David R. West

Event Reporting to a Primary Care Patient Safety Reporting System: A Report From the ASIPS Collaborative

BACKGROUND We examined reports to a primary care, ambulatory, patient safety reporting system to describe types of errors reported and differences between anonymous and confidential reports. METHODS Applied Strategies for Improving Patient Safety (ASIPS) is a demonstration project designed to collect and analyze medical error reports from clinicians and staff in 2 practice-based research networks: the Colorado Research Network (CaReNet) and the High Plains Research Network (HPRN). A major component of ASIPS is a voluntary patient safety reporting system that accepts reports of errors anonymously or confidentially. Reports are coded using a multiaxial taxonomy. RESULTS Two years into this project, 33 practices with a total of 475 clinicians and staff have participated in ASIPS. Participants submitted 708 reports during this time (66% using the confidential reporting form). We successfully followed up on 84% of the confidential reports of interest within the allotted 10-day time frame. We ended up with 608 relevant, codable reports. Communication problems (70.8%), diagnostic tests (47%), medication problems (35.4%), and both diagnostic tests and medications (13.6%) were the most frequently reported errors. Confidential reports were significantly more likely than anonymous reports to contain codable data. CONCLUSION A safe and secure reporting system that relies on voluntary reporting from clinicians and staff can be successfully implemented in primary care settings. Information from confidential reports appears to be superior to that from anonymous reports and may be more useful in understanding errors and designing interventions to improve patient safety.

An Electronic Practice-Based Network for Observational Comparative Effectiveness Research

Key Summary Points The Distributed Ambulatory Research in Therapeutics Network (DARTNet) is a federated network linking health data from 8 organizations representing more than 500 clinicians and more than 400 000 patients. Electronic health record and other clinical data are aggregated, standardized, and stored within each organization, then deidentified and made available for secure queries through the Web. A full set of patient data never leaves the individual clinical sites. DARTNet can be used for observational comparative effectiveness research, which may provide important information about safe and effective health care. DARTNet tools can prompt clinicians to obtain specific information during a patient encounter. Key clinical information traditionally missing in comparative effectiveness research is now available through DARTNet. The Distributed Ambulatory Research in Therapeutics Network (DARTNet) is a federated network of electronic health data that was created to facilitate advanced observational comparative effectiveness research and examine outcomes associated with prescription medications and devices. A federated network links geographically and organizationally separate databases so that a single database query can return results from multiple databases while maintaining the privacy and confidentiality of patient data. DARTNet currently links patient-level clinical data from 8 organizations representing more than 500 clinicians and more than 400000 patients. This article presents an overview of DARTNet, explains the contributions of DARTNet to the field of comparative effectiveness research, and summarizes the initial research study and lessons learned. Overview of DARTNet The Agency for Healthcare Research and Quality funded the creation of DARTNet through its Developing Evidence to Inform Decisions about Effectiveness Network. DARTNet was created to examine outcomes associated with prescription medications, devices, and various approaches to medical care. Within each member organization, DARTNet assembles patient-level information (such as vital signs, social history, family history, and physical examination findings) from electronic health records, laboratory tests, imaging results, pharmacy utilization databases, and billing systems. All of this information is put into 1 database that is deidentified and made available for secure access through the Web. Each organizations aggregated, deidentified clinical database is linked to similar databases in other DARTNet member organizations; the relationship among these databases is the federated network. The DARTNet research team can submit a precise request for information through a database query. The query runs simultaneously on each federated database, and the results provide highly detailed information about care and outcomes for hundreds of thousands of patients across the United States. The DARTNet technology currently works with 5 brands of ambulatory electronic health records, but it is capable of working with essentially any ambulatory electronic health record. In addition to facilitating queries among the standardized and federated databases, the DARTNet system can prompt clinicians to obtain specific information during a patient encounter. This capability allows the research team to collect additional data beyond what would normally be recorded in electronic records. Thus, DARTNet is designed to combine the essential elements of observational research with elements of a practical clinical trial (1). DARTNet is also designed to help support a learning community. By comparing clinical care provided across the network, high-performing practices and systems can be identified. One expectation of DARTNet membership is that high-performing sites will share their approaches to care with other members. The DARTNet staff is working to develop methods and systems to support this process. The learning community process is one of the main reasons that clinicians and practices wish to join DARTNet. Advancing the Science of Clinical Effectiveness Research By allowing examination of routine care from many clinicians, DARTNet provides insight that would not otherwise be available. Much of the current evidence base for health care depends on the results of randomized trials; however, those trials do not adequately account for the variability seen in actual care (1). Furthermore, randomized trials are not a practical way to answer important questions about the many possible variations in chronic disease treatment (1). Patients, physicians, payers, purchasers, health care administrators, and public health policymakers need better information to compare the effectiveness of treatments and thus make sound health care decisions from the individual to the national level (1). Traditionally, observational comparative effectiveness researchers examine data sets created for other purposes, such as insurance claims data. Cohorts of patients created from large populations are then compared. Statistical matching algorithms are applied to try to account for underlying clinical differences among the groups (2, 3), and the resulting analysis provides comparisons of outcomes of treatment. Although this methodology is powerful, it has been criticized because it cannot account for important clinical information not available in claims databases (4). Two brief examples demonstrate how the findings of a comparative effectiveness study might be skewed. First, a typical observational comparative effectiveness study of oral hypoglycemic medications cannot account for body mass index differences between cohorts. In addition, a comparative effectiveness examination of liver toxicity related to a particular medication would be much more robust if it could account for the use of alcohol or acetaminophen among cohort patients. DARTNet provides key missing clinical information of this type, which will advance the field of comparative effectiveness research. Research Using DARTNet To demonstrate the capabilities of DARTNet, we conducted a retrospective cohort study of patterns of use and comparative effectiveness and safety of oral diabetes medications for adults with type 2 diabetes. In phase 1 of the study, a limited set of data elements from a commercially available, integrated medical claims database (the Ingenix National Managed Care Impact database [Ingenix, Eden Prairie, Minnesota]) was used to examine the comparative effectiveness and safety of oral diabetes medications. This is a standard approach to observational comparative effectiveness research. A secondary aim was to identify limitations in claims databases for which DARTNet could supply useful and otherwise unavailable information. Phase 2 was designed to replicate phase 1 of the study, this time using DARTNet data. For phase 2, we studied a smaller patient sample but examined a broader range of data elements. We looked at electronic health record data and tested point-of-care data collection. Phase 2 findings show that DARTNet identified similarly sized panels of diabetic patients and patients receiving various oral diabetes medications, permitting analyses of similar power to the claims-based study in phase 1. In additional, DARTNet added useful clinical data (such as body weight, height, self-reported alcohol intake, and self-reported hypoglycemic events) to the analysis of comparative effectiveness of oral diabetes medications. Lessons Learned and Next Steps Our experience in developing DARTNet indicates that the system has broad utility and power. This power derives from the systems ability to access and standardize an array of data (approximately 150 data elements at this time) from various electronic health records and other clinical databases. Nonetheless, we encountered difficulty locating particular types of data in the electronic health records. It can be challenging to standardize data from separate practices, even those using the same electronic health record (5). We also found that many data fields in electronic health records lack reasonable range checks. For instance, most electronic health records will accept a systolic blood pressure in the thousands of mm Hg. Correction of these errors, although infrequent, will require work from the developers of electronic health records. DARTNet currently relies on a third-party clinical decision support system to extract and aggregate clinical data at each organization (6). The ongoing use of extracted data for clinical purposes serves as a continuous quality control feedback process: Clinicians who rely on the clinical decision support quickly recognize errors in the data and therefore routinely correct problems. Even with the use of powerful clinical decision support tools, we encountered difficulty in our attempts to view a discrete episode of care that spans multiple encounters. Many essential features linked to a given visit, including diagnoses, medication refills, and procedures, can be lost when the data are extracted for clinical decision support purposes. To improve its ability to examine episodes of care, DARTNet will include billing data to better distinguish encounters and procedures as well as focused point-of-care data collection. Even with effective decision support tools at their disposal, practices varied greatly in clinical outcomes and performance. This could be construed as a data quality issue, but it also highlights the potential for the system to add value to members through their participation in a learning community that identifies top-performing members, disseminates best practices, and provides facilitation to enhance clinical care at the practice level. The DARTNet prototype was connected to both small offices and large group practices specifically to demonstrate that it can include a wide array of organizations with varying informatics configurations, support levels, and ability to manipulate their information management environment.

Health information exchange in small-to-medium sized family medicine practices: Motivators, barriers, and potential facilitators of adoption

PURPOSE For small-to-medium sized primary care practices (those with 20 or fewer clinicians), determine desired functions of health information exchange (HIE) and potential motivators, barriers, and facilitators of adoption. METHODS Case study approach with mixed quantitative and qualitative methods. Nine practices in Colorado were purposively selected. Five used paper records and four were already participating in health information exchange. RESULTS Practices particularly desired HIE functions to allow anywhere/anytime lookup of test results and to consolidate delivery of test results. HIE-generated quality reporting was the least desired function. Practices were motivated to adopt HIE to improve the quality and efficiency of care, although they did not anticipate financial gains from adoption. The greatest facilitator of HIE adoption would be technical assistance and support during and after implementation. Financial incentives were also valued. Trust in HIE partners was a major issue, and practices with rich professional and social networks appeared to be especially favorable settings for HIE adoption. CONCLUSIONS These findings may assist policymakers in promoting adoption of HIE among small-to-medium sized primary care practices, a major component of the US healthcare system.

Rates of 5 Common Antidepressant Side Effects Among New Adult and Adolescent Cases of Depression: A Retrospective US Claims Study

BACKGROUND Antidepressants are the first-line treatment for depression, yet medication-related side effects may be associated with antidepressant discontinuation before reaching a period of exposure believed to result in effectiveness. There is a gap in knowledge of the prevalence of side effects across commonly prescribed antidepressants and the effect of the type of antidepressant on the likelihood of side effects in real-world clinical practice. OBJECTIVE The aim of this study was to estimate and compare the prevalence of headaches, nausea or vomiting, agitation, sedation, and sexual dysfunction among patients diagnosed with depression who initiated monotherapy across different classes of antidepressants and to estimate the effect of the type of antidepressant on the likelihood of each of the 5 side effects. METHODS A retrospective cohort of patients aged ≥13 who were newly diagnosed with depression and began antidepressant monotherapy was created using LifeLink managed care claims from 1998 to 2008. Antidepressant groups included selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), tricyclic antidepressants (TCAs), monoamine oxidase inhibitors (MAOIs), bupropion, phenylpiperazine, and tetracyclic antidepressants. Prevalence of headache, nausea or vomiting, agitation, sedation, and sexual dysfunction were compared across antidepressant groups. Propensity-adjusted Cox proportional hazards regression was used to estimate the likelihood of each of the 5 side effects for each antidepressant group compared with SSRIs, adjusted for demographic, clinical, and treatment characteristics. RESULTS The study cohort included 40,017 patients (3617 adolescents, aged 13-18 years, and 36,400 adults, aged ≥19 years; mean age = 45 years; 67% female) with a new episode of depression who were initiated on antidepressant monotherapy within 30 days of diagnosis (SSRI [66%], bupropion [14%], SNRI [12%], other [8%]). The most common side effects were headache (up to 17/1000 person-months of therapy in adults and adolescents) and nausea (up to 7.2/1000 in adults, 9.3/1000 in adolescents). Relative to adults receiving SSRIs, adults receiving SNRIs had a higher risk of nausea (hazard ratio [HR] = 1.26; 95%CI,1.05-1.51). Adults (HR = 0.78; 95% CI, 0.62-0.96) and adolescents (HR = 0.43; 95% CI, 0.21-0.87) taking bupropion were less likely to experience headaches compared with adults and adolescents, respectively, taking an SSRI. Adolescents receiving a tetracyclic were more likely to experience headaches than adolescents receiving an SSRI (HR = 3.16; 95%CI, 1.13-8.84). CONCLUSIONS Prevalence and risk of the 5 side effects varied across types of antidepressants for both adults and adolescents. Results from this study were consistent with prior clinical trials, suggesting that variation in side effect profiles exists in a more generalized managed care population.

The Role of the Champion in Primary Care Change Efforts: From the State Networks of Colorado Ambulatory Practices and Partners (SNOCAP)

Background: Change champions are important for moving new innovations through the phases of initiation, development, and implementation. Although research attributes positive health care changes to the help of champions, little work provides details about the champion role. Methods: Using a combination of immersion/crystallization and matrix techniques, we analyzed qualitative data, which included field notes of team meetings, interviews, and transcripts of facilitator meetings, from a sample of 8 practices. Results: Our analysis yielded insights into the value of having 2 discrete types of change champions: (1) those associated with a specific project (project champions) and (2) those leading change for entire organizations (organizational change champions). Relative to other practices under study, those that had both types of champions who complemented each other were best able to implement and sustain diabetes care processes. We provide insights into the emergence and development of these champion types, as well as key qualities necessary for effective championing. Conclusions: Practice transformation requires a sustained improvement effort that is guided by a larger vision and commitment and assures that individual changes fit together into a meaningful whole. Change champions—both project and organizational change champions—are critical players in supporting both innovation-specific and transformative change efforts.

Database Design to Ensure Anonymous Study of Medical Errors: A Report from the ASIPS Collaborative

Medical error reporting systems are important information sources for designing strategies to improve the safety of health care. Applied Strategies for Improving Patient Safety (ASIPS) is a multi-institutional, practice-based research project that collects and analyzes data on primary care medical errors and develops interventions to reduce error. The voluntary ASIPS Patient Safety Reporting System captures anonymous and confidential reports of medical errors. Confidential reports, which are quickly de-identified, provide better detail than do anonymous reports; however, concerns exist about the confidentiality of those reports should the database be subject to legal discovery or other security breaches. Standard database elements, for example, serial ID numbers, date/time stamps, and backups, could enable an outsider to link an ASIPS report to a specific medical error. The authors present the design and implementation of a database and administrative system that reduce this risk, facilitate research, and maintain near anonymity of the events, practices, and clinicians.

Enhancing Electronic Health Record Measurement of Depression Severity and Suicide Ideation: A Distributed Ambulatory Research in Therapeutics Network (DARTNet) Study

Background: Depression is a leading cause of morbidity worldwide. The majority of treatment for depression occurs in primary care, but effective care remains elusive. Clinical decision making and comparative studies of real-world antidepressant effectiveness are limited by the absence of clinical measures of severity of illness and suicidality. Methods: The Distributed Ambulatory Research in Therapeutics Network (DARTNet) was engaged to systematically collect data using the 9-item Patient Health Questionnaire (PHQ-9) at the point of care. We used electronic health records (EHRs) and the PHQ-9 to capture, describe, and compare data on both baseline severity of illness and suicidality and response and suicidality after diagnosis for depressed patients in participating DARTNet practices. Results: EHR data were obtained for 81,028 episodes of depression (61,464 patients) from 14 clinical organizations. Over 9 months, data for 4900 PHQ-9s were collected from 2969 patients in DARTNet practices (this included 1892 PHQ-9s for 1019 adults and adolescents who had at least one depression diagnosis). Only 8.3% of episodes identified in our depression cohort had severity of illness information available in the EHR. For these episodes, considerable variation existed in both severity of illness (32.05% with no depression, 26.89% with minimal, 19.54% with mild, 12.04% with moderate, and 9.47% with severe depression) and suicidality (69.43% with a score of 0, 22.58% with a score of 1, 4.97% with a score of 2, and 3.02% with a score of 3 on item 9 of the PHQ-9). Patients with an EHR diagnosis of depression and a PHQ-9 (n = 1019) had similar severity but slightly higher suicidality levels compared with all patients for which PHQ-9 data were available. The PHQ-9 showed higher sensitivity for identifying depression response and emergent (after diagnosis) severity and suicidality; 25% to 30% of subjects had some degree of suicidal thought at some point in time according to the PHQ-9. Conclusions: This study demonstrated the value of adding PHQ-9 data and prescription fulfillment data to EHRs to improve diagnosis and management of depression in primary care and to enable more robust comparative effectiveness research on antidepressants.

Practice Facilitation to Improve Diabetes Care in Primary Care: A Report From the EPIC Randomized Clinical Trial

PURPOSE We investigated 3 approaches for implementing the Chronic Care Model to improve diabetes care: (1) practice facilitation over 6 months using a reflective adaptive process (RAP) approach; (2) practice facilitation for up to 18 months using a continuous quality improvement (CQI) approach; and (3) providing self-directed (SD) practices with model information and resources, without facilitation. METHODS We conducted a cluster-randomized trial, called Enhancing Practice, Improving Care (EPIC), that compared these approaches among 40 small to midsized primary care practices. At baseline and 9 months and 18 months after enrollment, we assessed practice diabetes quality measures from chart audits and Practice Culture Assessment scores from clinician and staff surveys. RESULTS Although measures of the quality of diabetes care improved in all 3 groups (all P <.05), improvement was greater in CQI practices compared with both SD practices (P <.0001) and RAP practices (P <.0001); additionally, improvement was greater in SD practices compared with RAP practices (P <.05). In RAP practices, Change Culture scores showed a trend toward improvement at 9 months (P = .07) but decreased below baseline at 18 months (P <.05), while Work Culture scores decreased from 9 to 18 months (P <.05). Both scores were stable over time in SD and CQI practices. CONCLUSIONS Traditional CQI interventions are effective at improving measures of the quality of diabetes care, but may not improve practice change and work culture. Short-term practice facilitation based on RAP principles produced less improvement in quality measures than CQI or SD interventions and also did not produce sustained improvements in practice culture.

Comparative Effectiveness Research in DARTNet Primary Care Practices Point of Care Data Collection on Hypoglycemia and Over-the- Counter and Herbal Use Among Patients Diagnosed With Diabetes

Background:The Distributed Ambulatory Research in Therapeutics Network (DARTNet) is a federated network of electronic health record (EHR) data, designed as a platform for next-generation comparative effectiveness research in real-world settings. DARTNet links information from nonintegrated primary care clinics that use EHRs to deliver ambulatory care to overcome limitations with traditional observational research. Objective:Test the ability to conduct a remote, electronic point of care study in DARTNet practices by prompting clinic staff to obtain specific information during a patient encounter. Research Design:Prospective survey of patients identified through queries of clinical data repositories in federated network organizations. On patient visit, survey is triggered and data are relinked to the EHR, de-identified, and copied for evaluation. Subjects:Adult patients diagnosed with diabetes mellitus that scheduled a clinic visit for any reason in a 2-week period in DARTNet primary care practices. Measures:Survey on hypoglycemic events (past month) and over-the-counter and herbal supplement use. Results:DARTNet facilitated point of care data collection triggered by an electronic prompt for additional information at a patient visit. More than one-third of respondents (33% response rate) reported either mild (45%) or severe hypoglycemic events (5%) in the month before the survey; only 3 of those were also coded using the ICD-9 (a significant difference in detection rates 37% vs. 1%). Nearly one-quarter of patients reported taking an OTC/herbal, 4% specifically for the treatment of symptoms of diabetes. Conclusions:Prospective data collection is feasible in DARTNet and can enable comparative effectiveness and safety research.

Case study of a primary care-based accountable care system approach to medical home transformation.

We report a case study of a mature primary care–based accountable care organization that is both a health plan and a network of medical homes. Over 20 years, WellMed Inc (San Antonio, Texas) implemented many patient-centered services, experimenting to find which belong within clinics and which operate best as system functions. The adjusted mortality rate is half that of the state for people older than 65 years. Hospitalization and readmission rates and emergency department visits have not changed over time, but preventive services have improved. Phased implementation across the network makes it difficult to link improvements to specific processes but they seem to have improved outcomes collectively.