right-sizing hospital units and nurse staffing -...

Eugene Litvak, Ph.D.

Institute for Healthcare Optimization

www.ihoptimize.org

Pat Rutherford, RN, MS

Institute for the Healthcare Improvement

April 6, 2016

Right-sizing Hospital Units

and Nurse Staffing

This presenter has nothing

to disclose.

http://www.ihoptimize.org/

Session Description

Characteristics such as patient-per-nurse staffing ratio and nurse hours per patient day has been demonstrated to be a major, determinant of quality of care and patient safety. They dramatically affect hospital-acquired infections, patient experience, mortality, readmissions, etc.

Operations Management tools such as Queuing Models make it possible for organizations to predict and manage the variability of random patient demand, generally associated with clinically-driven patient needs. These tools allow managers to make informed judgments on the resource capacity needed to serve variable demand flows, such as in different medicine services. This session will explore how to properly allocate the number of beds across units and how to optimize staffing to achieve the desired level of service to improve operational, financial, and clinical performance.

Session Objectives

After this session, participants will be able to:

Describe how the understanding of queuing theory

enables operation management teams to plan for

staffing and bed/unit needs

Identify the importance of “right-sizing” hospital units

and nurse staffing levels to ensure patient safety

3

4

Nurse Staffing….

Why should we care about it

and what should we do about it?

Staffing Ratios and Adverse Events

Recent studies show that lower nurse-to patient

staffing ratios are associated with higher rates

of adverse events, including:

• Nosocomial infections (e.g. UTI, post-op infection, and pneumonia)

• Pressure ulcers

• Cardiac and respiratory failure and “failure to rescue”

• Increased length of stay

Aiken et al, 2002; Needleman et al, 2002; Seago, 2001 and Kovner, 2002

Nurse Staffing and

Patient Outcomes in Hospitals

This study found statistically significant relationships between nursestaffing variables and the following patient outcomes in acute care :

• Medical Patients: urinary tract infection, pneumonia, shock,upper gastrointestinal bleeding, length of stay

• Patients Undergoing Major Surgery: urinary tract infection, pneumonia, failure to rescue (defined as the death rate among patients with sepsis, pneumonia, shock, upper gastrointestinal bleeding, or deep vein thrombosis)

High RN staffing associated with 3-12% decrease in likelihood of events, high total nursing staffing associated with 2-25% decrease

No effects of staffing on mortality in either medical or surgical patients

Main Analyses involved 1997 discharges from 799 hospitals across 11 states (AZ, CA, MA, MD, MI, NV, NY, SC, VA, WI, WV

Needleman, Buerhaus, et al. (2001). Nurse Staffing and Patient Outcomes in Hospitals. Report available at www.hrsa.gov/dn

http://www.hrsa.gov/dn

Hospital Nurse Staffing, and Patient Mortality,

Nurse Burnout and Job Dissatisfaction

University of Pennsylvania study: 10,000 nurses and 230,000

patients from 168 hospitals in Pennsylvania from 1998-1999.

For each additional patient assigned to a nurse findings showed:

• 30-day patient mortality increases by 7%

• failure-to rescue rates increase by 7%

• the odds of nursing job dissatisfaction increase by 15%

• the odds of nurse burnout increase by 23%

If nurses had eight patients instead of four, their patients had a

31% higher chance of dying within 30 days of admission.

43% of the nurses surveyed were burned out and emotionally

exhausted.

Aiken LH, Clarke SP, Sloane DM Sochalaski J, Silber JH (2002) Hospital nurse

staffing, and patient mortality, nurse burnout and job dissatisfaction JAMA, 288(16)

1987-1993).

Summary of Issues

The issues of acuity and the need for more flexibility in determining staffing levels need to be considered

The idea of mandating more nurses at the bedside won’t necessarily make that a reality

Complexity of variables which effect the nurse-to-patient ratios make it difficult for researchers to the determine optimal nursing levels

Increasing staffing ratios without other improvements in the work environment and in processes of care is unlikely to dramatically improve the quality and safety of patient care

Center for Health Outcomes and Policy Research Sean Clarke, RN, PhD,

CRNP, CS, Assistant Professor, School of Nursing, Associate Director

Nurse Staffing and Hospital Mortality

In this retrospective observational study, staffing of RNs below target levels was associated with increased mortality, which reinforces the need to match staffing with patients' needs for nursing care

For hospitals that generally succeed in maintaining RN staffing levels that are consistent with each patient's requirements for nursing care, this study underscores the importance of flexible staffing practices that consistently match staffing to need throughout each patient's stay

Our findings suggest that nurse staffing models that facilitate shift-to-shift decisions on the basis of an alignment of staffing with patients' needs and the census are an important component of the delivery of care.

We also found that the risk of death among patients increased with increasing exposure to shifts with high turnover of patients. Staffing projection models rarely account for the effect on workload of admissions, discharges, and transfers

Nurse Staffing and Inpatient Hospital Mortality, Needleman J.,

Buerhaus P., et al. N Engl J Med 2011; 364:1037-1045, March 17, 2011

9

http://www.nejm.org/toc/nejm/364/11/

Why redesign work on nursing units?

Nurses spend 31-44% of their time in direct patient care activities

Nurses experienced an average 8.4 work system failures per 8-hour shift

Medications

Orders

Supplies

Staffing

Equipment

Nurses spend 42 minutes of each 8-hour shift resolving operational failures

….and we are experiencing a nursing shortage!!!

Anita L. Tucker and Steven J. Spear, Operational Failures and Interruptions in

Hospital Nursing, Health Research and Educational Trust, 2006, pp. 1-20.

Improve the Work Environment through

Physical Space Design

Streamline and standardize supplies and

equipment throughout the unit.

Relocate essential supplies and equipment near

or in patients’ rooms.

Decentralize nursing workstations and pods.

Organize just-in-time supplies for special

treatments and procedures so that staff do not

have to hunt and gather.

Rutherford P, Bartley A, Miller D, et al. Transforming Care at the Bedside How-to

Guide: Increasing Nurses’ Time in Direct Patient Care. Cambridge, MA: Institute

for Healthcare Improvement; 2008. Available at www.IHI.org.

http://www.ihi.org/

Eliminate Waste and Redesign Key Processes

on Medical and Surgical Units

Admission Processes– Admission Team Trio at ThedaCare

Discharge Processes– “Ticket Home” at Virginia Mason

Medication Administration

– Locate Meds in or Near Patient Rooms

Handoffs and inter-professional team communications

Routine Care– Intentional Rounding

Rutherford P, Bartley A, Miller D, et al. Transforming Care at the Bedside How-to

Guide: Increasing Nurses’ Time in Direct Patient Care. Cambridge, MA: Institute for

Healthcare Improvement; 2008. Available at www.IHI.org.

http://www.ihi.org/

Demand/Capacity Management

Time

# o

f P

ati

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ts

Time

# o

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Eugene Litvak, PhD, Institute for Healthcare Optimization

What nurse staffing is needed to consistently

provide safe and quality care?

14

Example

Assumptions: 200 surgical beds

average census for surgical beds 160

staffing level 40 nurses (1 nurse per 4 patients)

average residual from 160 patients census is 20% or 32

patients

patients are distributed evenly between the nurses

How the mortality rate will change with 20%

increase in surgical demand?

© Institute for Healthcare Optimization 2016

15

Results

• 32 additional patients will be distributed evenly between

32 nurses: 1 additional patient per nurse or 4 + 1 = 5

patient per nurse

• these 32 nurses now will take care of 160 patients,

whose mortality rate increases by 7%

• if these additional 32 patients will be distributed evenly

between 16 nurses, then each such nurse will take care

of 4 + 2 = 6 patients

• these 16 nurses now will take care of 96 patients,

whose mortality rate increases by 14%


16Adoption of National Quality Forum Safe

Practices by Magnet Hospitals

Maintaining higher affordable nurse

staffing levels is only possible by

managing variability in patient flow

Jayawardhana, Jayani PhD; Welton, John M. PhD, RN; Lindrooth, Richard PhD,

Journal of Nursing Administration: September 2011 - Volume 41 - Issue 9, pp 350-356


17

Variability and health care-associated infection

Jeannie P. Cimiotti DNS,RN, Linda H. Aiken PhD, Douglas M. Sloane PhD, Evan S. Wu, BS

American Journal of Infection Control: August 2012- Volume 40, pp 486-490

“There was a significant association between patient-to-nurse ratio

and urinary tract infection (0.86; P ¼ .02) and surgical site infection

(0.93; P ¼ .04). In a multivariate model controlling for patient severity

and nurse and hospital characteristics, only nurse burnout remained

significantly associated with urinary tract infection (0.82; P ¼.03) and

surgical site infection (1.56; P <.01) infection. Hospitals in which

burnout was reduced by 30% had a total of 6,239 fewer infections, for

an annual cost saving of up to $68 million.”


18

Variability and Quality of Care*

Inadequate numbers of nursing staff contribute

to 24% of all sentinel events in hospitals.

Inadequate orientation and in-service education

of nursing staff are additional contributing

factors in over 70% of sentinel events

* Dennis S. O’Leary, - former President JCAHO (personal communication)


19

Variability and Readmissions I

Does variability affect readmission rate?

“The odds of one or more discharges becoming an unplanned

readmission within 72 hrs were nearly two and a half times higher on

days when ≥9 patients were admitted to the neurosciences critical care

unit …” *

“The odds of readmission were nearly five times higher on days when

≥10 patients were admitted …” *

* Baker, David R. DrPH, MBA; Pronovost, Peter J. MD, PhD; Morlock, Laura L. PhD, et al. Patient flow

variability and unplanned readmissions to an intensive care unit. Critical Care Medicine: November

2009 - Volume 37 - Issue 11 - pp 2882-2887


20

Variability and Readmissions II

Hospital Nursing and 30-Day Readmissions Among Medicare Patients With Heart Failure, Acute

Myocardial Infarction, and Pneumonia. McHugh, Matthew D. PhD, JD, MPH, RN; Ma, Chenjuan PhD,

RN. Medical Care: January 2013 - Volume 51 - Issue 1 - p 52–59

“Each additional patient per nurse in the average

nurse’s workload was associated with a 7%

higher odds of readmission for heart failure, 6%

for pneumonia patients, and 9% for myocardial

infarction patients ”.


21

• Quality and Safety Corner at www.ihoptimize.org

• The Institute for Healthcare Optimization’s approach to

managing variability in healthcare delivery addresses

some of the most intractable quality and safety issues

such as readmissions, mortality, infections, ED boarding

and others. Learn more »


http://www.ihoptimize.org/

http://www.ihoptimize.org/what-we-do-methodology-quality-and-safety.htm

22

What is easier:

to talk to your colleagues or to your lawyers?

http://www.nhmedmallawyer.com/blog/post/show/hospital-staffing-

and-its-effect-on-quality-care

http://www.healthleadersmedia.com/content/LED-269595/PDH-

Understaffing-a-Possible-Factor-in-Deaths-at-CRMC##


http://www.nhmedmallawyer.com/blog/post/show/hospital-staffing-and-its-effect-on-quality-care

http://www.healthleadersmedia.com/content/LED-269595/PDH-Understaffing-a-Possible-Factor-in-Deaths-at-CRMC

Nurse Staffing, Hospital Operations, Care

Quality, and Common Sense

1. Staff hospitals 24/7 according to the peaks in both bed

occupancy and admissions.

2. Be "creative" by introducing dynamic PNRs that will

fluctuate in a synchronous manner with census and

admissions

3. Legislate PNRs

4. Preserve the status quo and do nothing.

5. Change hospital patient flow management.

Litvak E, Laskowski-Jones,L; Nurse staffing, hospital operations, care

quality, and common sense; Nursing, August 2011.

24

Rapid Response Team

Does the Rapid Response Team helps at your hospital?

Why?

Litvak E, Pronovost PJ. Rethinking rapid response teams. JAMA. 2010;304(12):1375–6.

http://jama.jamanetwork.com/article.aspx?articleid=186602


25


IHO Variability Methodology®

http://www.ihoptimize.org/what-we-do-approach.htm

http://www.ihoptimize.org/what-we-do-approach.htm

Managing Unnecessary Variability in

Patient Demand

Surgical Demand:Perform Phase 1 and Phase 2

Establish a compliance committee

Medical demand: Develop a consensus on Admission-Discharge-Transfer

(ADT) criteria.

Establish a compliance committee

Litvak E, Buerhaus PI, Davidoff F, Long MC, McManus ML, Berwick DM. “Managing

Unnecessary Variability in Patient Demand to Reduce Nursing Stress and Improve Patient

Safety,” Joint Commission Journal on Quality and Patient safety, 2005; 31(6): 330-338.

27

How to determine the number of beds

needed

28

Phase III

Determination of Bed

And Staffing needs

Expected Benefits

• Further decreases in patient wait times where they exist

• Further enhancement of patient placement

• Decrease in staffing expense

• Enhanced utilization of existing resources

• Accurate determination of capacity growth need (Additional Med/Surg bed

requires ≈ $1- 3 million in capital cost + over $.25 - .$8 million annual

operational cost)


Right-Sizing Hospital Units

Unscheduled (medical and emergent/urgent

surgical) and scheduled (mostly surgical)

patients should be provided with separate bed

capacities

Capacity for the unscheduled demand should be

determined by Queuing Theory modeling

Capacity for scheduled demand could be

determined by computer simulation modeling

29


Right-Sizing Hospital Units

Average utilization of beds for scheduled

admissions could potentially be ≥ 90%

The rule of thumb for the average utilization of

beds for scheduled admission is ≈ 80%. Why?

30


How High Could Your Hospital bed occupancy

(census) Be (unscheduled patients only)?

Demand

Waiting time

80% utilization


Flex Capacity to Meet Seasonal, Day of the Week and

Hourly Variations in Demand

Can you predict a surge in admissions for patients with

medical conditions in the winter months?

Use seasonal flex units to manage increases in medical

patients during the winter months

Can you anticipate which units need more bed capacity?

(clue – which services consistently have a large number of

“off-service patients)

Use data analytics to quantify needs of each service

Do you have a regular surge of activity mid-week with the

hospital census regularly reaching >95% occupancy?

Smooth elective surgical schedules (particularly for patients who

will require ICU care post-op)


What should we do?

1. Streamline medical and surgical flow as discussed this

morning.

2. Perform Phase III of the IHO Variability Methodology® :

3. Apply Queuing Theory to determine capacity for

medical and other unscheduled demands (surgical,

Cath. Lab, transfers).

4. Use computer simulation to determine capacity for

elective patient demand. The results of such simulation

should be validated

33


http://www.ihoptimize.org/what-we-do-methodology.htm

James M. Anderson Center

For

Health Systems Excellence

Getting the Nursing Part Right

Nurse Staffing and Hospital Mortality

•Tertiary Medical Center – 197,691 patients, 176,696 RN shifts, 43 hospital units

•Relationship between nurse staffing and patient turnover

• Risk of Death 2-3 % for each below target shift

• Risk of Death 4-7 % for every high turnover shift • Admissions, discharges, and transfers

• Risk of Death 12 % for each below target shift

• Risk of Death 15 % for every high turnover shift

•Independent Variables when considering risks

Needleman J. et al. N Engl J Med 2011;364:1037-45.

ICU

Patient

Non-ICU

Patient

1st 5 days

LOS


For


Staffing and Environment

Nurse Staffing and Hospital Mortality / Failure to Rescue

•Effect of Nurse / Staffing Ratio on Mortality and Failure to Rescue is

directly related to team work environment

•Education Effect–10% BSN educated RN’s 4% mortality

•Lowering the patient-to-nurse ratio – effect of environment

• Marked improvement – good environment

• Modest improvement – fair environment

• No effect – poor environment

•Better environments lower mortality at all hospitals…but

• Poorly staffed hospitals - 2-3% improvement

• Best staffed hospitals - 12% – 14%

Aiken L. et al. Med Care 2011;49:1047-1053.


For


Staffing Prediction – Proactive Planning

• Data to Front Line Leaders – Updated daily

• Right Staff for the Right Patients

• Correct Number and Competency

• Flexible with Changing Environment

• Prediction of Needs – Be Prepared – Be Resilient


For



For


Predicting Unit Census vs Actual Census


For


Stressed Microsystems : Objectives

Quantitative metrics and qualitative measures indicative of

microsystem stress

Describe mitigation strategies at the unit, microsystem and

organizational levels to prevent serious harm and other

types of poor outcomes in stressed systems.

Discuss a systematic approach to predict stressed

microsystems.

Mitigate

Predict


For


Microsystem Stress Report

Microsystem Stress Report

Week of: 2/28/16 to 3/5/16

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A3N Surgery 22 76.4% 16.8 13.1 78.2% 59.7% 12.5 15.9 3.4 27.5% 22.82% 94.1 10.7% 160 18.2% 0 0.0% 0.0% 0.0% 0.0%

A3S TCC 24 91.7% 22.0 23.1 105.2% 96.4% 17.8 14.1 -3.7 -20.5% 20.56% 188.0 11.2% 221 13.2% 2 1.1% 35.7% 2.4% 38.1%

A4C1 Rehab 12 68.3% 8.2 11.1 135.3% 92.5% 18.2 9.0 -9.2 -50.5% 7.66% 155.4 29.4% 0 0.0% 1 1.6% 4.8% 0.0% 4.8%

A4N Transplant/Surgery 24 79.2% 19.0 18.5 97.1% 76.9% 15.2 14.1 -1.1 -7.4% 1.37% 5.0 0.4% 0 0.0% 0 0.0% 0.0% 0.0% 0.0%

A4S GI/Colorectal 24 79.2% 19.0 19.3 101.4% 80.3% 13.6 11.9 -1.7 -12.3% 8.18% 38.2 3.4% 213 19.0% 0 0.0% 0.0% 0.0% 0.0%

A5C Hem/Onc 32 88.4% 28.3 29.2 103.3% 91.4% 17.6 15.8 -1.8 -10.0% 0.00% 380.6 15.7% 115 4.7% 1 0.4% 2.4% 7.1% 9.5%

A5S BMT 36 83.1% 29.9 32.5 108.7% 90.3% 19.9 16.5 -3.3 -16.7% 23.64% 328.2 11.5% 288 10.1% 4 1.4% 19.0% 4.8% 23.8%

A6C Cardiology 17 88.2% 15.0 13.9 92.4% 81.5% 17.9 15.9 -2.0 -11.0% 5.03% 84.9 7.0% 72 5.9% 7 5.9% 2.4% 0.0% 2.4%

A6N Adol. Medicine 24 75.0% 18.0 20.9 116.0% 87.0% 13.6 9.1 -4.5 -33.2% 0.00% 39.3 3.5% 12 1.1% 1 0.8% 0.0% 0.0% 0.0%

A6S Child Medicine 24 75.0% 18.0 20.1 111.5% 83.6% 13.3 11.1 -2.2 -16.3% -9.83% 84.1 6.9% 32 2.6% 5 3.5% 11.9% 0.0% 11.9%

A7C1 Complex Pulmonary 11 78.2% 8.6 10.2 118.5% 92.6% 12.5 10.5 -2.0 -15.8% 20.19% 157.0 25.1% 0 0.0% 2 2.8% 7.1% 7.1% 14.3%

A7C2 CRC/Diabetes 11 79.1% 8.7 9.7 111.5% 88.2% 10.7 8.5 -2.2 -20.3% 11.14% 0.0 0.0% 0 0.0% 0 0.0% 0.0% 0.0% 0.0%

A7NS Neurosciences 41 70.7% 29.0 31.9 110.1% 77.9% 16.1 12.9 -3.3 -20.2% 6.02% 215.9 11.5% 144 7.7% 9 4.5% 4.8% 0.0% 4.8%

B4 NICU 59 89.5% 52.8 56.9 107.7% 96.4% 18.2 14.6 -3.6 -19.9% 8.56% 761.7 14.3% 456 8.6% 18 3.4% 21.4% 4.8% 26.2%

B5CA Complex Airway 11 66.4% 7.3 7.8 107.0% 71.0% 16.9 14.3 -2.6 -15.4% 16.52% 24.9 5.0% 32 6.4% 0 0.0% 2.4% 0.0% 2.4%

B5CC PICU 35 77.1% 27.0 29.8 110.2% 85.0% 26.3 23.9 -2.4 -9.2% 20.26% 489.4 11.6% 648 15.3% 6 1.4% 45.2% 26.2% 71.4%

B6HI CICU 25 74.8% 18.7 18.1 96.6% 72.3% 25.3 25.2 -0.1 -0.4% 11.86% 36.4 1.5% 595 25.0% 2 0.9% 2.4% 0.0% 2.4%

Total 432 80.2% 346.3 366.0 105.7% 84.7% 3083.1 10.4% 2988 10.1% 58 1.9% 9.4% 3.1% 12.5%

Status Criteria

Red< 90%;

> 105%< -5% > 12% > 15% > 10 %

Yellow90% - 95%;

100% - 105%

Green 95% - 100%


For


Inpatient Indicators of Stressed System

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Inpatient Indicators

% Occupancy to Capacity (< 85 %)

% Occupancy To Budgeted AWC (< 105%)

% Variance of Direct Care NHPPD (> - 5%)

% Operational Vacancy (< 12%)

% Float Staff Used (< 15%)

% Orange and Red Shifts (< 10%)

right-sizing hospital units and nurse staffing -...

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