Screening for and Diagnosis of Depression Among Adolescents in a Large Health Maintenance Organization.

OBJECTIVE: The aim of this analysis was to determine changes in patterns of depression screening and diagnosis over three years in primary and specialty mental health care in a large health maintenance organization (HMO) as part of a project to develop quality measures for adolescent depression treatment. METHODS: Two series of aggregate data (2010-2012) were gathered from the electronic health records of the HMO for 44,342 unique adolescents (ages 12 to 21) who had visits in primary and mental health care. Chi square tests assessed the significance of changes in frequency and departmental location of Patient Health Questionnaire-9 (PHQ-9) administration, incidence of depression symptoms, and depression diagnoses. RESULTS: There was a significant increase in PHQ-9 use, predominantly in primary care, consistent with internally generated organizational recommendations to increase screening with the PHQ-9. The increase in PHQ-9 use led to an increase in depression diagnoses in primary care and a shift in the location of some diagnoses from specialty mental health care to primary care. The increase in PHQ-9 use was also linked to a decrease in the proportion of positive PHQ-9 results that led to formal depression diagnoses. CONCLUSIONS: The rate of depression screening in primary care increased over the study period. This increase corresponded to an increase in the number of depression diagnoses made in primary care and a shift in the location in which depression diagnoses were made, from the mental health department to primary care. The frequency of positive PHQ-9 administrations not associated with depression diagnoses also increased.

Usual Care for Adolescent Depression From Symptom Identification Through Treatment Initiation.

IMPORTANCE: Published guidelines describing effective adolescent depression care in primary care settings include screening, assessment, treatment initiation, and symptom monitoring. It is unclear the extent to which these steps are documented in patient health records. OBJECTIVE: To determine rates of appropriate follow-up care for adolescents with newly identified depression symptoms in 3 large health systems. DESIGN, SETTING, AND PARTICIPANTS: In this analysis conducted from March to September 2014, structured data retrospectively extracted from electronic health records were analyzed for 3 months following initial symptom identification to determine whether the patient was followed up and, if so, whether treatment was initiated and/or symptoms were monitored. Records were collected from 2 large health maintenance organizations in the western United States and a network of community health centers in the Northeast. The study group included adolescents (N = 4612) with newly identified depression symptoms, defined as an elevated score on the Patient Health Questionnaire (≥ 10) and/or a diagnosis of depression. MAIN OUTCOMES AND MEASURES: Rates of treatment initiation, symptom monitoring, and follow-up care documented within 3 months of initial symptom identification. RESULTS: Among the 4612 participants, the mean (SD) age at index event was 16.0 (2.3) years, and 3060 were female (66%). Treatment was initiated for nearly two-thirds of adolescents (79% of those with a diagnosis of major depression; n = 023); most received psychotherapy alone or in combination with medications. However, in the 3 months following identification, 36% of adolescents received no treatment (n = 1678), 68% did not have a follow-up symptom assessment (n = 3136), and 19% did not receive any follow-up care (n = 854). Further, 40% of adolescents prescribed antidepressant medication did not have any documentation of follow-up care for 3 months (n = 356). Younger age (ages 15-17 years: odds ratio [OR], 0.78; 95% CI, 0.67-0.92 and ages 18-20 years: OR, 0.83; 95% CI, 0.70-0.99; P = .008), more severe initial symptoms (moderate: OR, 0.99; 95% CI, 0.82-1.21; moderate to severe: OR, 1.46; 95% CI, 1.19-1.80; and severe: OR, 2.14; 95% CI, 1.65-2.79; P < .001), and receiving a diagnosis (major depression/dysthymia: OR, 2.65; 95% CI, 2.20-3.20 and unspecified depression/adjustment disorder: OR, 1.75; 95% CI, 1.43-2.14; P < .001) were significantly associated with treatment initiation. Differences in rates of follow-up care were evident between sites (site 2: OR, 1.77; 95% CI, 1.45-2.16 and site 3: OR, 2.10; 95% CI, 1.72-2.57), suggesting that differences within health systems may also affect care received. CONCLUSIONS AND RELEVANCE: Most adolescents with newly identified depression symptoms received some treatment, usually including psychotherapy, within the first 3 months after identification. However, follow-up care was low and substantial variation existed between sites. These results raise concerns about the quality of care for adolescent depression.

Is It Feasible to Use Electronic Health Records for Quality Measurement of Adolescent Care?

OBJECTIVE: To determine the extent to which it is feasible to implement quality measures on electronic health records (EHRs) as currently implemented in pediatric health centers. METHODS: A survey of information technology professionals at 10 institutions that provide primary care services to adolescents. The survey asked whether data about care was being captured electronically across the nine domains relevant to adolescent well care: Screening, Health Risks, Sexual Health, Diagnosis and History, Laboratory Results, Prescriptions, Referrals, Forms Management, and Patient Demographics. For each domain, we developed a scale of the extent to which the EHR makes quality measurement feasible. RESULTS: Overall feasibility scores varied across centers from 34% to 85% and from 53% to 80% across care domains. One centre reported 100% feasibility for 8 of 10 care domains. CONCLUSIONS: Electronic health records can facilitate quality improvement, but the feasibility of such use depends on the presence, validity, and accessibility of the quality data in the EHR. Even among the largest and most sophisticated pediatric EHR systems, quality of care measurement is not possible yet for all aspects of adolescent well care without manual effort to review and code data. Nevertheless, almost all quality measures were reported to be feasible in some systems.

Health IT-Enabled Care Coordination: A National Survey of Patient-Centered Medical Home Clinicians.

PURPOSE: Health information technology (IT) offers promising tools for improving care coordination. We assessed the feasibility and acceptability of 6 proposed care coordination objectives for stage 3 of the Centers for Medicare and Medicaid Services electronic health record incentive program (Meaningful Use) related to referrals, notification of care from other facilities, patient clinical summaries, and patient dashboards. METHODS: We surveyed physician-owned and hospital/health system-affiliated primary care practices that achieved patient-centered medical home recognition and participated in the Meaningful Use program, and community health clinics with patient-centered medical home recognition (most with certified electronic health record systems). The response rate was 35.1%. We ascertained whether practices had implemented proposed objectives and perceptions of their importance. We analyzed the association of organizational and contextual factors with self-reported use of health IT to support care coordination activities. RESULTS: Although 78% of the 350 respondents viewed timely notification of hospital discharges as very important, only 48.7% used health IT systems to accomplish this task. The activity most frequently supported by health IT was providing clinical summaries to patients, in 76.6% of practices; however, merely 47.7% considered this activity very important. Greater use of health IT to support care coordination activities was positively associated with the presence of a nonclinician responsible for care coordination and the practice's capacity for systematic change. CONCLUSIONS: Even among practices having a strong commitment to the medical home model, the use of health IT to support care coordination objectives is not consistent. Health IT capabilities are not currently aligned with clinicians' priorities. Many practices will need financial and technical assistance for health IT to enhance care coordination.

Addressing electronic clinical information in the construction of quality measures.

Electronic health records (EHR) and registries play a central role in health care and provide access to detailed clinical information at the individual, institutional, and population level. Use of these data for clinical quality/performance improvement and cost management has been a focus of policy initiatives over the past decade. The Children's Health Insurance Program Reauthorization Act of 2009 (CHIPRA)-mandated Pediatric Quality Measurement Program supports development and testing of quality measures for children on the basis of electronic clinical information, including de novo measures and respecification of existing measures designed for other data sources. Drawing on the experience of Centers of Excellence, we review both structural and pragmatic considerations in e-measurement. The presence of primary observations in EHR-derived data make it possible to measure outcomes in ways that are difficult with administrative data alone. However, relevant information may be located in narrative text, making it difficult to interpret. EHR systems are collecting more discrete data, but the structure, semantics, and adoption of data elements vary across vendors and sites. EHR systems also differ in ability to incorporate pediatric concepts such as variable dosing and growth percentiles. This variability complicates quality measurement, as do limitations in established measure formats, such as the Quality Data Model, to e-measurement. Addressing these challenges will require investment by vendors, researchers, and clinicians alike in developing better pediatric content for standard terminologies and data models, encouraging wider adoption of technical standards that support reliable quality measurement, better harmonizing data collection with clinical work flow in EHRs, and better understanding the behavior and potential of e-measures.

Using computer-extracted data from electronic health records to measure the quality of adolescent well-care.

OBJECTIVE: To determine whether quality measures based on computer-extracted EHR data can reproduce findings based on data manually extracted by reviewers. DATA SOURCES: We studied 12 measures of care indicated for adolescent well-care visits for 597 patients in three pediatric health systems. STUDY DESIGN: Observational study. DATA COLLECTION/EXTRACTION METHODS: Manual reviewers collected quality data from the EHR. Site personnel programmed their EHR systems to extract the same data from structured fields in the EHR according to national health IT standards. PRINCIPAL FINDINGS: Overall performance measured via computer-extracted data was 21.9 percent, compared with 53.2 percent for manual data. Agreement measures were high for immunizations. Otherwise, agreement between computer extraction and manual review was modest (Kappa = 0.36) because computer-extracted data frequently missed care events (sensitivity = 39.5 percent). Measure validity varied by health care domain and setting. A limitation of our findings is that we studied only three domains and three sites. CONCLUSIONS: The accuracy of computer-extracted EHR quality reporting depends on the use of structured data fields, with the highest agreement found for measures and in the setting that had the greatest concentration of structured fields. We need to improve documentation of care, data extraction, and adaptation of EHR systems to practice workflow.

Achieving the Promise of Electronic Health Record-enabled Quality Measurement: a Measure Developer's Perspective.

Electronic health record (EHR) systems support local quality improvement efforts by health care organizations and provide the opportunity to address national priority areas for quality measurement, such as specialty care, overuse and efficiency, coordination of care, change over time and patient- reported outcomes (PROs). However, variations in provider workflow and documentation habits, adoption of advanced EHR functions and exchange of interoperable data, and eMeasure specification standards affect the ability to develop and test measures that target these high priority areas for improvement. Measure developers are working with providers, national standards organizations, and other eMeasure stakeholders to address these challenges and support learning health organizations in using EHR-based measurement to improve quality.

Comparison of computerized surveillance and manual chart review for adverse events.

OBJECTIVE: To understand how the source of information affects different adverse event (AE) surveillance methods. DESIGN: Retrospective analysis of inpatient adverse drug events (ADEs) and hospital-associated infections (HAIs) detected by either a computerized surveillance system (CSS) or manual chart review (MCR). MEASUREMENT: Descriptive analysis of events detected using the two methods by type of AE, type of information about the AE, and sources of the information. RESULTS: CSS detected more HAIs than MCR (92% vs 34%); however, a similar number of ADEs was detected by both systems (52% vs 51%). The agreement between systems was greater for HAIs than ADEs (26% vs 3%). The CSS missed events that did not have information in coded format or that were described only in physician narratives. The MCR detected events missed by CSS using information in physician narratives. Discharge summaries were more likely to contain information about AEs than any other type of physician narrative, followed by emergency department reports for HAIs and general consult notes for ADEs. Some ADEs found by MCR were detected by CSS but not verified by a clinician. LIMITATIONS: Inability to distinguish between CSS false positives and suspected AEs for cases in which the clinician did not document their assessment in the CSS. CONCLUSION: The effect that information source has on different surveillance methods depends on the type of AE. Integrating information from physician narratives with CSS using natural language processing would improve the detection of ADEs more than HAIs.

Política e planejamento de saúde / Policy and health planning

Procura-se enfocar alguns aspectos doutrinários e práticos no que concerne ao Sistema Nacional de Saúde, modelo teòrico, a implantaçäo da AIS e seu contexto normativo, as bases doutrinárias e práticas do Planejamento da Saúde e uma abordagem das Perspectivas da Assistência Médico-Hospitalar no Brasil face à situaçäo sanitária vigente .