# Presentation: 225609

## The Relation of Crowded Working Conditions to Patient Safety in Hospitals 

- AHRQ Conference: *Patient Safety and Health Information Technology:  Making the Health Care System Safer through Implementation and Innovation*
- June 6, 2005

**Joel S. Weissman, Ph.D.**
**MGH/Harvard Institute for Health Policy**

## Study Personnel

**MGH**
- Joel S. Weissman, Ph.D. (PI)
- Eran Bendavid, MD
- Joann David-Kasdan, RN (Central Study RN)
- Jenya Kaganovich, Ph.D
- Peter Sprivulis, MD
**BWH**
- Jeff Rothschild, MD (Co-PI)
- Fran Cook, Sc.D.
- David Bates, MD

**LDS – Dept of Informatics**
- Scott Evans, Ph.D.  (PI-Aim2)
- Peter Haug, Ph.D.
- Jim Lloyd

**Vanderbilt Univ**
- Harvey Murff, MD

**NWH**
- Les Slebovitz, MD

## What I will Cover

- Overview of the federal grant
- Conceptual Model
- Data Collection Goals
- Preliminary Results
- Conclusions and Next Steps

## Relation of Hospital Workload to Patient Safety

- Federal grant that is supported by the Agency for Healthcare Research and Quality (AHRQ)
- Partnership between Partners and LDS (in Utah)
- The project has two major aims:
- Determine relationship between hospital crowding and adverse events (AEs)
- Develop new methods to monitor and track adverse events using electronic medical records

## “...motives and actions very often originate not from within but from the situation in which individuals find themselves”

- Karl Mannheim
- Philosopher and sociologist

**Notes:**
Quote from Friedson Profession of Medicine, p. 85.  Karl Mannheim was the last and is the least appreciated of the founding fathers of classical sociology. As one of the founders of the sociology of knowledge, Mannheim developed a sophisticated analysis of the role of intellectuals and of the role and history of ideology. Although he wrote widely on sociological and political topics he returned again and again to the problems of knowledge and of ideology. 

## Uncrowded State

**Activity**

**Usual Processes of Care**

**Usual or Desirable  Outcomes**

## What Happens Under Crowded Conditions?

## Crowded State

**Capacity Limits**

**Inadequate  Responses by Staff & Other Systems**

**Increases in  Patient Workload/**
**Activity**

**Increase in Adverse Events??**

**Over-Crowding**

**Process of Care**

## Methods - Highlights

- 4 hospitals
- 2 major teaching hospitals
- 2 community hospitals
- ~10,000 chart reviews
- Patients hospitalized during 2000-2001
- Collected data on workload for each calendar day

- Study Question:  How does the daily rate of adverse events vary by workload, controlling for patient complexity and nurse staffing?

## Data Collection Goals

- The project has two major data collection goals:
- Chart abstraction
- Collection of workload information from study hospitals
- Primary purpose of chart abstraction is to detect Adverse Events (AEs) in the charts of hospitalized patients and describe their severity and preventability

## Errors versus Adverse Events

**Errors**
**&**
**Near Misses**

**Adverse**
**Events**

**Notes:**
Are a subset of medical “outcomes”
An injury caused by medical management, rather than the underlying condition of the patient. (IOM) 
A negative consequence of care that results in unintended injury or illness, which may or may not have been preventable (NQF, 2002)
Are a close synonym of “complications”

Potential AEs -- might have been intercepted or patient simply did not develop AE (e.g., due to drug mistake)
Non-preventable AEs - controversial.  Some purists say that all AEs are preventable if they result from medical management;  otherwise they are the result of the natural progression of the disease.  

## Why Adverse Events and not Errors?

- Most errors are not reported in charts
- Many deviations from procedure are not viewed as errors by staff
- Many errors are not known without a “root cause analysis”
- Many adverse events, while not errors, are still cause for review since they are poor outcomes that therefore have implications for overall quality of care

- “The cause is hidden. The effect is visible to all.”  -Ovid

## Computerized Adverse Event Detection Tool (CADeT)

- One strength of the study was pre-defined AEs
- The tool was developed with four main considerations in mind:
- It should provide the pre-defined criteria for abstracters
- It should be user-friendly and easy to navigate
- It should allow reliable detection of AEs
- Each review should take a short time to complete

## Primary Measures of Crowding/ Workload & Patient Complexity

- Census/Occupancy rates
- Throughput (admissions to and discharges from the Unit)
- Diversion
- Average severity/complexity
- Average nursing acuity

**Each of the following may vary from day to day, and can be measured at various levels of aggregation:**

## Primary Measures of Staffing

- Total RN staff
- Total non-RN staff
- Ratio of RNs / non-RNs
- Variance between actual and “planned”
- Patients per nurse

**Each of the following may vary from day to day, and can be measured at various levels of aggregation:**

## Analysis

- Basic Model:
*Prob (AE) = f (Patient vars, Day vars, Grouped data: superunit level, Grouped data: hospital level)*
- Patient-Day analysis
- Poisson regression
- Dep Var =  = 0, 1, 2, or 3 AEs on any given day for a given patient
- Control for clustering within admission
- Day analysis
- Aggregated patient-level characteristics
- N = 365 days
- Dep Var = Rate of AEs

## Results and Conclusions

- Forthcoming

## Why is the Study Important?

- We focus on system explanations, NOT individual fault or blame
- There is concern that hospitals are becoming over-crowded and under-staffed, but we can NOT determine optimum nurse staffing levels from this particular study
- In Aim 2: We will be able to identify new, inexpensive methods for tracking AEs.

## End of presentations