The speakers do not have any disclosures relevant to today...

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Copyright © 2010 Institute for Healthcare Improvement

Unless from another source © 2015 Institute for Healthcare Optimization Slide 2

Disclosures

The speakers do not have any disclosures relevant to today’s session and discussion


Faculty

!  Jason Leitch Clinical Director, Quality Unit Scottish Government Health Department

!  Peter Lachman Deputy Medical Director Great Ormond Street Hospital for Children NHS Foundation Trust

!  Eugene Litvak President and CEO, Institute for Healthcare Optimization, USA

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Aims

The impact of falling budgets and increasing demand on health services requires a new approach to solving intractable problems.

In this session participants will learn that one can deliver safe and high quality care, improve outcomes and improve patient experience by changing the operational paradigm.

This session demonstrate that principles of managing operations lead to cost-effective ways to deliver safe care. The theory will be illustrated by real time case studies and participatory work.


Learning Objectives

Explain the relationship between flow safety and cost

Implement in a health care setting the principles of managing operations

Describe the key challenges in redesigning the flow of patients to improve safety


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Setting the Context

!  Is the central problem in healthcare cost or quality?

!  Does your organization need additional resources?

!  Is your biggest problem deciding where to close services or how to improve quality ….or both?

!  Does managed care/capitation accompanied by reduced budget leads to poorer quality of care.



Reference Health Foundation

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Reference Health Foundation



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Spending more does not improve quality

CMS data:

Higher spending states have poorer quality

Source: Baicker K, Chandra A. Medicare spending, the physician workforce, and beneficiaries' quality of care. Health Aff (Millwood). 2004 Jan-Jun;Suppl Web Exclusives:W4-184-97.


AUS CAN FRA GER NETH NZ NOR SWE SWIZ UK US

OVERALL RANKING (2013) 4 10 9 5 5 7 7 3 2 1 11

Quality Care 2 9 8 7 5 4 11 10 3 1 5

Effective Care 4 7 9 6 5 2 11 10 8 1 3

Safe Care 3 10 2 6 7 9 11 5 4 1 7

Coordinated Care 4 8 9 10 5 2 7 11 3 1 6

Patient-Centered Care

5 8 10 7 3 6 11 9 2 1 4

Access 8 9 11 2 4 7 6 4 2 1 9

Cost-Related Problem 9 5 10 4 8 6 3 1 7 1 11

Timeliness of Care 6 11 10 4 2 7 8 9 1 3 5

Efficiency 4 10 8 9 7 3 4 2 6 1 11

Equity 5 9 7 4 8 10 6 1 2 2 11

Healthy Lives

4 8 1 7 5 9 6 2 3 10 11

Health Expenditures/Capita, 2011** $3,800 $4,522 $4,118 $4,495 $5,099 $3,182 $5,669 $3,925 $5,643 $3,405 $8,508

COUNTRY RANKINGS

Top 2*

Middle

Bottom 2*

EXHIBIT ES-1. OVERALL RANKING

Notes: * Includes ties. ** Expenditures shown in $US PPP (purchasing power parity); Australian $ data are from 2010.Source: Calculated by The Commonwealth Fund based on 2011 International Health Policy Survey of Sicker Adults; 2012 International Health Policy Survey of Primary Care Physicians; 2013 International Health Policy Survey; Commonwealth Fund National Scorecard 2011; World Health Organization; and Organization for Economic Cooperation and Development, OECD Health Data, 2013 (Paris: OECD, Nov. 2013).


International Comparison of Spending on Health, 1980–2011

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Traditionally, healthcare providers have prospered without needing to focus on costs and waste…

!  Emphasis on revenue growth !  Little price transparency !  Less competitive than most

industries !  Typically funded with public

funds

…but several forces suggest that this will not continue

!  Cost inflation>incomes !  Decreased public funding !  NHS initiatives focusing on efficiency

and cost !  Threat of disruptive, lower cost

entrants - privatisation

Providers Will Find Increasing Pressures to Focus on Costs and Waste


The challenge to improve value

Can we look at ways of delivering health care at lower cost at the

same time we increase quality and safety?


What is value?

!  Value should always be defined around the customer, and in a well-functioning health care system, the creation of value for patients should determine the rewards for all other actors in the system.

!  Since value depends on results, not inputs, value in health care is measured by the outcomes achieved, not the volume of services delivered, and shifting focus from volume to value is a central challenge.

!  Nor is value measured by the process of care used; process measurement and improvement are important tactics but are no substitutes for measuring outcomes and costs

What Is Value in Health Care? Michael E. Porter, Ph.D. N Engl J Med 2010; 363:2477-2481December 23, 2010

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Where to improve flow

!  Can one reduce ED overcrowding just by improving patient flow through the ED?

!  Can one improve ICU patient flow by increasing its size?


Reducing the Time of Patient Transfer…

… between two hospital units or the length of stay in the unit may not be right goal

!  Do we really want to transfer patients from the ED to Med/Surg bed in one hour?

!  Should we “fight” for reducing ICU length of stay?

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High Census May Not be a Good Reason for Adding More Beds

!  Midnight or midday census? The answer might be …“neither”

!  Does the patient mix matter? Which patients mix?

!  Does the size of your hospital/unit matter?

!  What would you do when your waiting room is full?


Is Your Goal to Have a High Utilization of Your Resources?

!  Is this a right goal?

!  What happens when your resources are highly utilized?

!  When utilization is high enough?


Can Your Hospital or Physician Office Reduce Overcrowding?

!  Should this be your goal?

!  Do you want to attract more patients?

!  Would more patients reduce overcrowding and wait times?

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Patient driven health care?

!  Do patients want to spend hours/days in overcrowded EDs?

!  Do they want to be taken care of by stressed nurses and as a result be subjected to medical errors?

!  Do they want to acquire hospital infection?

!  Do they want to be readmitted and start all over again?

!  Do they want to deteriorate during their hospital stay?


!  There is a right goal for patient flow improvement – increasing patient throughput and access to care while improving or controlling quality of care!

!  Can Operations Management and Variability Methodology help you to achieve this goal?


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Boeing 737 Imagine it has to carry 300 people aboard on a particular day from point A to point B. !  What would be the quality of service provided by the four flight attendants? !  What would be the cost of fixing up such a plane after the flight? !  How about having rather Boeing 747 more suitable for this load? !  The problem is that on the way back from point B to point A there are only

100 passengers to be carried. !  Then, using either plane would introduce significant waste, although for

Boeing 747 greater than for Boeing 737. !  Suppose that this is an everyday pattern. !  What plane should one choose: more expensive but safer (under this

scenario) Boeing 747 or cheaper but unsafe Boeing 737?

This is a wrong question that we are trying to answer in health care.

The right question is: Why do we have to carry 300 passengers one way and 100 – another way?



Phase 1 Building a safe and efficient health care delivery without managing patient flow

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Phase 2 Building a safe and efficient health care delivery without managing patient flow


New IOM report


Gaps in healthcare

!  Ineffectiveness of care !  Lack of efficiency in delivery system !  Inadequate safety !  Insufficient patient-centerness !  Inadequate timeliness of care

IOM Crossing the Quality Chasm (2001)

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Major health care delivery problems

!  Patient Safety

!  Nurse understaffing/overloading

!  ED overcrowding/access to care

!  High cost

Addressing variability is necessary, although not sufficient, to satisfactorily resolve these problems.


How does an unsmooth census looks like? (no holidays, no weekends, weekdays only)


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Systemic Effects of Peak Loads

!  Internal Divert !  Patients sent to alternative wards\Intensive Care

locations !  Internal Delays

!  Recovery room backs up !  External Divert

!  ED overcrowding and waits !  Staff overload

!  medical errors and inability to retain staff !  System Gridlock

!  Increase in LOS !  Decreased throughput


Controlling the total cost, without knowing cost of delivery, decreases quality.

Take-out Pizza Example


"  Ed. Litvak E; Managing Patient Flow in Hospitals: Strategies and Solutions, Second Edition; Beurhaus P, Rudolph M, Fuda K., et al; Joint Commission Resources, 2009.

http://www.jointcommissioninternational.org/managing-patient-flow-in-hospitals-strategies-and-solutions-second-edition/

"  Litvak E. & Long MC. Cost and Quality Under Managed Care: Irreconcilable Differences? American Journal of Managed Care, 2000; 6 (3): 305-312.

http://www.ajmc.com/files/articlefiles/AJMC2000MarLitvak305_312.pdf

"  Litvak E. "Optimizing patient flow by managing its variability". In Berman S. (ed.): Front Office to Front Line: Essential Issues for Health Care Leaders. Oakbrook Terrace, IL: Joint Commission Resources, 2005, pp. 91-111.

http://www.ihoptimize.org/Collateral/Documents/English-US/front%20lines%20chapter.pdf

Variability is the key

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Quotes from the 2006 IOM report The Future of Emergency Care in the U.S. Health System (Hospital-Based Emergency Care: At the Breaking Point)

!  “By applying variability methodology, queuing theory and the I/T/O model, hospitals can identify and eliminate many of the patient flow impediments caused by operational inefficiencies”

!  “By smoothing the inherent peaks-and valleys of patient flow, and eliminating the artificial variabilities, that unnecessarily impair patient flow, hospitals can improve patient safety and quality while simultaneously reducing hospital waste and cost”


The Ideal Healthcare System

(100% efficiency)

1. All patients have the same disease with the same severity.

2. All patients arrive at the same rate.

3. All providers (physicians, nurses) are equal in their ability to provide quality care.


Natural Variability

1.  Clinical Variability 2.  Flow Variability 3.  Professional Variability

! Random ! Can not be eliminated (or even reduced) ! Must be optimally managed

}

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Can your health care delivery system become a Toyota product line?


Designing and Testing Complex Mechanical Systems: The Family Car

!  Hitting a pothole vs. high speed impact against the wall

!  Health care “financial bumper”

Are the stresses an intrinsic part of health care delivery?


What makes hospital census variable?

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What makes hospital census variable?

!  If ED cases are 50% of admissions and…

!  Elective-scheduled surgical cases are 35% of admissions then…

!  Which would you expect to be the largest source of census variability?


The answer is…

The ED and Elective-Scheduled surgeries have approximately equal effects on census variability.

Why? Because of another (hidden) type of

variability...


Artificial Variability

!  Non-random

!  Non-predictable (driven by unknown individual priorities)

!  Should not be managed, must be identified and eliminated

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Why managing variability today is more important than

before?


Does the healthcare system need more capacity?

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At what cost?

Typical cost of new capacity

!  Inpatient beds - £1M in capital and £125K-500K annual operating expense

!  Operating theatres - €2 – 7 Million, + €250K annual operating expense

!  Major imaging (CT, MRI, PET/CT, etc.) – approx. €1M+

!  Cardiac Catheterization Lab – approx.€2M

Nursing and other provider shortages?

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Is this about access to care?


Wards

ICU

ED

Variability and access to care

Scheduled demand


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Can one solve your ED, ICU or Medical /Surgical units overcrowding without smoothing elective admissions?

Can one match capacity and patient demand without throwing excessive resources at the system or by other means without smoothing elective admissions?


The right size of the hospital units for both scheduled and unscheduled admissions can only be determined after elective patient flow is smoothed (and only then!)


!  Do you always place every patient into the appropriate bed - or do you have outliers?

!  What happens if you do not?

!  How important is to know the right size of the unit?

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Rapid Response Team

!  Does the Rapid Response Team help improve care at your hospital ?

!  Why does it help?

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


Elective Surgical Requests vs Total Refusals

Michael L. McManus, M.D., M.P.H.; Michael C. Long, M.D.; Abbot Cooper; James Mandell, M.D.; Donald M. Berwick, MD; Marcello Pagano, Ph.D.; Eugene Litvak, Ph.D. Impact of Variability in Surgical Caseload on Access to Intensive Care Services, Anesthesiology 2003; 98: 1491-1496.


Variability and health care-associated infection

!  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.”

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

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Does variability affect readmission rate?

“The main outcome variable is unplanned patient readmission to the neurosciences critical care unit within 72 hrs of discharge to a lower level of care.

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

Variability and Readmissions


Is this about nurse staffing as well? e.g. Nearly one in 10 hospitals in England have had a fill rate for nursing shifts of less than 90%.

Can you provide an adequate nurse staffing without smoothing elective admissions?


Five ways of staffing* 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/regulate 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. http://journals.lww.com/nursing/Fulltext/2011/08000/Nurse_staffing,_hospital_operations,_care_quality,.1.aspx

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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*

!  “…higher numbers of nurse hours per patient, larger proportions of RNs and high levels of competition with other hospitals were all correlated with higher levels of NQF Safe Practices adoption.”**

*Dennis S. O’Leary - former president of JCAHO (personal communication) ** http://nursing.advanceweb.com/News/National-News/Magnet-Designated-Hospitals-More-Likely-to-Adopt-NQF-Safety-Practices.aspx


Variability and mortality

“Each additional patient per nurse was associated with a 7% increase in the likelihood of dying within 30 days of admission and a 7% increase in the odds of failure-to-rescue”

Linda H. Aiken, Sean P. Clarke, Douglas M. Sloane, Julie Sochalski, and Jeffrey H. Silber. Hospital Nurse Staffing and Patient Mortality, Nurse Burnout, and Job Dissatisfaction. JAMA, 2002; 288: 1987:1993


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?

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.

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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%


“There was a significant association between increased mortality and increased exposure to unit shifts during which staffing by RNs was 8 hours or more below the target level “

“The association between increased mortality and high patient turnover was also significant “

Nurse Staffing and Inpatient Hospital Mortality, Needleman J., Buerhaus P., et al. N Engl J Med 2011; 364:1037-1045, March 17, 2011

Patient Mortality and Patient Flow


Quality and Safety Corner 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 »

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Part 3 Applications to the health care problems

Applying theory to the front line


Questions that you may have:

!  Why are we doing this?

!  Why will this succeed?

!  What exactly are we going to do?

!  How much additional work is this going to mean for me?

!  How will we ensure this does not damage what currently happens?


Why change?

!  Bumped or delayed elective surgery cases

!  Delays in securing theatre access for urgent and emergent cases (transplantations)

!  Overburdened nurses, medical errors, high overtime, excessive nurse vacancies

!  Lack of timely access to nursing units

!  Prevent ED overcrowding and boarding

!  Improve patient, provider and staff satisfaction

“By smoothing the inherent peaks and valleys in pa5ent flow, and elimina5ng the ar5ficial variabili5es that unnecessarily impair pa5ent flow, hospitals can improve pa5ent safety and quality while simultaneously reducing hospital waste and cost.” Ins5tute of Medicine, June 2006

JCAHO’s Patient Flow Leadership Standard - "LD.3.15 The leaders develop

and implement plans to identify and mitigate impediments to efficient patient

flow throughout the hospital.”

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Expertise Necessary for Success

! Application of operations management to healthcare

! Clinical expertise

! Hospital management expertise

! Project management and data analysis experience

The key pillars of expertise that drive success in an Operating Theatre redesign project are:


IHO Approach to Operating Theatre (OT) Design and Patient Flow Improvement

Phase I Separation of

OT Flows

Phase II Smoothing of Elective Flows

Phase III Determination

of Bed and Staffing needs


Project Overview Phase I

Separation of Operating Theatre Flows

9 months

Goals •  To assess the extent of artificial

patient volume variability and patient flow bottlenecks in key areas of the hospital

•  To separate flows of scheduled (elective) patients from that of unscheduled (emergent/urgent) and work-in patients through the Operating Theatres

Expected Benefits •  Increase in surgical capacity /

volume with no decrease in any individual surgeon’s volume as a result

•  Decrease in patient wait times for emergent and urgent surgeries

•  Decrease in Theatre overtime

•  Increase in staff and patient satisfaction

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Phases II and IIb Theatre and Cath Lab

Smoothing

Expected Benefits •  Further increases in capacity / throughput •  Enhanced patient placement in preferred beds • Decrease in nursing stress • Decrease in mortality and medical errors related to delays

and patient misplacement •  Increase in transplantations volume •  Prevention of ED overcrowding


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 over €1 million in capital cost + over €.25 million annual operational cost)


Managing Patient Flow: A Focus on Critical Processes

http://store.trihost.com/jcaho/product.asp?dept%5Fid=34&catalog%5Fitem=712

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Example 1: Cincinnati Children’s !  Weekend waiting time (for urgent / emergent surgeries) down 34%

despite 37% volume increase !  Weekday waiting time down 28% despite 24% volume increase (results

for the first three months after implementation) !  Operating Theatre overtime down by 57% (approx. $559K saved

annually) !  Initially an equivalent of 1 Operating Theatre capacity freed up !  Surgery volume has sustained 7% growth per year for the last two years !  Inpatient occupancy increased from 76% to 91% resulting in $115

million/year plus 100 new beds avoided capital cost (over $100 million) !  Substantially improved provider satisfaction

Sources: 1.  Frederic Ryckman, MD, Cincinnati Children’s Hospital Medical Center 2.  Scott Allen, No waiting: A simple prescription that could dramatically improve hospitals - and American health

care, The Boston Globe, July 30, 2009 http://www.boston.com/bostonglobe/ideas/articles/2009/08/30/a_simple_change_could_dramatically_improve_hospitals_ndash_and_american_health_care/?page=full


Their opinion…

“this is the best thing for ortho since I have been here…we get our cases done earlier which makes our families very happy...patients don’t have to wait NPO until the evening. The weekends are unbelievably good, the OR hostility that was previously present has been virtually eliminated”.

(Orthopaedic Surgeon, Division Director)


“I feel there is an improvement in our time and efficiency when assigning staff. We are assigning add-on staff the day before instead of pulling them from other rooms...”

(Theatre nurse)

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Example 2: Boston Medical Center

!  Surgical throughput up 10% !  Bumped surgeries down 99.5% !  Reduced nurse stress; 1/2 hour reduction

(6%) in nurse hours per patient day in one unit ($ 130.000 annual saving)

!  ED waiting time down 33% !  2.8 hour wait in one of state’s busiest EDs

vs. 4 to 5+ hours for most of the academic hospitals in Boston

Source: John Chessare, MD, then Chief Medical Officer at Boston Medical Center


Example 3: Mayo Clinic (Fl)

CHANGES IN OPERATIONAL PERFORMANCE OF OPERATING THEATRES Pre- Re-Design Post-Re-Design % Change Surgical Cases (count) 11,874 12,367 4% Surgical Minutes 1,757,008 1,844,479 5% Prime Time OR Utilization 61% 64% 5% Number of Overtime FTE's (average) 7.4 5.4 -27% Staff Turnover Rate 20.3% 11.5% -43% Daily Elective Room Changes (Average/Mon) 80 25 -69% Daily Elective Room Changes (%) 8% 2% -70% Cost/Case (added 15 OR Staff FTEs) $1,062 $1,070 0% Cost/Minute of Surgery (added 15 OR Staff FTEs) $7.18 $7.26 1% Staff Turnover Cost (millions) $2.47 $1.40 -43% Overtime Cost Savings $111,488 Total OT Net Revenue (fee increase adjusted) $93,929,569 $98,686,693 5% Net Operating Income $15,877,986 $21,957,708 38%

Operating margin 17% 22% 28%

Source: C. Daniel Smith, et al. Re-Engineering the Operating Room Using Variability Management to Improve Healthcare Value. Journal of the American College of Surgeons, Volume 216, Issue 4 , Pages 559-568, April 2013


Example 4: John Hopkins

!  Waiting time decreased by 39% !  Compliance increased by 16.7% despite no dedicated Cardiac or

Pediatric level rooms !  Level I compliance increased from 24% to 81%

!  Throughput increased !  5 cases per day in the GOR/Weinberg !  4 cases per day in JHOC

!  Prime Time Utilization provides additional room for substantially more throughput !  Additional 7 cases per NHW at 85% PT utilization

!  No increase in afterhours case minutes !  6.6% decrease in the proportion of overtime minutes !  Case length decrease reflects increased team performance

!  Provides 1 ”free” additional room per day

Source: Dr. Jackie Martin, Medical Director of Perioperative Services Professor , Anesthesiology and Critical Care Medicine

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Phase I Estimated Return on Investment

Relative to Assessment (Mar-Jun 2010)

Relative to Pre-Implementation (Sep-Nov 2011)

Previous Cases/ NHW 87 89

Current Cases/ NHW 92 92

Incremental Cases/ NHW 5 3

Incremental Margin/ Year* $6,350,000 $3,810,000 *Assumes $5,000 margin per case x 254 Non-Holiday Weekdays per Year

Source: Dr. Jackie Martin, Medical Director of Perioperative Services for the Johns Hopkins Hospital; Professor , Anesthesiology and Critical Care Medicine


Example 5 St. Thomas Community Health Center New Orleans, LA Outpatient clinics

!  Improved capacity by 35% thereby improving access to care

for patients !  Increased number of patients treated by 25% by proper

allocation of resources and scheduling practice !  Reduced patient waiting time !  Timely access to same day and next day service !  Improved team performance, provider and patient satisfaction !  Increased operational efficiency and quality of care

http://www.theneworleansadvocate.com/community/crescentcity/11387066-171/st-thomas-fills-health-care http://www.ihoptimize.org/who-we-work-with-iho-impact-clinics.htm


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What would be notional return on investment from applying these concepts?

Eugene Litvak (IHO), Maureen Bisognano (IHI). More Patients, Less Payment: Increasing Hospital Efficiency In The Aftermath Of Health Reform. Health Affairs, January 2011 (30):176-180


OECD Acute Care Bed Occupancy

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Canad

a

Norway

Switzerl

and

Irelan

d UK

Japan

Austria

Spain

German

y Ita

ly

France

U.S.

90% 88% 86% 86% 84% 79% 79% 79%

76% 76% 73%

67%

Acute Care Bed Occupancy 2009 OECD Health Data

Slide provided by Sandeep Green Vaswani, Institute for Healthcare Optimization


US hospitals are 1/3 empty and…overcrowded!!!

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National Opportunity – An Example !  Based on AHA 2007 data, overall nationwide hospital inpatient

occupancy was about 65%

!  Even if one were to assume that all admissions are urgent in nature (statistically random arrivals), 80% occupancy should be achievable (based on queuing methodology) without compromising access or quality of care1simultaneously improved patient safety and quality of care.

!  By comparison, US airlines operated at just under 80% in 2007 and 20082 and US utilities operated at an 80-90% utilization in 2007-20083

!  A mix of elective and random admissions allows for achievement of even greater occupancy with

Litvak E., Bisognano M. More Patients, Less Payment: Increasing Hospital Efficiency In The Aftermath Of Health Reform . Health Affairs, 2011, vol. 30, No. 1, pp. 76-80


National Opportunity Financial Impact

!  Total capital plus operational cost for the US in 10 years is between $400 billion and over $1 trillion*

!  “National diffusion could reduce total US per capita spending by an estimated 4% to 5%” (over $100 billion/year)**

* Managing Variability in Healthcare Delivery” , “The Healthcare Imperative: Lowering Costs and Improving Outcomes: Workshop Series Summary”, Institute of Medicine, 2010: http://www.nap.edu/openbook.php?record_id=12750&page=294

** Milstein A., Shortell S. Innovations in Care Delivery to Slow Growth of US Health Spending. JAMA, October 10, 2012—Vol 308, No. 14, 1439-40


Effects of Flow Variability on Quality of Care and Patient Safety

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

Patient Flow

Variability

2-4% increase in mortality risk

for each exposure to an

understaffed shift Unmanageable

Nurse: Patient staffing leading to overwork and

stress

Up to 500%+ increase in

odds of readmission

Diversion and delays for

Emergency Department

patients Increased

medical errors infections and

non-compliance

with NQF safe practices

Unnecessary launches of

Rapid Response

Teams

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Variability, Quality of Care and Patient Safety

“A reliable health system is one that functions properly in difficult and unanticipated circumstances as well as in normal or easy ones — indeed, difficult and unanticipated circumstances are par for the course in health care.

Among the most common such conditions are periods of excess patient load that can overwhelm even the most conscientious physician or nurse and impair the quality of care.”

The New England Journal of Medicine Perspective Smoothing the Way to High Quality, Safety, and Economy, October 24, 2013 | E. Litvak and H.V. Fineberg


Three alternatives to meet increasing demand

!  Historical

!  Provide the resources (e.g., staffing) sufficient to meet current patient peaks in demand

!  Current

!  Staff below the peaks and tolerate ED overcrowding, nursing overloading and medical errors

!  Future

!  Smooth artificial variability and provide the resources to meet patient (vs. schedule) driven peaks in demand. Variability methodology can quantify and justify such additional resources


Overcrowding Mortality Readmissions High cost Medical errors Nurse shortage

Healthcare “culture”

Mortality, Readmissions, Medical Errors, High Cost vs. Health Care “Culture”:

What Will Prevail?

You decide!

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Part 4 UK Examples


Great Ormond Street Hospital

!  Analysed our data to see variability

!  Then developed programmes to implement change

!  The following data is from one of the work streams


Great Ormond Street Hospital

!  Tertiary and quaternary hospital !  No Emergency Room !  However variability is still a problem !  How do we deal with this

!  Analysis of data !  Understand the problem !  Develop the model for system wide

change

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Where was the problem?

!  Inpatient beds ! Diagnostic procedures ! Operating theatre ! Outpatients


GOSH Admissions By Date

0

20

40

60

80

100

120

140

160

Sun/

01/0

4/07

Sun/

15/0

4/07

Sun/

29/0

4/07

Sun/

13/0

5/07

Sun/

27/0

5/07

Sun/

10/0

6/07

Sun/

24/0

6/07

Sun/

08/0

7/07

Sun/

22/0

7/07

Sun/

05/0

8/07

Sun/

19/0

8/07

Sun/

02/0

9/07

Sun/

16/0

9/07

Sun/

30/0

9/07

Sun/

14/1

0/07

Sun/

28/1

0/07

Sun/

11/1

1/07

Sun/

25/1

1/07

Sun/

09/1

2/07

Sun/

23/1

2/07

Sun/

06/0

1/08

Sun/

20/0

1/08

Sun/

03/0

2/08

Sun/

17/0

2/08

Sun/

02/0

3/08

Sun/

16/0

3/08

Sun/

30/0

3/08

Cou

nt

Admission Date

Grand Total Total Mean

Program for Management of Variability in Health Care Delivery Boston University Health Policy Institute


Admissions By Urgency and Date

0

20

40

60

80

100

120

140

160

Sun/

01/0

4/07

Sun/

15/0

4/07

Sun/

29/0

4/07

Sun/

13/0

5/07

Sun/

27/0

5/07

Sun/

10/0

6/07

Sun/

24/0

6/07

Sun/

08/0

7/07

Sun/

22/0

7/07

Sun/

05/0

8/07

Sun/

19/0

8/07

Sun/

02/0

9/07

Sun/

16/0

9/07

Sun/

30/0

9/07

Sun/

14/1

0/07

Sun/

28/1

0/07

Sun/

11/1

1/07

Sun/

25/1

1/07

Sun/

09/1

2/07

Sun/

23/1

2/07

Sun/

06/0

1/08

Sun/

20/0

1/08

Sun/

03/0

2/08

Sun/

17/0

2/08

Sun/

02/0

3/08

Sun/

16/0

3/08

Sun/

30/0

3/08

Cou

nt

Admission Date

Grand Total Total Mean Elective Elective Mean Emergency Emergency Mean


35


Elective Inpatient Admissions by Use of Theatres: Non-holiday Weekdays

0

5

10

15

20

25

30

35

40

45

50

4/2/

2007

4/

9/20

07

4/16

/200

7 4/

23/2

007

4/30

/200

7 5/

7/20

07

5/14

/200

7 5/

21/2

007

5/28

/200

7 6/

4/20

07

6/11

/200

7 6/

18/2

007

6/25

/200

7 7/

2/20

07

7/9/

2007

7/

16/2

007

7/23

/200

7 7/

30/2

007

8/6/

2007

8/

13/2

007

8/20

/200

7 8/

27/2

007

9/3/

2007

9/

10/2

007

9/17

/200

7 9/

24/2

007

10/1

/200

7 10

/8/2

007

10/1

5/20

07

10/2

2/20

07

10/2

9/20

07

11/5

/200

7 11

/12/

2007

11

/19/

2007

11

/26/

2007

12

/3/2

007

12/1

0/20

07

12/1

7/20

07

12/2

4/20

07

12/3

1/20

07

1/7/

2008

1/

14/2

008

1/21

/200

8 1/

28/2

008

2/4/

2008

2/

11/2

008

2/18

/200

8 2/

25/2

008

3/3/

2008

3/

10/2

008

3/17

/200

8 3/

24/2

008

3/31

/200

8 Non-theatre Non-theatre Mean Theatre Theatre Mean



Inpatient vs. Day Case Admissions: Non-holiday Weekdays Only


0

10

20

30

40

50

60

70

80

90

100

Mon

/02/

04/0

7

Mon

/16/

04/0

7

Mon

/30/

04/0

7

Mon

/14/

05/0

7

Mon

/28/

05/0

7

Mon

/11/

06/0

7

Mon

/25/

06/0

7

Mon

/09/

07/0

7

Mon

/23/

07/0

7

Mon

/06/

08/0

7

Mon

/20/

08/0

7

Mon

/03/

09/0

7

Mon

/17/

09/0

7

Mon

/01/

10/0

7

Mon

/15/

10/0

7

Mon

/29/

10/0

7

Mon

/12/

11/0

7

Mon

/26/

11/0

7

Mon

/10/

12/0

7

Mon

/24/

12/0

7

Mon

/07/

01/0

8

Mon

/21/

01/0

8

Mon

/04/

02/0

8

Mon

/18/

02/0

8

Mon

/03/

03/0

8

Mon

/17/

03/0

8

Mon

/31/

03/0

8

Cou

nt

Admission Date

DC DC Mean IP IP Mean


Theatre Cases by Date: Non-holiday Weekdays Only


0

10

20

30

40

50

60

Mon

/02/

04/0

7

Mon

/16/

04/0

7

Mon

/30/

04/0

7

Mon

/14/

05/0

7

Mon

/28/

05/0

7

Mon

/11/

06/0

7

Mon

/25/

06/0

7

Mon

/09/

07/0

7

Mon

/23/

07/0

7

Mon

/06/

08/0

7

Mon

/20/

08/0

7

Mon

/03/

09/0

7

Mon

/17/

09/0

7

Mon

/01/

10/0

7

Mon

/15/

10/0

7

Mon

/29/

10/0

7

Mon

/12/

11/0

7

Mon

/26/

11/0

7

Mon

/10/

12/0

7

Mon

/24/

12/0

7

Mon

/07/

01/0

8

Mon

/21/

01/0

8

Mon

/04/

02/0

8

Mon

/18/

02/0

8

Mon

/03/

03/0

8

Mon

/17/

03/0

8

Mon

/31/

03/0

8

Cou

nt

Admission Date

DC DC Mean IP IP Mean Outpatient Outpatient Mean

36


Discharges by Date

0

20

40

60

80

100

120

140

160

Sun/

01/0

4/07

Sun/

15/0

4/07

Sun/

29/0

4/07

Sun/

13/0

5/07

Sun/

27/0

5/07

Sun/

10/0

6/07

Sun/

24/0

6/07

Sun/

08/0

7/07

Sun/

22/0

7/07

Sun/

05/0

8/07

Sun/

19/0

8/07

Sun/

02/0

9/07

Sun/

16/0

9/07

Sun/

30/0

9/07

Sun/

14/1

0/07

Sun/

28/1

0/07

Sun/

11/1

1/07

Sun/

25/1

1/07

Sun/

09/1

2/07

Sun/

23/1

2/07

Sun/

06/0

1/08

Sun/

20/0

1/08

Sun/

03/0

2/08

Sun/

17/0

2/08

Sun/

02/0

3/08

Sun/

16/0

3/08

Sun/

30/0

3/08

Cou

nt

Date

Discharges Overall Mean



Summary

!  While day case patients comprise majority of admissions, true inpatients have most impact

!  Substantial variability in elective admissions !  Theatre cases comprise large majority

!  Wasted bed & theatre capacity !  Improved scheduling of elective admissions,

especially theatre cases, needed



Recommendation Current

Central management of admissions Yes …starting

Establishment of a central ‘patient flow team’ Yes

Central management of operationally-relevant information systems Yes

Improve collection and reporting of flow data Yes

Separate emergency and elective beds No

Separate resources for day case and inpatients +/-

Determine best management strategies for ‘high utiliser’ patients +/-

Reconfigure wards into larger units +/-

37


0

5

10

15

20

25

30

35

13-D

ec-0

8

27-D

ec-0

8

10-J

an-0

9

24-J

an-0

9

07-F

eb-0

9

21-F

eb-0

9

07-M

ar-0

9

21-M

ar-0

9

04-A

pr-0

9

18-A

pr-0

9

02-M

ay-0

9

16-M

ay-0

9

30-M

ay-0

9

13-J

un-0

9

27-J

un-0

9

11-J

ul-0

9

25-J

ul-0

9

08-A

ug-0

9

22-A

ug-0

9

05-S

ep-0

9

19-S

ep-0

9

03-O

ct-0

9

17-O

ct-0

9

Abs

olut

e C

ount

Date

Cardiac VFM Pilot: Weekly Theatre Throughput and Critical Flow Failures

Weekly theatre throughput

Weekly theatre critical flow failures

Increasing NHS + IPP throughput

Decreasing critical flow failures (cancellations, emergency refusals and nursing shifts lost to sickness)

New solution go live

PDSA: flow management taken on by operational manager

Reduced variability - process more predictable

High variability - process unpredictable

Copyright Great Ormond Street


0

100

200

300

400

500

600

700

800

900

1000

13-D

ec-0

8

27-D

ec-0

8

10-J

an-0

9

24-J

an-0

9

07-F

eb-0

9

21-F

eb-0

9

07-M

ar-0

9

21-M

ar-0

9

04-A

pr-0

9

18-A

pr-0

9

02-M

ay-0

9

16-M

ay-0

9

30-M

ay-0

9

13-J

un-0

9

27-J

un-0

9

11-J

ul-0

9

25-J

ul-0

9

08-A

ug-0

9

22-A

ug-0

9

05-S

ep-0

9

19-S

ep-0

9

03-O

ct-0

9

17-O

ct-0

9

Cumulative throughput

Cumulative critical flow failures

Copyright Great Ormond Street


Utilisation of theatre time

38


Decrease in lost time



39


GP delay Trolley wait

Elective cancellation

Medicines reconciliation Boarding

Readmission

Waiting for care package

Waiting for consultant decision

Whole System Patient Flow Improvement Programme


Whole System Patient Flow Improvement Programme -

Workstreams

Established – ‘Spread’

• Enhanced Recovery

• Fracture Redesign

• Day Surgery

Developing

• Patient safety/Flow ‘Huddles’

• Criteria-led Discharge

Proof of Concept

•  Institute for Healthcare Optimisation (IHO) Variability Methodology


Phase 1: Building

Capacity & Capability

Establish Pilot Board Teams

Operations Management

Education

Patient Flow Assessment

Identify Patient Flow

Redesign Project

Phase 3: Evaluation &

Spread Disseminate

Phase 2 outcomes

Finalize Plan for Spread

(within hospital boards and at the national

level) Develop tools and materials

Execute Spread

Overall Project Summary

Phase 2: Implementation

Implement Chosen Redesign(s): 1.  Operating

Theater 2.  Surgical

Inpatient Flow

3.  Medical Inpatient Flow

Develop Scale-up Program

40


Phase 1: Building Capacity & Capability Prepare

Pilot Boards

NHS Forth Valley, NHS

Borders, NHS Glasgow and Clyde & NHS

Tayside

Identify clinical & administrative

staff to serve as expert resource

pool

Monthly Patient

Flow Project

4th Quarter:

Identify Pilot Service Areas

On-site conference

Prepare for implementation

Engage implementation

teams

Operations Management Education

1st Quarter:

Onsite Conferences

Background Readings

Interactive Lectures

Develop guided assessment programme

Patient Flow

Assessment

2nd and 3rd Quarters:

Regular webinars and workshops

Training in Flow Analysis

Guidance and resource

development


Phase 1 in NHS Forth Valley


NHS Forth Valley Approach

Advisory Group Big group, ensure engagement

Project Board Small group, strategic decisions

Core Team Project

Manager Programme

Support Data Analyst Clinical Lead

41


NHS Forth Valley Average Elective Inpatient

Admissions by day of week (01 Jan 2014 – 30 June 2014)


NHS Forth Valley Daily Elective & Non-Elective

Hospital Admissions (01 Jan 2014 – 30 June 2014)


NHS Forth Valley Approach

Guided patient flow assessment

Staff engagement at all levels

Maintained interest when little changing

Check what's already been tried or in development

Prepared for all possible changes before decisions made

42


Challenges


Key Lessons


Key Lessons

43


To conclude

!  Scientific managing variability in patient flow is absolutely necessary to increase overall hospital patient throughput while improving quality of care, patient safety and reducing nursing workload.

!  It requires rigorous data analysis, scientific management of operations, clinical and organizational behavior expertise.


References

http://www.ihoptimize.org http://www.ihoptimize.org/knowledge-center-publications.htm

Managing Patient Flow in Hospitals: Strategies and Solutions, Second Edition 2009. 166 pages. ISBN: 978-1-59940-372-4

http://www.jointcommissioninternational.org/Books-and-E-books/Managing-Patient-Flow-in-Hospitals-Strategies-and-Solutions-Second-Edition/1497


WWW.IHOPTIMIZE.ORG Quality and Safety Corner

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, ED boarding and others. Learn more »

Healthcare Cost Corner Hospital costs can be decreased by millions of dollars annually by adopting the Institute for Healthcare Optimization’s approach to managing variability in healthcare delivery. Learn more »

A Case Study How one hospital increased annual revenue by $137M, and avoided $100M in cost, while improving quality of care Learn more »

IHO Impact on Healthcare Performance Applying IHO Variability Methodology® in the hospital, outpatient and primary care settings demonstrated significant improvement in operational efficiency, quality of care as well as improvements in patient and staff satisfaction. Learn more »

ROI Estimator Estimate your hospital's ROI. Read More »

44


Some helpful links

http://www.ihoptimize.org/what-we-do-methodology-artificial-variability-healthcare-provider-administrators.htm

http://www.ihoptimize.org/what-we-do-methodology-artificial-variability-healthcare-provider-surgeons.htm

http://www.ihoptimize.org/what-we-do-methodology-artificial-variability-healthcare-provider-nurses-other-providers.htm

http://www.rwjf.org/pr/product.jsp?id=50488

http://www.ihoptimize.org/Collateral/Documents/English-US/MPF09-Pages24-25.pdf

http://www.ihoptimize.org/Collateral/Documents/English-US/Cincinnati_Childrens_Questionnaire.pdf

http://www.ihi.org/ihi/files/WIHI/WIHI_20091202_Patient_Flow.mp3

http://www.ihi.org/NR/rdonlyres/E632D3DD-CFA8-425A-A676-11AA11B354CA/0/ORInitiativeClip20090917.mp3


@jasonleitch

@peterlachman

@IHOptimization

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