Health Interview Survey (NHIS). MEPSHC is a 2-year rotating panel of households designed to yield nationally representative estimates of healthcare expenditures for the civilian, noninstitutionalized population. Data are collected through 5 rounds of interviews and include medical expenditures, insurance coverage, premiums, and other socioeconomic characteristics.

MEPS-HC is a rich data source for analyzing out-ofpocket burdens because it includes all payers and all components of expenditures, unlike claims data, which only include covered services. Because MEPS-HC is a household survey, the data collected are self-reported. However, MEPS also includes a Medical Provider Component (MPC) that requests data from hospitals, physicians, home healthcare providers, and pharmacies identified by MEPSHC respondents. The MEPS-MPC is designed to obtain information on both the medical and financial characteristics of medical events. Its purpose is to supplement and/ or replace information received from the MEPS-HC respondents about the healthcare that was provided to sampled household members in the course of the survey year.

For the burden analysis among patients receiving treatment for hypertension, we pooled MEPS-HC data for the period of 2007 to 2009 to obtain a large enough sample to make reliable estimates for population subgroups. The unit of observation is a person aged 18 to 64 years. The burden measure includes out-of-pocket expenditures for all healthcare services, because burdens may be greater than the cost of hypertension treatment. Healthcare burdens are constructed as the share of family-level after-tax income spent on health-related expenditures, because family members share financial resources. Family-level burdens are then assigned to individuals within the family and the results are presented at the person level, enabling us to quantify the number of persons who live in families with high burdens.

Following previous literature, high burden is defined as health-related spending in excess of 20% of income.23-28 Although there is no consensus on what constitutes affordable costs, similar patterns across subgroups result from using alternative thresholds such as 10% and 30%. Healthcare burden includes expenditures on all healthcare services such as deductibles, copays, cost sharing, and payments for services not covered by insurance plans. Total burden includes out-of-pocket expenditures on health insurance premiums in addition to out-of-pocket expenditures on healthcare services.

We also examined the impact of perceived financial barriers. The MEPS questionnaire asked respondents if they delayed care or were unable to get care, as well as the reason for delaying or going without care. If the respondent reported that (1) they could not afford care; or (2) insurance would not approve, cover, or pay; or (3) a doctor refused the family insurance plan, we coded that person as having reported financial barriers to care. The MEPS questionnaire also asked whether delaying or going without care was a big problem.

Medical conditions were collected verbatim from households and coded by professional coders using the

 International Classification of Diseases, Ninth Revision

). Condition categories were created using AHRQ’s Clinical Classification Software (CCS), which compiles 

 codes into clinically meaningful categories.

 Our analysis was based on “treated prevalence” (ie, persons who reported medical treatment for hypertension any time during a year).

We classified persons into 3 mutually exclusive categories: (1) hypertension (persons with 1 or more medical events associated with CCS codes 98 and 99); (2) other chronic condition (persons with no medical events associated with hypertension, but who have 1 or more medical event associated with other chronic conditions); and (3) no chronic condition (persons with no medical events associated with any chronic conditions). Chronic conditions were defined based on the fully specified ICD-9-CM (Clinical Modification) diagnosis codes using the Healthcare Cost and Utilization Project’s (HCUP) Chronic Condition Indicator (CCI).

The pooled MEPS-HC sample includes 9383 persons who received hypertension treatment, 17,404 persons with other chronic conditions, and 47,650 persons with no chronic conditions. We also examined burdens among persons who reported that they have hypertension in the current year but were not actively in treatment (ie, those who chose not to get treatment for hypertension).

Each person is classified as having private group (employment- related) insurance, private non-group (individual) insurance, public insurance, or no coverage. Persons with no private or public coverage anytime during the year were classified as having no coverage. We distinguished between 2 types of private insurance because non-group insurance is generally more expensive and provides less generous benefits.

Out-of-pocket premiums were collected from household respondents for private group coverage and private non-group coverage. All premium amounts were prorated to account for the duration of coverage during the year.

Expenditures were classified into 4 service categories: hospital stays, ambulatory visits (office-based provider and outpatient), prescription medications, and “all other” services (emergency department, home health visits, dental visits, and other). All expenditure amounts were converted using the Consumer Price Index for all urban consumers and reported in 2009 US dollars.

All estimates were weighted to represent the US civilian noninstitutionalized population. Standard errors were corrected to account for the complex design of MEPS, with Taylor series linearization of the variance. Only differences statistically significant at the 5% level were discussed in the text. For further details of the methodology, see 

 show that risk of high total burdens was significantly greater for persons receiving treatment for hypertension compared with other chronically ill patients and well patients. Among patients who received hypertension treatment, 13.1% had high total burdens in contrast to 10.5% among those with other chronic conditions and 5.3% among those without chronic conditions. Cancer, diabetes, and heart disease are other chronic conditions that lead to similarly high burdens.

Burdens by Insurance Status Among Patients With Hypertension Treatment 

The average annual population of persons receiving treatment for hypertension was 30.3 million between the years 2007 and 2009. Among nonelderly adults who received hypertension treatment, 71.1% had private group coverage (21.6 million), 2.6% had private non-group coverage (0.8 million), 15.5% had public coverage (4.7 million), and 10.8% were uninsured (3.3 million).

Focusing on healthcare burdens, Table 1 shows that the uninsured and those with public coverage were the most likely to have high burdens (20.0% and 16.8%, respectively). However, in terms of total burdens, those with private non-group insurance were the most likely to have high burdens (49.9%), followed by the uninsured (21.0%), those with public insurance (18.6%), and those with private group insurance (9.3%).

Among nonelderly adults who received hypertension treatment, older age, being female, never married or widowed, having no children, not working, working parttime, not having high income (being poor, near poor/ low income, or middle income), lower education (high school or less), living in non-metropolitan statistical areas (MSAs), living anywhere except the Northeast, and having other chronic conditions were associated with high total burdens (eAppendix, Table 1).

Out-of-Pocket Expenditures by Service Type

 presents person-level spending on hypertension treatment versus other conditions among nonelderly adults who were receiving treatment for hypertension. Among all nonelderly adults who received hypertension treatment, the mean of total out-of-pocket expenditure on healthcare was $1163. Mean out-of-pocket expenditure on healthcare was highest among those with private nongroup insurance ($1796), followed by the uninsured ($1555), those with private group insurance ($1139), and those with public insurance ($897). Out-of-pocket expenditures for hypertension treatment (including all types of services for hypertension treatment) accounted for 15% of total outof- pocket expenditures on healthcare among those with treated hypertension.

Mean out-of-pocket expenditures on all prescription drugs (for the treatment of hypertension and other conditions) was $535. Prescription drugs for hypertension treatment accounted for 25% of total out-of-pocket expenditure on prescription drugs for those who received hypertension treatment.

Self-Perceived Financial Barriers to Care Among Adults Receiving Treatment for Hypertension 

Articles

POLICY

Among nonelderly adults receiving hypertension treatment, 13.1% had high burdens, meaning that healthcare expenditures accounted for more than 20% of their income.

Out-of-Pocket Healthcare Expenditure Burdens Among Nonelderly Adults With Hypertension

Electronic health records (EHRs) have potential to improve quality and safety of healthcare. However, EHR users have experienced safety concerns from EHR design and usability features that are not optimally adapted for the complex work flow of real-world practice. Few strategies exist to address unintended consequences from implementation of EHRs and other health information technologies. We propose that organizations equipped with EHRs should consider the strategy of “proactive risk assessment” of their EHR-enabled healthcare system to identify and address EHR-related safety concerns. In this paper, we describe the conceptual underpinning of an EHR-related self-assessment strategy to provide institutions a foundation upon which they could build their safety efforts. With support from the Office of the National Coordinator for Health Information Technology (ONC), we used a rigorous, iterative process to develop a set of 9 self-assessment tools to optimize the safety and safe use of EHRs. These tools, referred to as the Safety Assurance Factors for EHR Resilience (SAFER) guides, could be used to self-assess safety and effectiveness of EHR implementations, identify specific areas of vulnerability, and create solutions and culture change to mitigate risks. A variety of audiences could conduct these assessments, including frontline clinicians or care teams in different practices, or clinical, quality, or administrative leaders within larger institutions. The guides use a multifaceted systems-based approach to assess risk and empower organizations to work with internal or external stakeholders (eg, EHR developers) on optimizing EHR functionality and using EHRs to drive improvements in the quality and safety of healthcare.

The Safety Assurance Factors for EHR Resilience (SAFER) guides can be used to selfassess the safety and effectiveness of electronic health record (EHR) implementation and use, identify specific areas of vulnerability, and create solutions and culture change to mitigate risks.

		EHRs have potential to improve the quality and safety of healthcare.

		EHR users have experienced safety concerns from EHR design and usability features that are not optimal for complex work flows of real-world practice settings.

		We propose that organizations equipped with EHRs consider “proactive risk assessment” of their EHR-enabled healthcare system to identify and address EHR-related safety concerns.

Electronic health records (EHRs) have the potential to improve the quality and safety of healthcare.

 Since the enactment of the Health Information Technology for Economic and Clinical Health Act (HITECH), 

 organizations have been adopting EHRs at an unprecedented rate.

 While the challenges of rapid EHR implementation can be numerous and disruptive, EHRs have clear potential to improve quality and safety with better access to information,

 and more reliable provider-to-provider communication.

 Nevertheless, in the early stages of an EHR-enabled healthcare system, benefits thus far have been difficult to achieve and unintended consequences have emerged.

 Clinicians have experienced safety concerns from EHR design and usability features that are not optimal for complex work flows in real- world practice settings.

 To respond to these challenges, the Office of the National Coordinator for Health Information Technology (ONC) commissioned the 2012 Institute of Medicine Report, “Health IT and Patient Safety: Building Safer Systems for Better Care”

 and recently released the Health Information Technology Patient Safety Action and Surveillance Plan that lays out their response to these issues.

National initiatives to improve the safety of EHRs must be accompanied by practical and helpful strategies for those on the front lines of EHR-enabled care delivery. Strategies to address unintended consequences borne from EHR implementation are nonetheless scarce, and frontline clinicians and healthcare organizations (HCOs) are often unaware of best practices for safe EHR implementation and use. For example, they often have minimal guidance to handle problems such as too many alerts,

 a slow EHR, or an EHR that requires an excessive number of “clicks” to complete tasks. These are not skills routinely expected of healthcare providers in the past.

Clinicians are also not privy to safety concerns embedded in flawed interfaces between various components of EHRs and in the way EHRs are configured. Solutions to these problems are often multifaceted, requiring analysis and redesign of work flows and organizational processes and procedures that cannot be addressed through improvements in technology alone.

Addressing EHR-related safety concerns is inherently complex and requires a comprehensive and multifaceted systems-based approach. We propose that HCOs equipped with EHRs should consider the strategy of “proactive risk assessment” of their EHR-enabled healthcare to identify and address EHR-related safety concerns.17 Herein, we describe the conceptual underpinnings of an EHR-related self-assessment strategy to provide clinicians and HCOs a foundation upon which they could build their safety efforts.

With support from the ONC, we used rigorous, iterative methodologies to develop 9 self-assessment tools to optimize the safety and safe use of EHRs (

 These tools, the Safety Assurance Factors for EHR Resilience (SAFER) guides, are designed to help clinicians and HCOs self-assess the safety and effectiveness of their EHR implementations, identify specific areas of vulnerability, and create solutions and culture change to mitigate risks.

The goal of SAFER guide-based proactive risk assessment is to eliminate or minimize EHR-related safety hazards to build system resilience, defined as “degree to which a system continuously prevents, detects, mitigates, or ameliorates hazards or incidents so that an organization can bounce back to its original ability to provide care.”

Each SAFER guide consists of between 10 and 25 “recommended practices,” which can be assessed as fully implemented, partially implemented, or not implemented. Recommended practices help the clinic or organization know ‘what’ to do to optimize the safety and safe use of the EHR. The recommended practices address principles (

) that represent ‘why’ the recommended practices are needed, although any given recommended practice may support several principles that support health IT safety.

The methods used to identify risk areas and associated practices have been described elsewhere, but are briefly summarized here.

 To develop the content of the guides, we consulted subject matter experts in informatics, patient safety, quality improvement, risk management, and human factors engineering and usability. To ensure generalizability, we conducted site visits at both small and large practices and hospitals. We also reviewed literature to identify existing EHR-related assessment items, which we validated and refined during site visits. To ensure that the guides would be useful to our intended audiences (eg, clinicians, EHR developers, IT professionals, and quality improvement leaders), we engaged with a broad range of stakeholders, such as professional organizations representing diverse groups of intended users. We undertook multiple revisions of items in the guides to increase their applicability and interpretability by individuals with differing degrees of expertise. We also considered the perspectives of those working within organizations at different points in their EHR adoption journey. Thus, we ensured that the SAFER guides are based on best evidence and expertise currently available, as well as on field research and iterative testing.

 To facilitate wide implementation and use, the SAFER guides have been made available free of charge from ONC’s website (http://www.healthit.gov/safer/).

Content of the SAFER guides is organized around 2 conceptual frameworks that account for the complex sociotechnical system in which EHRs are implemented and the risks specific to various phases of implementation, respectively (

). The first model describes the 8 contextual dimensions of EHR-enabled healthcare systems: (1) hardware and software; (2) clinical content; (3) human-computer interface; (4) people; (5) work flow and communication; (6) internal organization policies, procedures, physical environment, and culture; (7) external rules, regulations, and pressures; and (8) system measurement and monitoring.

 Along with this sociotechnical framework, we used a 3-phase framework of EHR safety that describes risks along different points of the EHR implementation life cycle.

 The overall goal,as described by Blumenthal and Tavenner, is for healthcare organizations to move from a paper-based medical record system “up the escalator” to become fully EHRenabled healthcare systems.

 Within each phase of the 3-phase framework, all 8 dimensions of the sociotechnical model come into play. Phases remind organizations ‘which’ aspect of safety is being addressed as they adopt EHRs and build safety programs.

The first step on the “escalator” framework (safe health IT) accounts for safety events unique and specific to EHRs and often emerge early in implementation (eg, safety problems owing to unavailable or malfunctioning hardware/software). The second step (using health IT safely) addresses unsafe or inappropriate technology use, including unsafe changes in work flows that emerge due to technology use. The third step (monitoring safety) addresses use of technology to monitor healthcare processes and patient outcomes and to identify potential safety concerns before harm occurs. Together, the sociotechnical framework and 3-phase implementation framework form the conceptual foundation of the self-assessment process.

Organizations should consider using the “high priority practices SAFER guide” to identify their most pressing needs and decide which of the more specific SAFER guide(s) to use for a more in-depth self-assessment. In pilot testing at smaller practices (ie, 1-5 physicians with 1-10 clinical and administrative support staff) with experienced EHR users, it took approximately 30 minutes to work through the high priority guide practices. It may take longer for the other SAFER guides.

The SAFER guides include planning worksheets to help organizations set goals and track progress. The worksheets offer a rationale that explains ‘why’ each recommended practice is important. For example, 1 recommended practice within the “Test Results Reporting and Follow-up SAFER guide” is to create “back-up procedures (including use of surrogates) and fail-safe escalation systems” to communicate test results to responsible providers.

 The rationale is based on known risks introduced at handoffs between providers, especially those involving trainees and part-time providers.

 Finally, to help operationalize each recommended practice, the worksheets include examples that illustrate ‘how’ recommended practices can be implemented. For example, one way to implement the recommendation above is to have unacknowledged test result notifications forwarded to alternate providers or escalated to supervisors after a certain number of days.

The SAFER guides will need to be integrated within each HCO or clinic’s current patient safety programs. However, the existing patient safety structures and processes will likely need to be modified as well to incorporate the unique skill mix needed. For example, larger HCOs need informaticians or clinicians trained in the newly created sub-specialty of clinical informatics

 in addition to a multidisciplinary oversight committee to help identify risks, prioritize interventions, and review EHR-related solutions. A thorough review of the literature combined with our field research and survey suggest that currently most HCOs do not have the tools and expertise to optimize the safety and safe use of EHRs and have not built EHR safety into their patient safety programs.

The SAFER guides are intended to be used by stakeholders from several administrative and clinical departments within a large hospital. For example, in larger organizations where major departments have differences in implementation and use of EHRs, it might be beneficial for each of these departments to conduct the assessment. Additionally, departments external to organizations such as EHR vendors, clinical knowledge suppliers, or information technology infrastructure providers might also need to take part in such assessments. These include chief information officers, chief medical informatics officers, risk managers, pharmacists, diagnostic services such as laboratory and radiology services, practice managers, patient safety and quality personnel, key clinical stakeholders, and—very importantly&mdash;EHR developers. In smaller practices that rely on remotely hosted EHRs and external IT consultants, representatives of external organizations will be key participants in implementing best practices from the SAFER guides.

Self-assessment should be integrated with ongoing patient safety, quality improvement, and risk management programs both in smaller ambulatory practices and in the different departments within larger hospital-based settings. Any of these parties could leverage the SAFER guides to start a meaningful conversation on optimizing safety and safe use of EHRs. In most settings, SAFER guides could be completed by multidisciplinary teams. Similar to studies of checklists in other high-risk environments such as the operating room,

achieving a shared understanding of safe practices would facilitate better teamwork and collaboration and perhaps stimulate implementation of solutions. However, when too many stakeholders are involved, there is a risk that certain team members may lack role clarity or assume that others are primarily responsible for addressing key tasks.

 We suggest that frontline users must engage in multidisciplinary teams to conduct self-assessments because they interact with all aspects of the EHR-enabled healthcare delivery system. To lead, they must have authority and resources from organizational leadership committed to the safety and safe use of EHRs. Organizational leaders should then work with individuals or organizations (sometimes external ones, such as EHR developers) and assign responsibility for the work to implement recommended practices based on the assessment.

Although a self-assessment is generally intended to stimulate and sustain recommended practices, it can also establish a baseline for measuring effects of interventions designed to improve safety over time. If used for this purpose, the SAFER guides could be used to reassess safety at periodic intervals (eg, annually) or when changes are made to the EHR.

We found in our preliminary field research that the recommended practices in each SAFER guide apply to all types and sizes of EHR-enabled practice settings. Implementation of the recommended practices will vary by type of practice and type of EHR (eg, remotely hosted vs local server). The number and type of people involved in conducting SAFER guide assessments will also vary by practice setting. To address these variations, the SAFER guides have accompanying worksheets with specific examples of different methods to implement the recommended practices.

We also expect variation in the importance or urgency of implementing some of the recommended practices based upon the type of practice setting. Some recommended practices, such as contingency planning to avoid EHR downtimes, may be critical for tertiary care hospitals and less urgent for outpatient clinics that do not perform complex surgical procedures or provide overnight care.

 In addition, it is important for assessment results to be discussed with EHR vendors and open the dialogue about solutions and patches that might have been created for other clients. For example, EHR vendors may have developed a solution to the specific problem identified but not yet released this fix to all their customers. Furthermore, it is important for parties conducting these assessments to carefully weigh the risks that apply to their settings as well as the difficulty in implementing the required changes in hardware, software, or clinical work flow.

 The SAFER guides are designed to help all types and sizes of EHRenabled practice settings target areas of concern specific to their unique circumstances and resources.

To fulfill the promise of EHRs, the SAFER guides can empower organizations, and practitioners that work within them, to address their EHR-related problems along front lines of care. Furthermore, they could help front-line users and other stakeholders have meaningful conversations on how to optimize EHR functionality and how to use EHRs to drive substantial improvements in the quality and safety of healthcare. These conversations, though likely a first step in a long process for most organizations, could go a long way toward helping leverage the potential of EHRs to improve health and healthcare. 

 University of Texas Health Science Center at Houston’s School of Biomedical Informatics and the UT-Memorial Hermann Center for Healthcare Quality & Safety, Houston, TX (DFS); Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR (JSA); Houston Veterans Administration Health Services Research & Development Center for Innovations in Quality, Effectiveness and Safety, the Michael E. DeBakey Veterans Affairs Medical Center and the Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX (HS).

 The SAFER project is supported through a subcontract from Westat (HHSP-23320095655WC0095655; Anticipating the Unintended Consequences of Health IT) funded by the Office of the National Coordinator for Health Information Technology (ONC) (HHSP23337003T; to Drs Sittig, Ash, and Singh). Dr Singh was supported in part by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development and the Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413).

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the ONC. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

 Concept and design (DFS, JSA, HS); acquisition of data (DFS, JSA, HS); analysis and interpretation of data (DFS, JSA, HS); drafting of the manuscript (DFS, JSA, HS); critical revision of the manuscript for important intellectual content (DFS, JSA, HS); obtaining funding (DFS, JSA, HS); administrative, technical, or logistic support (HS).

 Dean F. Sittig, PhD, UT-Memorial Hermann Center for Healthcare Quality & Safety, 6410 Fannin St, UTPB 1100.43, Houston, TX 77030. E-mail: dean.f.sittig@uth.tmc.edu.1. Blumenthal D, Glaser JP. Information technology comes to medicine. N Engl J Med. 2007;356(24):2527-2534.

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3. Wright A, Henkin S, Feblowitz J, McCoy AB, Bates DW, Sittig DF. Early results of the meaningful use program for electronic health records. N Engl J Med. 2013;368(8):779-780.

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10. ECRI Institute PSO Deep Dive: Health Information Technology. ECRI Institute website. https://www.ecri.org/EmailResources/PSRQ/ECRI_ Institute_PSO_Deep%20Dive_HIT_TOC.pdf. Published December 2012.

11. Farley HL, Baumlin KM, Hamedani AG, et al. Quality and safety implications of emergency department information systems. Ann Emerg Med. 2013;62(4):399-407.

12. Institute of Medicine. Health IT and Patient Safety: Building Safer Systems for Better Care. Washington DC: The National Academies Press; 2012.

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14. Weingart SN, Seger AC, Feola N, Heffernan J, Schiff G, Isaac T. Electronic drug interaction alerts in ambulatory care: the value and acceptance of high-value alerts in US medical practices as assessed by an expert clinical panel. Drug Saf. 2011;34(7):587-593.

15. Carspecken CW, Sharek PJ, Longhurst C, Pageler NM. A clinical case of electronic health record drug alert fatigue: consequences for patient outcome. Pediatrics. 2013;131(6):e1970-e1973.

16. Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010;304(11):1227-1228.

17. Marx DA, Slonim AD. Assessing patient safety risk before the injury occurs: an introduction to sociotechnical probabilistic risk modelling in healthcare. Qual Saf Healthcare. 2003;12(suppl 2):ii33-ii38.

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TRENDS FROM THE FIELD

We describe the conceptual underpinnings of an EHR-related selfassessment strategy to provide institutions a foundation upon which they can build their safety efforts.

The SAFER Guides: Empowering Organizations to Improve the Safety and Effectiveness of Electronic Health Records

To better understand the direct costs of insomnia. Our study aimed to compare healthcare costs and utilization of patients diagnosed with insomnia who received care in a managed care organization with a set of matched controls.

Our observational, retrospective cohort study compared 7647 adults with an insomnia diagnosis with an equally sized matched cohort of health plan members without an insomnia diagnosis between 2003 and 2006. We also compared a subset of patients diagnosed with and treated for insomnia with those diagnosed with insomnia but not treated.

A large Midwestern health plan with more than 600,000 members.

Multivariate analysis was used to estimate the association between insomnia diagnosis and costs, controlling for covariates, in the baseline and follow-up periods. Although we cannot conclude a causal relationship between insomnia and healthcare costs, our analysis found that insomnia diagnosis was associated with 26% higher costs in the baseline and 46% in the 12 months after diagnosis. When comorbidities were recognized, the insomnia cohort had 80% higher costs, on average, than the matched control cohort.

These outcomes suggest the need to look beyond the direct cost of insomnia to how its interaction with comorbid conditions drives healthcare cost and utilization.

Increasing costs for treated and untreated insomnia suggest the need to look beyond the cost of insomnia to how its interaction with comorbid conditions drives healthcare cost and utilization: 

		At baseline prior to diagnosis, insomnia was associated with 26% higher healthcare costs than matched controls.

		In the 12 months following diagnosis, insomnia was associated with 46% higher costs.

		Predicted follow-up costs for the insomnia cohort were 80% higher than the controls, reflecting the relatively greater increase in morbidity in the insomnia cohort.

		Of those diagnosed with insomnia, 75% received pharmaceutical treatment.

		Those treated for insomnia had higher utilization and costs than those not treated. n Insomnia and its treatment may be indications of more serious underlying conditions.

Chronic insomnia, difficulty falling or staying asleep or experiencing poor-quality sleep, is a growing health problem with significant consequences for both individuals and the healthcare system.

 Chronic insomnia is associated with a host of physical, psychosocial, and emotional problems, including premature mortality, depression, anxiety, and poor quality of life.

 Chronic insomnia can also exacerbate comorbidities.

 In addition to the personal toll of chronic insomnia on the individual, major economic consequences for the healthcare system include increased direct medical costs and healthcare utilization.

 Research examining the economic consequences associated with chronic insomnia has highlighted the burgeoning indirect societal costs of insomnia, such as workplace absenteeism, lost productivity, and increased workplace errors and accidents.

 Less attention has been focused on direct healthcare costs and increased utilization associated with chronic insomnia.

Direct costs to the healthcare system associated with chronic insomnia are difficult to assess due to complexities and gaps in available data.

 Estimates of the aggregate costs of insomnia vary widely depending on the costs considered. 

 interviewed patients in primary care clinics to measure insomnia prevalence, associated functional impairment, lost productivity, and comorbidities. They found that 10% of patients reported insomnia, which was associated with functional impairment, disability, and increased use of health services. A more recent study estimated the direct and indirect costs of untreated insomnia and found that direct and indirect costs for adults younger than 65 years with insomnia were $1253 greater than for subjects without insomnia for the 6-month study period.

The most common treatment for insomnia is pharmacotherapy, with 2.5% of Americans taking prescription drugs to treat insomnia each year, and about 1 in 4 of them continuing treatment for 4 months or longer.

 Commonly prescribed US Food and Drug Administration (FDA)—approved hypnotics include benzodiazepines such as temazepam and triazolam, and drugs that act as agonists to benzodiazepine receptors such as zolpidem.

 These agents are efficacious in the short-term management of insomnia, but adverse effects include residual daytime sedation, cognitive impairment, motor incoordination, dependence, and rebound insomnia.

A variety of cognitive and behavioral therapy (CBT) programs are as efficacious as approved sleep medications in the short term, side effects are nil, and benefits are more durable.

 There is, however, limited access to these programs due to a shortage of trained professionals.

 A 2002 national survey estimated that 2.2% of Americans use complementary or alternative therapies for insomnia.

 However, herbals, dietary supplements, over the counter medications such as diphenhydramine, and alcoholic beverages are not recommended for insomnia treatment due to lack of efficacy data, potential for adverse effects, lack of standardization, or a combination of these concerns.

The purpose of this study was to estimate the healthcare costs associated with insomnia diagnosis by comparing costs and utilization of patients who have been diagnosed with insomnia to a set of matched controls. We compared costs in the baseline period before an insomnia diagnosis, in the 12-month follow-up period postdiagnosis, and the change in cost from baseline to follow-up. We also compared a subset of patients who received a diagnosis of insomnia and associated treatment with patients who received a diagnosis of insomnia but no treatment. This research will contribute to the scarce literature currently available on the direct costs and health services used by patients with chronic insomnia.

We conducted a retrospective observational study using data from a large Midwestern health plan with more than 600,000 members. A study cohort of 7647 adults with an insomnia diagnosis during 2003 to 2006 was identified and compared with an equally sized matched cohort of health plan members without an insomnia diagnosis. All health plan members were eligible for the study if they met the following criteria: (1) at least 18 months of continuous enrollment from January 1, 2003, to December 31, 2006; (2) aged 18 years or older; (3) continuous pharmacy coverage; and (4) Medicaid or commercial insurance coverage.

Members were considered to have insomnia if they had a qualifying insomnia diagnosis code from January 1, 2004, to December 31, 2005, preceded by a 6-month period free of an insomnia diagnosis (baseline period) and if they remained in the plan for at least 12 months after the diagnosis (follow-up period). Qualifying insomnia cohort

 International Classification of Diseases, Ninth Revisio

n codes were: 307.41 (transient disorder of initiating or maintaining sleep), 307.42 (persistent disorder of initiating or maintaining sleep), and 780.52 (insomnia unspecified). This research was reviewed and approved by the local Institutional Review Board.

A propensity score—matching method was used to create a matched control group that had similar covariate values as the insomnia cohort and therefore reduce confounding by covariate differences.

 First, a 10:1 age- and gender-matched sample of members without an insomnia diagnosis was created. Members were then matched with controls during the 6-month baseline period and 12-month follow-up period. Second, a logistic model was used to produce propensity scores for all members with an insomnia diagnosis and the 10:1 matched cohort. The dependent variable in the model was an indicator of a qualifying insomnia diagnosis, and the predictor variables were age, sex, insurance type, and specific individual baseline physical comorbidities (diabetes, cardiovascular disease, chronic obstructive pulmonary disorder, chronic heart failure, myocardial infarction, peripheral vascular disease, connective tissue disorder, ulcer disease, mild liver disease, moderate to severe renal disease, acquired immunodeficiency disorder, tumor, hemiplegia, leukemia or lymphoma, metastatic solid tumor) and specific mental health comorbidities (alcohol use disorder, opioid and substance use disorder, major depression, anxiety, bipolar disorder, posttraumatic stress disorder, dementia, schizophrenia, organic mental disorders, other psychotic disorders, affective disorders, personality disorders, or adjustment disorders). Finally, propensity scores from the model were used to identify a 1:1 cohort, matched on propensity score. This 1:1 propensity score—matched cohort was the comparison group to evaluate differences in healthcare costs and utilization. Multivariate regression methods were used to adjust for differences that remained between the cohorts after matching.

To compare members with an insomnia diagnosis with and without pharmacological treatment, pharmacological treatment was defined as a prescription fill for benzodiazepines or nonbenzodiazepines, categories of medications that have been approved by the FDA for the treatment of insomnia,

or prescriptions for medications frequently used off label for treatment of insomnia, including: trazodone, ziprasidone, amitriptyline, mirtazapine (with no depression diagnosis), doxepin, quetiapine fumarate (Seroquel), and diphenhydramine (Benadryl).

Healthcare cost and utilization data were obtained from the health plan’s administrative databases. They included type of insurance coverage, diagnostic codes for insomnia and other chronic conditions, plan reimbursed amounts, and member-paid amounts. These data were used to identify major medical comorbidities and total direct medical costs, including inpatient, outpatient, pharmacy, urgent care and emergency department services, for all members. Cost was defined as plan reimbursed amounts plus member-paid.

Multivariate analysis was used to estimate the association between study cohort and costs, controlling for covariates. The outcome was total healthcare costs, which captures the effect of insomnia on the use of healthcare services for all reasons. Estimates of the percentage of cost associated with an insomnia diagnosis were made at 3 times: the baseline period, the follow-up period, and the change from baseline to follow-up. For the baseline and follow-up analyses, a 2-part Heckman estimator was used because of the number of members in the control cohort that had no healthcare costs. First, the association between cohort and positive cost was determined by logistic regression. Second, the association between cohort and costs was estimated for those with positive costs. Because the outcomes were heavily skewed, a log transformation with Duan’s smearing estimator was used.

 For the analysis of change from baseline to follow-up, multivariate linear regression analysis was used and the dependent variable was untransformed dollars.

The covariates were age, sex, insurance type, Charlson Comorbidity Index (CCI) score, psychiatric medication use, and mental health diagnoses. We included interaction and polynomial terms that improved model performance. Comorbidities were identified using clinical classification software maintained by the Agency for Healthcare Research and Quality or defined by the health plan’s algorithms, which were summarized as a CCI score.

 The CCI includes 19 medical conditions, each with a weight of 1, 2, 3, or 6. The CCI score is the sum of the weights for the conditions identified as comorbidities. Total scores can range from 0 (no conditions) to 37 (all 19 conditions).

The parameter for insomnia diagnosis in the regression models for baseline and follow-up costs was used to estimate the percentage of costs that were associated with insomnia. To estimate the impact of insomnia and comorbidities on total costs, we used regression results to estimate predicted costs for each cohort in the follow-up period, with covariate values set at the mean value for each cohort.

We identified 7647 members with a diagnosis of insomnia who met the enrollment criteria during the identification period. They were matched with 7647 controls. In the insomnia cohort, the average age was 48 years, and 64.2% were women, compared with an average age of 49 years and 66.9% women in the control cohort (

). The average CCI score for the insomnia cohort in the baseline period was 0.26, while it was 0.28 for the control cohort. In the follow-up period, the average CCI score increased for both the insomnia and control cohorts and the percentage of members with a mental health diagnosis or psychiatric medication use increased for the insomnia cohort. In the follow-up period, all measures were greater for the insomnia cohort than for the control cohort (Table 1). Baseline and follow-up utilization and costs for each cohort are presented in 

. The insomnia cohort had higher utilization and costs in all categories than the control cohort in both baseline and follow-up periods. The average cost for the insomnia cohort during the 6-month baseline period was $5484, compared with $3937 for the control cohort. In the year-long follow-up period, the average cost for the insomnia cohort was $11,206, compared with $6939 for the control cohort.

Estimated model coefficients for the 2-part models of baseline costs are reported in Table 3. The probability of positive cost in the baseline period was higher for members in the insomnia cohort, women, and those with Medicaid insurance. Among 14,406 members with positive cost in the baseline period, members in the insomnia cohort, women, and those with Medicaid insurance, a mental health diagnosis, and use of psychiatric medication had higher costs. Costs were higher with increased age and CCI score. The parameter estimate for insomnia diagnosis was 0.23, which translates to 26% higher costs associated with insomnia in the baseline period. (The percentage change in US dollars is equal to the exponent of the parameter for log dollars, minus 1.)

Estimated model coefficients for the 2-part models of follow-up costs are reported in 

. The probability of positive cost in the follow-up period was higher for members in the insomnia cohort, females, and those with Medicaid insurance. Among 14,975 members with positive cost in the follow-up period, members in the insomnia cohort, women, and those with a mental health diagnosis and use of psychiatric medication had higher costs. Costs were higher with increased age and CCI score. Presence of Medicaid insurance was not a significant predictor of follow-up cost. The parameter estimate for insomnia diagnosis was 0.38, which translates to 46% higher costs associated with insomnia in the follow-up period. Using mean covariate values for each cohort, the predicted probability of positive costs was 100% for the insomnia cohort and 97% for the control cohort. The predicted follow-up cost for the insomnia cohort was $4395, 80% higher than predicted cost for the control cohort. The analysis of change in costs from the baseline to the follow-up period showed that insomnia was associated with an increase of $1553 from baseline to follow-up (Table 5).

Treated Versus Untreated Subsets of the Insomnia Cohort

In the insomnia cohort, 5773 (75%) were treated with a prescription medication for insomnia during the 12-month follow-up period. Compared with the insomnia cohort subset without treatment, the treated subset was slightly older (48.85 years vs 47.31 years), more likely to be women (65.7% vs 59.6%), more likely to have Medicaid insurance (8.6% vs 4.2%), and had a slightly higher CCI score (0.27 vs 0.21). The treated subset was more likely to have a mental health diagnosis in the baseline period (27.3% vs 16.0%) and to have a pharmacy claim for an antidepressant (47% vs 27.5%) and a mood stabilizer (9.0% vs 3.3%). All measures were statistically different at 

 <.005. Those differences persisted and grew into the follow-up period.

In the follow-up period, the treated subset had a higher CCI score (0.46 vs 0.29) and higher percentage of patients with a mental health diagnosis (50% vs 43%), an antidepressant prescription (77% vs 34%), and a mood stabilizer prescription (15% vs 5%) than the untreated subset. The treated subset also had significantly greater utilization and costs in all categories in both the baseline and follow-up periods. The analysis of cost change from the baseline period to follow-up period showed that the treated subset had a significantly greater increase in total costs than the untreated subset ($5527 vs $3145), and a significantly greater increase in medical cost (excluding pharmacy) than the untreated subset ($4276 vs $2309).

Our results confirm previous findings that untreated insomnia is associated with increased healthcare utilization and costs. Ozminkowski and colleagues

 found that direct costs for patients with untreated insomnia were 25% greater than a matched comparison group, $4755 versus $3831. We estimated that the insomnia diagnosis was associated with 26% higher costs in the baseline period, when members who eventually received an insomnia diagnosis were as yet untreated (Table 3). In addition to estimating the association of healthcare costs with untreated insomnia (during the baseline period), we estimated the association of healthcare costs with diagnosed insomnia during the follow-up period. We found that, in the 12 months after diagnosis, the insomnia diagnosis was associated with 46% higher costs (Table 4). Using regression results, we predicted the follow-up costs for the insomnia and control cohorts, using their respective covariate values. That analysis illustrated the impact of insomnia and comorbidities on total costs. We found that, in the 12 months after insomnia diagnosis, the insomnia cohort had 80% higher costs, on average, than the control cohort. The amount of the change from baseline to follow-up that was associated with insomnia was estimated to be $1553 (Table 5). In general, healthcare costs tend to increase from one period to another, but we found that the insomnia cohort had a greater relative increase in costs from baseline to follow-up than the control cohort. Part of that greater relative increase appeared to be due to the relative decrease in health of the insomnia cohort, as evidenced by the CCI score and mental health measures.

WEB EXCLUSIVE

Patients with an insomnia diagnosis have higher healthcare utilization and costs than a matched control group, both before and after the diagnosis.

Healthcare Utilization and Costs in Persons With Insomnia in a Managed Care Population

To demonstrate the feasibility and utility of using administrative claims data from commercial health plans to establish a high-risk indicator in a statewide immunization registry for enrollees with chronic conditions.

Administrative data were used to identify children with 1 or more chronic conditions enrolled in 2 commercial health plans during the 2008-2009 and 2009-2010 influenza seasons and matched with a statewide immunization registry. The proportion of cases that successfully matched and historical health services utilization, including influenza vaccinations and missed opportunities, were assessed.

A total of 93% of children with chronic conditions identified through administrative claims were successfully matched with the statewide registry. Less than one-third of children received the seasonal influenza vaccine in either the 2008-2009 (29%) or 2009-2010 (32%) seasons; 30% of children received the H1N1 vaccination in 2009-2010. Most children in the 2008-2009 (63%) and 2009-2010 (63%) seasons had at least 1 missed opportunity for seasonal influenza vaccination. Younger children had the highest percentage of missed opportunities while adolescents had the lowest rate of missed opportunities for vaccination. Conclusions It is feasible to identify children with chronic conditions using administrative data and to link them with a statewide immunization registry. Low influenza vaccination rates and high occurrences of missed opportunities among children with chronic conditions suggest the utility of integrating administrative claims data with statewide registries to support various outreach mechanisms, including physician-focused and parent-targeted reminder/recall, based on target age to improve vaccination rates.

Administrative data from commercial health plans can be used to identify children with chronic conditions in a statewide immunization registry. Low influenza vaccination rates and high occurrences of missed opportunities among children with chronic conditions in our sample suggest the utility of integrating administrative claims data with statewide registries. This integration can:

		Enable a population-based mechanism for identification of children with chronic conditions as priority cases during pandemic events or supply shortages.

		Support various outreach strategies to improve influenza vaccination rates, including physician-focused and parent-targeted reminder/recall.

Children with chronic conditions are especially vulnerable to complications from influenza.

 Annual influenza vaccinations have long been recommended for this group of children.

 More recently, the Advisory Committee on Immunization Practices (ACIP) adopted a universal recommendation of yearly influenza vaccination for healthy children aged 6 months to 18 years.

 Despite this recommendation, vaccination rates for seasonal influenza remain low for children with chronic conditions.

 Missed opportunities, where eligible children are seen by a practitioner but no vaccination dose is administered, have been documented among this population and may contribute to low vaccination rates.

Immunization registries, also known as immunization information systems (IISs), are well established in the United States and provide reminder/recall functions that are effective in increasing vaccination rates.

 However, IISs are not typically designed to track clinical information in addition to vaccinations and consequently cannot target reminder recall notices specifically to those with chronic conditions. Enhancing immunization registries with a high risk indicator may be a mechanism to promote increased vaccination rates among this population through the use of registry-based reminder/ recall capability. In 2006, Michigan’s IIS, known as the Michigan Care Improvement Registry (MCIR), was enhanced with a high risk indicator based on Medicaid administrative data to bolster vaccination rates among children with chronic conditions. This indicator has been demonstrated as being an effective mechanism to target reminder/ recall notices,

 although the benefits of that system have initially been limited to children enrolled in Medicaid.

With that in mind, the objective of this study was to build on the success of the Medicaid-based MCIR high risk indicator by expanding it to include administrative data from 2 commercial health plans. First, we sought to demonstrate the feasibility of using administrative claims data from commercial health plans to establish a high risk indicator in astatewide immunization registry for enrollees with an influenza-sensitive chronic condition (hereafter referred to as a “chronic condition”). We also sought evaluate the potential utility of this expansion of the MCIR high risk indicator by assessing historical influenza vaccination experiences among children with chronic conditions.

This study examines the feasibility and utility of using administrative claims from commercial health plans to identify children with chronic conditions in a statewide registry.

May 2014

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