Health-Related Data Sources Accessible to Health Researchers From the US Government: Mapping Review.

BACKGROUND: Big data from large, government-sponsored surveys and data sets offers researchers opportunities to conduct population-based studies of important health issues in the United States, as well as develop preliminary data to support proposed future work. Yet, navigating these national data sources is challenging. Despite the widespread availability of national data, there is little guidance for researchers on how to access and evaluate the use of these resources. OBJECTIVE: Our aim was to identify and summarize a comprehensive list of federally sponsored, health- and health care-related data sources that are accessible in the public domain in order to facilitate their use by researchers. METHODS: We conducted a systematic mapping review of government sources of health-related data on US populations and with active or recent (previous 10 years) data collection. The key measures were government sponsor, overview and purpose of data, population of interest, sampling design, sample size, data collection methodology, type and description of data, and cost to obtain data. Convergent synthesis was used to aggregate findings. RESULTS: Among 106 unique data sources, 57 met the inclusion criteria. Data sources were classified as survey or assessment data (n=30, 53%), trends data (n=27, 47%), summative processed data (n=27, 47%), primary registry data (n=17, 30%), and evaluative data (n=11, 19%). Most (n=39, 68%) served more than 1 purpose. The population of interest included individuals/patients (n=40, 70%), providers (n=15, 26%), and health care sites and systems (n=14, 25%). The sources collected data on demographic (n=44, 77%) and clinical information (n=35, 61%), health behaviors (n=24, 42%), provider or practice characteristics (n=22, 39%), health care costs (n=17, 30%), and laboratory tests (n=8, 14%). Most (n=43, 75%) offered free data sets. CONCLUSIONS: A broad scope of national health data is accessible to researchers. These data provide insights into important health issues and the nation's health care system while eliminating the burden of primary data collection. Data standardization and uniformity were uncommon across government entities, highlighting a need to improve data consistency. Secondary analyses of national data are a feasible, cost-efficient means to address national health concerns.

Utilization of Medicare's chronic care management services by primary care providers.

BACKGROUND: Medicare billing codes introduced in 2015 reimburses primary care providers for non-face-to-face, chronic care management (CCM) services rendered by clinical staff. PURPOSE: The purpose of this manuscript was to describe provider trends in billed CCM services and identify factors associated with CCM utilization. METHODS: Observational study using Medicare Public Use Files, 2015 to 2018. General, family, geriatric, and internal medicine physicians, nurse practitioners (NPs), and physician assistants (PAs) with billed primary care services were included. Multivariable analyses modeled associations between the CCM services and type of provider, adjusting for year, primary care services, practice, and patient characteristics. FINDINGS: Among 140,465 physicians and 141,118 NPs/PAs, CCM services increased each year, yet remained underutilized: 2% to 7% of physicians and 0.3% to 1.3% of NPs/PAs billed CCM in 2018. Increases in beneficiaries (p < .0001), percentage of dually enrolled (p = .0134), and primary care services (p < .0001) predicted higher CCM utilization. DISCUSSION: CCM utilization reflects practice-based efforts to improve patient access to care by enhancing care delivery.

Faculty education to improve integration of genomics education in nursing curriculum.

Nurses play a crucial role in providing healthcare and need to possess essential knowledge and skill to integrate genomics into practice. Nursing faculty is charged with the education of nurses however, studies show the majority of faculty in the United States is ill-prepared to teach genetics/genomics concepts. Our aim was to increase genetics/genomics content in our college's nursing curriculum by bolstering our faculty knowledge and confidence through the implementation of a face-to-face educational program. Therefore, we launched an intentional, strategic plan in fall 2017. First a comprehensive review of the college's undergraduate nursing curriculum for genetics/genomics content was conducted. Five development workshops on genetics/genomics were delivered over an academic year with knowledge and confidence in genetics/genomics was measured pre- and post-workshops. After the workshops, faculty revised curriculum and added genetics/genomics content to align with the competencies. Participants in the end-of-program survey reported higher confidence levels in all genomics-related tasks and answered more knowledge-based items, as compared to participants in the pre-workshop survey. Changes to the curriculum were made to integrate genetics/genomics in BSN courses and an online genetics course was developed. Genetics/genomics workshops can improve faculty knowledge and confidence and facilitate the integration of genetic/genomic content in undergraduate nursing curricula.

Facility-level program components leading to population impact: a coincidence analysis of obesity treatment options within the Veterans Health Administration.

Obesity is a well-established risk factor for increased morbidity and mortality. Comprehensive lifestyle interventions, pharmacotherapy, and bariatric surgery are three effective treatment approaches for obesity. The Veterans Health Administration (VHA) offers all three domains but in different configurations across medical facilities. Study aim was to explore the relationship between configurations of three types of obesity treatments, context, and population impact across VHA using coincidence analysis. This was a cross-sectional analysis of survey data describing weight management treatment components linked with administrative data to compute population impact for each facility. Coincidence analysis was used to identify combinations of treatment components that led to higher population impact. Facilities with higher impact were in the top two quintiles for (1) reach to eligible patients and (2) weight outcomes. Sixty-nine facilities were included in the analyses. The final model explained 88% (29/33) of the higher-impact facilities with 91% consistency (29/32) and was comprised of five distinct pathways. Each of the five pathways depended on facility complexity-level plus factors from one or more of the three domains of weight management: comprehensive lifestyle interventions, pharmacotherapy, and/or bariatric surgery. Three pathways include components from multiple treatment domains. Combinations of conditions formed "recipes" that lead to higher population impact. Our coincidence analyses highlighted both the importance of local context and how combinations of specific conditions consistently and uniquely distinguished higher impact facilities from lower impact facilities for weight management.

Obesity can contribute to increased rates of ill health and earlier death. Proven treatments for obesity include programs that help people improve lifestyle behaviors (e.g., being physically active), medications, and/or bariatric surgery. In the Veterans Health Administration (VHA), all three types of treatments are offered, but not at every medical center­in practice, individual medical centers offer different combinations of treatment options to their patients. VHA medical centers also have a wide range of population impact. We identified high-impact medical centers (centers with the most patients participating in obesity treatment who would benefit from treatment AND that reported the most weight loss for their patients) and examined which treatment configurations led to better population-level outcomes (i.e., higher population impact). We used a novel analysis approach that allows us to compare combinations of treatment components, instead of analyzing them one-by-one. We found that optimal combinations are context-sensitive and depend on the type of center (e.g., large centers affiliated with a university vs. smaller rural centers). We list five different "recipes" of treatment combinations leading to higher population-level impact. This information can be used by clinical leaders to design treatment programs to maximize benefits for their patients.

Deriving Weight From Big Data: Comparison of Body Weight Measurement-Cleaning Algorithms.

BACKGROUND: Patient body weight is a frequently used measure in biomedical studies, yet there are no standard methods for processing and cleaning weight data. Conflicting documentation on constructing body weight measurements presents challenges for research and program evaluation. OBJECTIVE: In this study, we aim to describe and compare methods for extracting and cleaning weight data from electronic health record databases to develop guidelines for standardized approaches that promote reproducibility. METHODS: We conducted a systematic review of studies published from 2008 to 2018 that used Veterans Health Administration electronic health record weight data and documented the algorithms for constructing patient weight. We applied these algorithms to a cohort of veterans with at least one primary care visit in 2016. The resulting weight measures were compared at the patient and site levels. RESULTS: We identified 496 studies and included 62 (12.5%) that used weight as an outcome. Approximately 48% (27/62) included a replicable algorithm. Algorithms varied from cutoffs of implausible weights to complex models using measures within patients over time. We found differences in the number of weight values after applying the algorithms (71,961/1,175,995, 6.12% to 1,175,177/1,175,995, 99.93% of raw data) but little difference in average weights across methods (93.3, SD 21.0 kg to 94.8, SD 21.8 kg). The percentage of patients with at least 5% weight loss over 1 year ranged from 9.37% (4933/52,642) to 13.99% (3355/23,987). CONCLUSIONS: Contrasting algorithms provide similar results and, in some cases, the results are not different from using raw, unprocessed data despite algorithm complexity. Studies using point estimates of weight may benefit from a simple cleaning rule based on cutoffs of implausible values; however, research questions involving weight trajectories and other, more complex scenarios may benefit from a more nuanced algorithm that considers all available weight data.

Clinical Outcomes of Acute Myocardial Infarction Hospitalizations With Systemic Lupus Erythematosus: An Analysis of Nationwide Readmissions Database.

Hospital readmissions post-acute myocardial infarctions (AMIs) are associated with adverse cardiovascular outcomes and also incur huge healthcare costs. Patients with systemic lupus erythematosus (SLE) are at an increased risk of AMI likely due to multifactorial mechanisms including higher levels of inflammation and accelerated atherosclerosis. We investigated if patients with SLE are at higher risk of hospital readmissions post-AMI compared to the patients without SLE. Furthermore, we sought to assess if inpatient outcomes of AMI in SLE patients are different than AMI without SLE. We conducted a retrospective analysis of adult hospital discharges with the principal diagnosis of AMI using the Nationwide Readmissions Database in 2018. We used the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) to identify comorbid conditions. The primary outcome was all-cause 30-day readmission. Secondary outcomes were cardiac procedures at index hospitalization (percutaneous coronary intervention [PCI] and coronary artery bypass grafting [CABG]), and adverse events at index hospitalization, including inpatient mortality, cardiac arrest, cardiogenic shock, cardiac assist device, coronary artery dissection, acute kidney injury, gastrointestinal bleeding, stroke, post-procedural hemorrhage, sepsis, and hospital costs. Complex samples multivariable logistic regression models were used to determine the association of SLE with outcomes. The patients with AMI and SLE had a higher 30-day readmission rate (15.5% vs 12.5%, aOR = 1.33, CI 1.12-1.57, P = 0.001), and inpatient mortality (aOR = 1.40 CI 1.1-1.79, P = 0.006) compared to the AMI without SLE cohort. The rates of acute kidney injury (aOR = 1.41 CI 1.21-1.64, P < 0.0001) and sepsis (aOR = 1.61 CI 1.16-2.23, P = 0.004) were higher among AMI with SLE group as compared to AMI without SLE group. Within the AMI with SLE cohort, the independent predictors of readmission were diabetes mellitus (aOR = 1.38 CI 0.99-1.91, P = 0.054), peripheral vascular disease (aOR = 2.10 CI 1.22-3.62, P = 0.007), anemia (aOR = 1.50 CI 1.07-2.11, P = 0.019), end-stage renal disease (aOR = 1.91 CI 1.10-3.31, P = 0.021), and congestive heart failure (aOR = 1.55 CI 1.12-2.16, P = 0.009). The length of stay in days during index hospitalization (5.10 vs 4.67) was similar in both cohorts. In the multivariable-adjusted regression model, no statistically significant differences were noted between the AMI with SLE and AMI without SLE cohorts for most inpatient adverse events during the index hospitalization. Patients with AMI and SLE had higher inpatient mortality during the index hospitalization and higher 30-day hospital readmissions compared to AMI patients without SLE. There were no significant differences in most of the other major inpatient outcomes between the 2 cohorts.

Facility-level conditions leading to higher reach: a configurational analysis of national VA weight management programming.

BACKGROUND: While the Veterans Health Administration (VHA) MOVE! weight management program is effective in helping patients lose weight and is available at every VHA medical center across the United States, reaching patients to engage them in treatment remains a challenge. Facility-based MOVE! programs vary in structures, processes of programming, and levels of reach, with no single factor explaining variation in reach. Configurational analysis, based on Boolean algebra and set theory, represents a mathematical approach to data analysis well-suited for discerning how conditions interact and identifying multiple pathways leading to the same outcome. We applied configurational analysis to identify facility-level obesity treatment program arrangements that directly linked to higher reach. METHODS: A national survey was fielded in March 2017 to elicit information about more than 75 different components of obesity treatment programming in all VHA medical centers. This survey data was linked to reach scores available through administrative data. Reach scores were calculated by dividing the total number of Veterans who are candidates for obesity treatment by the number of "new" MOVE! visits in 2017 for each program and then multiplied by 1000. Programs with the top 40 % highest reach scores (n = 51) were compared to those in the lowest 40 % (n = 51). Configurational analysis was applied to identify specific combinations of conditions linked to reach rates. RESULTS: One hundred twenty-seven MOVE! program representatives responded to the survey and had complete reach data. The final solution consisted of 5 distinct pathways comprising combinations of program components related to pharmacotherapy, bariatric surgery, and comprehensive lifestyle intervention; 3 of the 5 pathways depended on the size/complexity of medical center. The 5 pathways explained 78 % (40/51) of the facilities in the higher-reach group with 85 % consistency (40/47). CONCLUSIONS: Specific combinations of facility-level conditions identified through configurational analysis uniquely distinguished facilities with higher reach from those with lower reach. Solutions demonstrated the importance of how local context plus specific program components linked together to account for a key implementation outcome. These findings will guide system recommendations about optimal program structures to maximize reach to patients who would benefit from obesity treatment such as the MOVE!

Construction and Use of Body Weight Measures from Administrative Data in a Large National Health System: A Systematic Review.

OBJECTIVE: Administrative data are increasingly used in research and evaluation yet lack standardized guidelines for constructing measures using these data. Body weight measures from administrative data serve critical functions of monitoring patient health, evaluating interventions, and informing research. This study aimed to describe the algorithms used by researchers to construct and use weight measures. METHODS: A structured, systematic literature review of studies that constructed body weight measures from the Veterans Health Administration was conducted. Key information regarding time frames and time windows of data collection, measure calculations, data cleaning, treatment of missing and outlier weight values, and validation processes was collected. RESULTS: We identified 39 studies out of 492 nonduplicated records for inclusion. Studies parameterized weight outcomes as change in weight from baseline to follow-up (62%), weight trajectory over time (21%), proportion of participants meeting weight threshold (46%), or multiple methods (28%). Most (90%) reported total time in follow-up and number of time points. Fewer reported time windows (54%), outlier values (51%), missing values (34%), or validation strategies (15%). CONCLUSIONS: A high variability in the operationalization of weight measures was found. Improving methods to construct clinical measures will support transparency and replicability in approaches, guide interpretation of findings, and facilitate comparisons across studies.

Nurse staffing and healthcare-associated infections in a national healthcare system that implemented a nurse staffing directive: Multi-level interrupted time series analyses.

BACKGROUND: In 2010, the Veterans Health Administration Office of Nursing Services (VHA ONS) issued a Staffing Methodology (SM) Directive, standardizing the method of determining appropriate nurse staffing for VHA facilities. OBJECTIVES: To assess associations between the Directive, nurse staffing trends, and healthcare-associated infections. RESEARCH DESIGN: We conducted multi-level interrupted time series analyses of nurse staffing trends and the rates of two healthcare-associated infections before and after implementation of the Directive, October 1, 2008 - June 30, 2014. SUBJECTS: Acute care, critical care, mental health acute care, and longterm care nursing units (called Community Living Centers, CLC in VHA) among 285 VHA facilities were included in nurse staffing trends analyses, while acute and critical care units in 123 facilities were used in the analysis of infection rates. MEASURES: Monthly rates were calculated at the facility unit level and included nursing hours per patient day (NHPPD) for all nursing personnel and number of catheter-associated urinary tract infections (CAUTI) and central line-associated bloodstream infections (CLABSI) per 1000 device days. RESULTS: Nursing hours per patient day increased in both time periods. However, the differential change in rate of nursing hours per patient day following implementation of the Directive was not statistically significant. On average, we found a statistically significant decrease of 0.05 unit in the post-Directive central line-associated bloodstream infection rates associated with a unit increase in nursing hours per patient day. CONCLUSIONS: System-wide implementation of Staffing Methodology may be one contributing factor impacting patient outcomes.

A scale to measure nurses' and providers' patient centered care in primary care settings.

OBJECTIVES: 1) Refine pilot scale measuring patients' experiences of outpatient nurses' and providers' care; 2) Determine variance explained by (a) pilot scale items and (b) "Survey of Health Experiences of Patients" (SHEP)/"Consumer Assessment of Health Care Providers and Systems" (CAHPS) scale items. METHODS: Randomly selected Veteran patients with recent visits with primary care outpatient nurses and providers (n = 1192) completed scales: pilot "PCC in Primary Care: Nurses and Providers Scale" and SHEP/CAHPS scale items. Factor analyses conducted using structural equation modeling (SEM), variance measurement using regression strategies. RESULTS: SEM generated scale comprised 17 items in 3 factors; 2 operationalized nurses' care; 1 providers' care. Fit statistics were acceptable. Variance explained for total PCC: nurses = 42%, providers = 56%. Combined pilot and SHEP/CAHPS item analyses yielded similarly structured scale. 70% of provider care variance explained by single item. CONCLUSION: Appraisal of team, value-based care requires accrediting care to the appropriate clinician. The "PCC in Primary Care: Nurses and Providers Scale (PC2:NaPS)" provides a psychometrically sound measure for this purpose. PRACTICE IMPLICATIONS: PC2:NaPS use would improve primary care leaders' and clinicians' analyses of patient centered care and associated outcomes in their settings, and thus enhance success of quality improvement and organizational projects.

Trends in primary care encounters across professional roles in PCMH.

OBJECTIVES: Team-based care models, including the patient-centered medical home (PCMH), are increasingly promoted to improve the delivery of primary care. However, evaluation measures are often reported at a clinic or primary care provider (PCP) level, creating challenges in describing and analyzing the use and impact of non-PCP clinician team members. Thus, we aimed to measure clinician-specific care delivery trends and determine whether trends were responsive to systemwide PCMH implementation. STUDY DESIGN: Interrupted time-series analysis of 57 million primary care encounters among 5 million veterans at 764 Veterans Health Administration primary care clinics from 2009 to 2013. METHODS: Retrospective data identified patient encounters attributable to 12 types of clinicians, yielding an encounters-by-clinician metric. Negative binomial regression modeled the monthly clinic-level rates of encounters for each type of clinician, before and during PCMH implementation. RESULTS: Over 5 years, the percentage of encounters by non-PCP clinicians increased from 29% to 35%. Monthly encounter rates for nurses and social workers significantly increased by 0.5% and 1.3%, respectively, after the introduction of PCMH, whereas PCP encounter rates significantly decreased over time. Encounter trends for pharmacists, nutritionists, and behavioral health clinicians did not significantly change. CONCLUSIONS: This study demonstrated the feasibility of capturing care delivered by a full complement of team members using routinely collected data. Findings suggest that the proportions of care delivered by non-PCP clinicians were sensitive to a change in care delivery model. As team-based care models expand, availability and use of metrics that account for care by all team members are critical for inferring clinician-related effects on outcomes.

Anesthesia Care Practice Models in the Veterans Health Administration.

Although the VHA primarily relies on teams for anesthesia care, unsupervised certified registered nurse anesthetists also are used to meet veterans' surgical care needs.

Factors Associated With the Implementation of a Nurse Staffing Directive.

BACKGROUND: In 2010, the Veterans Health Administration issued a Staffing Methodology (SM) Directive to provide a standardized, data-driven method for determining appropriate inpatient nurse staffing. OBJECTIVE: We aimed to describe experiences and factors related to SM implementation. METHODS: We administered a Web-based survey to chief nurse executives to obtain their implementation experiences. Structural, process, and outcome factors and barriers associated with self-reported implementation success were identified. RESULTS: Respondents representing 104 of 117 facilities participated. Almost all facilities (96%) had completed at least 1 cycle of SM, yet only half (52%) rated their implementation highly successful. Early implementation date, higher levels of leadership confidence in SM, and higher frequency in which nursing staff think in terms of hours per patient day were associated with higher SM implementation success. Time, staff training and educational needs, and engagement were common barriers. DISCUSSION: Understanding factors that influence successful implementation of staffing policies is important to ensuring safe staffing.

Do Patient-Centered Medical Home Access and Care Coordination Measures Reflect the Contribution of All Team Members? A Systematic Review.

Patient-Centered Medical Home (PCMH) evaluations have primarily focused on primary care providers and not on the primary care team. This systematic literature review examined the extent to which access and care coordination measures in PCMH reflect the involvement of associate care providers (ACPs), which include registered and licensed practical nurses, nursing and medical assistants, clerks, pharmacists, social workers, and dietitians. Among 42 studies, few measures specified ACP roles or linked ACP care to outcomes. Increasing attention on team-based care emphasizes a vital need to reframe measures within a team context.

Factors that affect implementation of a nurse staffing directive: results from a qualitative multi-case evaluation.

AIM/S: To assess implementation of the Veterans Health Administration staffing methodology directive. BACKGROUND: In 2010 the Veterans Health Administration promulgated a staffing methodology directive for inpatient nursing units to address staffing and budget forecasting. DESIGN: A qualitative multi-case evaluation approach assessed staffing methodology implementation. METHODS: Semi-structured telephone interviews were conducted from March - June 2014 with Nurse Executives and their teams at 21 facilities. Interviews focused on the budgeting process, implementation experiences, use of data, leadership support, and training. An implementation score was created for each facility using a 4-point rating scale. The scores were used to select three facilities (low, medium and high implementation) for more detailed case studies. RESULTS/FINDINGS: After analysing interview summaries, the evaluation team developed a four domain scoring structure: (1) integration of staffing methodology into budget development; (2) implementation of the Directive elements; (3) engagement of leadership and staff; and (4) use of data to support the staffing methodology process. The high implementation facility had leadership understanding and endorsement of staffing methodology, confidence in and ability to work with data, and integration of staffing methodology results into the budgeting process. The low implementation facility reported poor leadership engagement and little understanding of data sources and interpretation. CONCLUSION: Implementation varies widely across facilities. Implementing staffing methodology in facilities with complex and changing staffing needs requires substantial commitment at all organizational levels especially for facilities that have traditionally relied on historical levels to budget for staffing.

A Comparison of Health Plan- and Provider-Delivered Chronic Care Management Models on Patient Clinical Outcomes.

BACKGROUND: The real world implementation of chronic care management model varies greatly. One aspect of this variation is the delivery mode. Two contrasting strategies include provider-delivered care management (PDCM) and health plan-delivered care management (HPDCM). OBJECTIVE: We aimed to compare the effectiveness of PDCM vs. HPDCM on improving clinical outcomes for patients with chronic diseases. DESIGN: We used a quasi-experimental two-group pre-post design using the difference-in-differences method. PATIENTS: Commercially insured patients, with any of the five chronic diseases-congestive heart failure, chronic obstructive pulmonary disease, coronary heart disease, diabetes, or asthma, who were outreached to and engaged in either PDCM or HPDCM were included in the study. MAIN MEASURES: Outreached patients were those who received an attempted or actual contact for enrollment in care management; and engaged patients were those who had one or more care management sessions/encounters with a care manager. Effectiveness measures included blood pressure, low density lipoprotein (LDL), weight loss, and hemoglobin A1c (for diabetic patients only). Primary endpoints were evaluated in the first year of follow-up. KEY RESULTS: A total of 4,000 patients were clustered in 165 practices (31 in PDCM and 134 in HPDCM). The PDCM approach demonstrated a statistically significant improvement in the proportion of outreached patients whose LDL was under control: the proportion of patients with LDL < 100 mg/dL increased by 3 % for the PDCM group (95 % CI: 1 % to 6 %) and 1 % for the HPDCM group (95 % CI: -2 % to 5 %). However, the 2 % difference in these improvements was not statistically significant (95 % CI: -2 % to 6 %). The HPDCM approach showed 3 % [95 % CI: 2 % to 6 %] improvement in overall diabetes care among outreached patients and significant reduction in obesity rates compared to PDCM (4 %, 95 % CI: 0.3 % to 8 %). CONCLUSIONS: Both care management delivery modes may be viable options for improving care for patients with chronic diseases. In this commercially insured population, neither PDCM nor HPDCM resulted in substantial improvement in patients' clinical indicators in the first year. Different care management strategies within the provider-delivered programs need further investigation.

Characteristics of patients with diabetes who accept referrals for care management services.

INTRODUCTION: Patients with chronic conditions can improve their health through participation in self-care programs. However, awareness of and enrollment in these programs are generally low. OBJECTIVE: We sought to identify factors influencing patients' receptiveness to a referral for programs and services supporting chronic disease management. METHODS: We analyzed data from 541 high-risk diabetic patients who completed an assessment between 2010 and 2013 from a computer-based, nurse-led Navigator referral program within a large primary care clinic. We compared patients who accepted a referral to those who declined. RESULTS: A total of 318 patients (75%) accepted 583 referrals, of which 52% were for self-care programs. Patients who accepted a referral had more primary care visits in the previous year, were more likely to be enrolled in another program, expressed more interest in using the phone and family or friends for support, and were more likely to report recent pain than those who declined a referral. DISCUSSION: Understanding what factors influence patients' decisions to consider and participate in self-care programs has important implications for program design and development of strategies to connect patients to programs. This work informs outreach efforts to identify and engage patients who are likely to benefit from self-care activities.

Evaluating the Veterans Health Administration's Staffing Methodology Model: A Reliable Approach.

All Veterans Health Administration facilities have been mandated to use a standardized method of determining appropriate direct-care staffing by nursing personnel. A multi-step process was designed to lead to projection of full-time equivalent employees required for safe and effective care across all inpatient units. These projections were intended to develop appropriate budgets for each facility. While staffing levels can be increased, even in facilities subject to budget and personnel caps, doing so requires considerable commitment at all levels of the facility. This commitment must come from front-line nursing personnel to senior leadership, not only in nursing and patient care services, but throughout the hospital. Learning to interpret and rely on data requires a considerable shift in thinking for many facilities, which have relied on historical levels to budget for staffing, but which does not take into account the dynamic character of nursing units and patient need.

Comparison of provider and plan-based targeting strategies for disease management.

OBJECTIVES: We aimed to describe and contrast the targeting methods and engagement outcomes for health plan-delivered disease management with those of a provider-delivered care management program. STUDY DESIGN: Health plan epidemiologists partnered with university health services researchers to conduct a quasi-experimental, mixed-methods study of a 2-year pilot. We used semi-structured interviews to assess the characteristics of program-targeting strategies, and calculated target and engagement rates from clinical encounter data. METHODS: Five physician organizations (POs) with 51 participating practices implemented care management. Health plan member lists were sent monthly to the practices to accept patients, and then the practices sent back data reports regarding targeting and engagement in care management. Among patients accepted by the POs, we compared those who were targeted and engaged by POs with those who met health plan targeting criteria. RESULTS: The health plan's targeting process combined claims algorithms and employer group preferences to identify candidates for disease management; on the other hand, several different factors influenced PO practices' targeting approaches, including clinical and personal knowledge of the patients, health assessment information, and availability of disease-relevant programs. Practices targeted a higher percentage of patients for care management than the health plan (38% vs 16%), where only 7% of these patients met the targeting criteria of both. Practices engaged a higher percentage of their targeted patients than the health plan (50% vs 13%). CONCLUSIONS: The health plan's claims-driven targeting approach and the clinically based strategies of practices both provide advantages; an optimal model may be to combine the strengths of each approach to maximize benefits in care management.