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STUDY OF CORRELATION OF HOSPITAL
NURSE STAFFING AND OUTCOMES
Tentative Study Approach
March 25, 2014
Overview
• Reality Check
• Data availability on staffing and outcomes
• Data about confounding factors
• Chronology of staffing and outcome data
• Tentative Staged Approach
• Short Term: Variation in staffing
• Short Term: Association between available staffing and outcome
data
• Mid-term: Analysis of additional staffing data to overcome
shortcomings
• Potentially longer-term: Analysis w/confounding variables
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Data Availability: Staffing Data
• Staffing plan data available in Spring 2014 (for CY/FY
2014)
• Actual staffing data in June 2014 for the first quarter
• Data will be at the unit level (categories from the Labor
Management Institute)
• Data will not be:
• Specific to shifts, including potential high-patient load periods
• Distinguish between various levels of qualifications/experience for
nurses
• Break out “other assistive personnel” into the granularity favored in
workgroup discussions
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Data Availability: Confounding Factors
• Case-mix adjusters are built-in to standard groupers, such
as APR-DRGs
• Patient complexity can be ascertained through analysis of
the diagnostic codes
• Patient demographics may further help to make valid
comparisons (age, gender, zip code, etc.)
• Limits:
• True acuity systems do not exist uniformly and they vary in their
robustness
• Data on factors such as unit activity, culture/environment, nurse
autonomy are not readily available and challenging to collect
across the entire hospital industry
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Data Availability: Outcome Data
• About a dozen outcome measures exists that are nurse-
sensitive
• Some concordance (not perfect) across various measure
owners: infection, falls, pressure ulcers
• Many measures are claims-based
• Data are not:
• Specific to units or shifts, but measure an overall hospital’s
performance
• Fully reflective of the range of nurse staffing performance
• Real-time – because many are based on claims data, they come
with a lag
• Characterized by large number of observations – short periods of
data will now allow identification of associations
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Chronology of Data
• Data on patient outcomes, hospital claims and nurse
staffing are poorly aligned over time
• Association between variables are only meaningful if
relationships are sticky over time
-- 2012 -Jan
Jan
Claims Data
-- 2013 -Dec
Oucome Data
-- 2014 --
Dec
Jan Mar
Staffing Data
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TENTATIVE STUDY
APPROACH
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MDH Nurse Staffing Levels and Patient
Outcomes - Study Timeline
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Tentative Study Approach Overview
• Short term (April – June 2014)
• Part 1: What can available data tell us about hospital nurse
staffing variation in Minnesota across peer hospitals?
• Part 2: To what extent can variation in this preliminary
staffing data tell us about the relationship between staffing
and patient outcomes?
• Medium term (July – October 2014)
• What additional analysis and data collection is necessary to
• strengthen the preliminary analysis and
• overcome some of the limitations associated with available data?
• Potential long term? (2015 and beyond)
• How to obtain data to address confounders?
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SHORT-TERM APPROACH
April – June 2014
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Part 1: Preliminary Description of Nurse
Staffing Variation in Minnesota
• MHA Nurse Staffing Plans (April 2014)
• Inpatient nurse staff only
• Non-managerial staff direct patient care
• No skill mix information
• Staffing by unit type: ICU and non-ICU
• Hospital Annual Report (HAR) vintage FY
2012 and historical
• All hospital nurse staff (inpatient and outpatient)
• Skill mix: RN, LPN, Nursing Assistant/Aide
• Revenue and utilization data
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Part 2: Preliminary Analysis of the Relationship
Between Staffing and Patient Outcomes Data
• HAR (most recent FY 2012)
• Patient length of stay (inpatient or adjusted)
• Hospital Adverse Health Events (most recent FY
2013)
• Patient fall with serious injury or death
• All patient falls?
• Stage 3 or 4 pressure ulcers (serious bedsores)
• Hospital Discharge Data (most recent CY 2012) for
National Quality Forum Nurse Sensitive Core
Measures
• Decubitus/pressure ulcer
• Failure to rescue
• Infections due to medical care (ICU only)
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Nurse Sensitive Patient Outcome Measures
Select Measure
Name
Death in low mortality
DRG
Decubitus/ pressure
ulcer
Failure to rescue
Postoperative PE or
DVT (PSI 12)
Infection due to
medical care
AHRQ Patient
Safety Indicators
NQF Core
Measures
ANA National
Database of NQI
X
Potential Data
Sources (Vintage)
HDD (2012)
X
X
X
HDD (2012)/ HAE
(2013)
X
X
X
HDD (2012)
X
X
HDD (2012)
Only ICU
X
HDD (2012)
Patient falls
prevalence
X
HAE (2013)
Patient falls with injury
X
HAE (2013)
Restraint prevalence
X
Source: Adapted from Savitz et al., Quality indicators sensitive to nurse staffing in acute care settings. In: Hendrickson K, Battles
JB, Marks ES, et al, eds. Advances in Patient Safety: From Research to Implementation. Rockville, MD: Agency for Healthcare
Research and Quality; 2005:375–385
Note: HDD is Hospital Administrative Discharge Data; HAE is Hospital Adverse Events
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Analysis of Nurse Staffing Options
• Option 1: Nurse staffing for entire hospital (both
inpatient and outpatient) using HAR data
• Option 2: First Quarter 2014 MHA staffing plan
data (no skill mix), unit type (level of granularity at
this point is unclear)
• Option 3: Retrospective years using MHA staffing
plan data as a benchmark adjusting by HAR skill
mix data
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MEDIUM TERM APPROACH
July – October 2014
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Part 1: Description of Nurse Staffing
Variation in Minnesota
• MHA Nurse Staffing Report (July
2014)
• Inpatient nurse staff only
• Non-managerial staff direct patient care
• Staffing by unit type: ICU, non-ICU types
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Part 2: Nurse Staffing & Patient
Outcomes: Important Related Factors
Patient factors:
Hospital Discharge Data (CY 2013 and prior)
• Age, sex, patient acuity (APR-DRG severity score,
comorbidities) primary payer, ZIP
Hospital factors:
Hospital Discharge Data (CY 2013 and prior)
• Case mix index for all hospitals
HAR (FY 2012) and Other Data Sources:
• Teaching status, urban-rural location, critical access,
bed size, ownership, total admissions, and payer mix
• Medicare case mix index for PPS hospitals (FY 2013)
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Part 2: Nurse Staffing & Patient Outcomes
Analytical Options
• Option 1: Analyze only concurrent data
• Nurse Staffing – MHA Nurse Staffing Reports 1st Qrt. 2014
• Patient Outcomes – Collect HDD data from hospitals 1st Qrt. 2014
• Other Factors – HDD data from hospitals 1st Qrt. 2014
• Option 2: Analyze historical data
• Nurse Staffing – MHA Nurse Staffing Reports 1st Qrt. 2014
• Patient Outcomes – Historical HAE, HDD (FY 2010-2013)
• Other Factors – Historical HDD (CY 2010-2013), other sources
• Option 3: Analyze historical data using additional data
• Nurse Staffing – Collect historical data w/ skill mix (FY 2010-2013)
• Patient Outcomes – Historical HAE, HDD (FY 2010-2013)
• Other Factors –HDD, other sources (FY 2010-2013)
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POTENTIAL LONG-TERM
APPROACH
Post January 2015
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More Robust Studies Would Require
Currently Unavailable Data
• Nurse experience
• Organizational culture
• Physical space and layout
• Available technologies (both patient care and
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administrative)
Organizational structure and hierarchy
Administrative practices, including use of float nurses
Available supports and specialties (such as wound care
consults, therapy teams, transport teams)
Use of rapid response teams
Work environment
Separation of staffing in non-ICU hospital unit types
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DISCUSSION
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Questions for Discussion
• Where to focus on outcome measures?
• How to arrive at alignment of data on outcomes, staffing
and claims over time?
• Where should MDH focus efforts in further data collection,
if at all?
• What preliminary and final analyses should MDH
consider?
• Other comments?
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Contact Information
• Nate Hierlmaier
(651)201-3541
[email protected]
• Stefan Gildemeister
(651) 201-3550
[email protected]
www.health.state.mn.us/healtheconomics
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