Facility Characteristics Associated with the Use of Electronic Information Systems for Medical Care

Facility characteristics associated with the use of electronic information systems for clinical decision support: Evidence from the 2004 National Nursing Home Survey Sophia Chan, PhD, MPH June 4th, 2007 AcademyHealth Annual Research Meeting Research Question What characteristics of a nursing home are associated with the use of electronic information systems (EIS) for clinical decision support? Significance of Research Question • Using EIS to enhance medical care quality - a policy priority (Executive Order 13335) • NHs lag behind other health organizations in EIS adoption • Little is known about facility characteristics related to the use of EIS for clinical care support in NHs • Descriptive data from a study on home health and hospice agencies: EIS use = f (patient load + number of special services offered to patients) Hypotheses 1. Use of EIS for medical care = f (occupancy rate of an NH) 2. Use of EIS for medical care = f (number of special services offered by an NH) Study Design: Data • 2004 National Nursing Home Survey (NNHS) facility-level data (N = 1,174) • Advantages of 2004 NNHS: – Most current data – Nationally representative – High response rate (81%) – CAPI enhances data quality – Most knowledgeable NH staff as respondents Study Design: EIS Question • “Does this facility use an electronic information system for any of the following tasks?” • Response categories (recoded as dichotomous): – Physician orders – Drug dispensing – Laboratory / procedures information – Patient medical records – Medication administration information Study Design: Facility variables • • • • • • • • • • • • Occupancy rate (Hypothesis # 1) Number of special services (Hypothesis # 2) Census region MSA status Chain ownership Profit status Accreditations by JCAHO, CARF, or CCAC Number of special programs Number of special beds Number of years as administrator Administrator’s highest degree Primary source of payment (Medicare, Medicaid, other) Analysis Strategy • Bivariate analyses: Chi-square tests • • • • Multivariate analyses: Logistic regression Software: SAS 9.1 Missing data, DK, and refusals excluded Weighted estimates Results: Using EIS for… Drug dispensing (base = no) More likely if: • More special services (OR = 1.23, 95% CI = 1.13, 1.34) (Hypothesis 2 supported) • West (vs. South) (OR = 1.63, 95% CI = 1.10, 2.43) Less likely if: • Northeast (vs. South) (OR = 0.45, 95% CI = 0.31, 0.67) • 5 to 9 years as administrator (vs. 20+ years) (OR = 0.54, 95% CI = 0.37, 0.78) Results: Using EIS for… Physician orders (base = no) More likely if: • 95%+ occupancy (vs. < 70%) (OR = 1.27, 95% CI = 1.03, 1.64) • More special services (OR = 1.16, 95% CI = 1.06, 1.28) (Hypothesis 2 supported) • West (vs. South) (OR = 1.91, 95% CI = 1.28, 2.85) • Micropolitan (vs. rural) (OR = 1.39, 95% CI = 1.14, 1.74) Results: Using EIS for… Physician orders (base = no) (con’t) Less likely if: • 70 to 79% occupancy rate (vs. < 70%) (OR = 0.49, 95% CI = 0.28, 0.84) (Hypothesis 1 partly supported) • 5 to 9 years as administrator (vs. 20+ years) (OR = 0.45, 95% CI = 0.30, 0.65) • Midwest (vs. South) (OR = 0.59, 95% CI = 0.43, 0.80) • Northeast (vs. South) (OR = 0.29, 95% CI = 0.19, 0.46) • Metropolitan (vs. rural) (OR = 0.69, 95% CI = 0.50, 0.97) Results: Using EIS for… Medication administration records (base = no) More likely if: • More special services (OR = 1.21, 95% CI = 1.10, 1.32) (Hypothesis 2 supported) • West (vs. South) (OR = 1.85, 95% CI = 1.24, 2.75) Results: Using EIS for… Medication administration records (base = no) (con’t) Less likely if: • 70 to 79% occupancy (vs. < 70%) (OR = 0.41, 95% CI = 0.23, 0.72) (Hypothesis 1 rejected) • Metropolitan (vs. rural) (OR = 0.70, 95% CI = 0.50, 0.97) • Northeast (vs. South) (OR = 0.43, 95% CI = 0.27, 0.66) • < 5 years as administrator (vs. 20+) (OR = 0.49, 95% CI = 0.33, 0.71) Results: Using EIS for… Lab orders or procedures (base = no) More likely if: • More special services (OR = 1.21, 95% CI = 1.10, 1.32) (Hypothesis 2 supported) • Administrator has an advanced degree (vs. HS only) (OR = 1.86, 95% CI = 1.11, 3.11) • 40%+ residents with non-Medicare/Medicaid payment sources (vs. < 20%) (OR = 0.71, 95% CI = 0.61, 0.97) • Accredited by JCAHO, CCAC, CARF (vs. not) (OR = 1.53, 95% CI = 1.04, 2.26) Results: Using EIS for… Lab orders or procedures (base = no) (con’t) Less likely if: • 5 to 9 years as administrator (vs. 20+ years) (OR = 0.50, 95% CI = 0.34, 0.73) Results: Using EIS for… Patient medical records (base = no) More likely if: • More special services (OR = 1.11, 95% CI = 1.02, 1.22) (Hypothesis 2 supported) • West (vs. South) (OR = 2.06, 95% CI = 1.38, 3.06) • 20 to 39% of residents with non-Medicare/Medicaid payment sources (vs. < 20%) (OR = 1.79, 95% CI = 1.34, 2.40) Results: Using EIS for… Patient medical records (base = no) (con’t) Less likely if: • Northeast (vs. South) (OR = 0.48, 95% CI = 0.31, 0.74) • Metropolitan (vs. rural) (OR = 0.76, 95% CI = 0.69, 0.99) Summary of Findings • Hypothesis 1 is partly supported: – EIS use = f (Occupancy rate) only for • physician orders • occupancy rate ≥ 95% • Hypothesis 2 is generally supported: – EIS use = f (Number of special services) • Also, EIS use= f ( Census region, MSA status, number of years as administrator, administrator’s education, % of residents with non-Medicare/Medicaid payment sources, accredited by independent accreditation organizations) Strengths and Limitations of Study • Strengths: – First study of its kind – Most current and nationally representative data on nursing homes • Limitations: – Data based on self-report – EIS information captured by one question only Implications • Adoption of EIS in NHs is piecemeal and may be taskrelated • For health policy: – Need support from public sector to improve access to capital, provide technical guidance, and correct misalignment of incentives. • For quality of care: – NHs need clinical evidence from large-scale/national studies on benefits of EIS on quality of care