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.

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.

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.

Family history, diabetes, and other demographic and risk factors among participants of the National Health and Nutrition Examination Survey 1999-2002.

INTRODUCTION: Family history of diabetes has been recognized as an important risk factor of the disease. Family medical history represents valuable genomic information because it characterizes the combined interactions between environmental, behavioral, and genetic factors. This study examined the strength and effect of having a family history of diabetes on the prevalence of self-reported, previously diagnosed diabetes among adult participants of the National Health and Nutrition Examination Survey 199-2002. METHODS: The study population included data from 10,283 participants aged 20 years and older. Gender, age, race/ethnicity, poverty income ratio, education level, body mass index, and family history of diabetes were examined in relation to diabetes status. Diabetes prevalence estimates and odds ratios of diabetes were calculated based on family history and other factors. RESULTS: The prevalence of diabetes among individuals who have a first-degree relative with diabetes (14.3%) was significantly higher than that of individuals without a family history (3.2%), corresponding to a crude odds ratio of five. Both prevalence and odds ratio estimates significantly increased with the number of relatives affected with diabetes. Family history was also associated with several demographic and risk factors. CONCLUSION: Family history of diabetes was shown to be a significant predictor of diabetes prevalence in the adult U.S. population. We advocate the inclusion of family history assessment in public health prevention and screening programs as an inexpensive and valuable source of genomic information and measure of diabetes risk.