lean six sigma practitioners making the transition to healthcare
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TRANSCRIPT
SAINTVINCENT
Lean Six Sigma Practitioners:Making the Transition to
Healthcare
Steve Osborn, CPHQ, CSSBBSaint Vincent Health Center
System
Saint Vincent Health Center
Beds 425
Admissions 18,500
ER visits 64,000
Westfield (NY) Memorial Hospital:
4 Beds, ER, 2000 Outpatient Visits
Saint Vincent Surgery Center – 7,900 outpatient surgeries
Saint Vincent Medical Group: 110,000 visits at 13 Primary Care Sites; 11 Surgical and Specialty Practices; ER Docs
Home Health Agency – 8400 visits
System: About 110 leaders
Saint Vincent Health SystemErie, PA
3
My Background
Education and Certification• Engineering BS (1980) and
MBA (1982) from Penn State• Started at Saint Vincent in
1990• MS in Healthcare
Administration in 1996• General Health Care Quality
Certification: CPHQ (1996)• Lean Six Sigma Black Belt:
Kent State University• CSSBB: KSU and ASQ (2009)
Hospital Career:• Medical Affairs: 1990 – 1999• Med Staff Quality: 1993-2010• Utilization Management and
Medical Record: 1993-1999• Quality Informatics: created
department in 1990’s• Joint Commission Oversight:
1996-2010• Infection Control and Patient
Safety: 2000 – 2005• Lean Six Sigma: 2006-2010• IRB/CME: 1996-1999; 2010
4
Presentation Summary
1. High level SIPOC of Hospitals• Review of Suppliers and Inputs, as they can contribute to
process variation• Overview of distortions created by insurance and device
makers/pharmaceuticals• Review of Outputs and Customers – who is the customer?
2. High level review of Hospital Quality systems and measurement problems
3. Implications for Lean Six Sigma program• Our history of LSS deployment• Thoughts on traditional LSS versus hospital LSS with case
study
5
Secondary Themes
• Provide viewpoint of Masters in Healthcare Administration (not MBA)
• More in depth in Quality Systems – too many LSS practitioners are isolated from the other quality functions
• My bias is from the Six Sigma methods, since they have the deepest roots in healthcare
• Our team consensus is to move toward use of TPS (lean) quality management approach
Part 1Hospital System SIPOC
7
Healthcare System SIPOC
• This model excludes Quality inputs, processes and outputs• It also is not intended to cover every supplier, major process (e.g.
accounting, fund raising, HR, credentialing), or customer• This is simplified to the hospital (but has application to other
healthcare providers (e.g. doctor offices, home health, etc.)
SUPPLIERS INPUTS PROCESS OUTPUTS CUSTOMERSTraining Influences Patient Entry Pay Checks
Age Disparity Registration Caring GratificationExperiences Assessments Workshop
Individual Differences Diagnostics Education/ResidencyAcute Problems Diagnoses Process Perceptions
Chronic Conditions Medical Care Clinical OutputsSocio-economics Other: education Education
Other Risk Factors Other: documentation Ongoing Care (Rx, PT)Health Status Discharge, transitions "Value"
Local Health Resources Coding, Billing, AR Change in Health StatusSociety Healthcare "System" Environment: Food Research/Knowledge
Payment Model Service, Engineering Bills InsurersQuality P4P HousekeepingProducts & Payments Suppliers
Pharmaceuticals
Employees
PhysiciansStaff
Communities
Patient
Suppliers
Patients
Insurers
Community
8
Purpose of SIPOC(and Hence My Method)
• The Analyze phase (in DMAIC) is the search for the critical variables causing variation in process outputs.
• The source of variation in an Output “must”be in the Supplier, Inputs or the Process (of a SIPOC)– Your process variation is likely to be more
obvious– Idea is to not forget all the possible Suppliers
and Inputs to your process– Fishbone root cause analysis forces
consideration of inputs (e.g. manpower, machines, etc.)
9
STAFFING
Staff Training Variation
• Physicians: 8 years of college and medical school followed by 3 to 8 more years of internship, residency and fellowship
• Pharmacy: 7 to 10 years of training• Nurse Practitioners and Physician Assistants: 2
year masters degrees• Nursing: 2 years (RN), BSN (4 years)
– Leadership: Desire for MSN; many are not
• High School (GED): Unit Secretary, Aides, housekeeping, transporters, etc.
11
Source: American Medical Association. (2009 Edition). Physician Characteristics and Distribution in the US.
350
300
250
200
150
100
50
0
50
100
150
200
250
300
350
400
450
500
1980 1990 2007
Under 35
35-44
45-54
55-64
65 & Over
Age
45
and
over
Age
und
er 4
4
Num
ber
of P
hysi
cian
s (T
hous
ands
)
Age Group
400
550600
Number of Physicians by Age1980, 1990, and 2007
12
0
Num
ber
of R
Ns
(Tho
usan
ds)
Age
und
er 4
0A
ge 4
0 an
d ov
er
AgeGroup
20s
30s
40s
50s
60s
1,000
500
500
1,000
1,500
2,000
1980 1990 2000 2010 (proj.) 2020 (proj.)
2,500
Source: Bureau of Health Professions, Health Resources and Services Administration. (1980-2004). Findings from the National Survey of Registered Nurses. Link: https://bhpr.hrsa.gov/healthworkforce/nursing.htm. 2010 and 2020 projections derived from The Lewin Group analysis of the National Sample Survey of Registered Nurses, 2000.
RN Workforce by Age Group1980 – 2020 (Projected)
13
Staffing Issues
• Aging population, increasing demand for healthcare services
• Aging workforce
• Expected staffing shortages (again)• Age generations (e.g. Baby Boomers versus Gen
X versus Gen Y)
• Trying to maintain team parity, when there are huge differences in education and status amongst team members
14
Analysis of One StaffingVariable: Education Type
MDDOAHP
120
100
80
60
40
20
0
ProvType
ARRIVE/OUT
Boxplot of ARRIVE/OUT
Project: ANOVA ON PROV TYPE.MTW; 4/14/2009 11:13:58 AM
1129680644832160
0.020
0.015
0.010
0.005
0.000
ARRIVE/OUT
Density
50.29 19.24 6473
45.02 18.77 19693
53.95 20.68 29360
Mean StDev N
AHP
DO
MD
ProvType
Histogram of ARRIVE/OUTNormal
Project: ANOVA ON PROV TYPE.MTW; 4/14/2009 11:10:10 AM
• Box Plot show variation between three provider types: DO (45 minutes), Allied Health Practitioner (50), and MD (54)
• N = 55,000 observations (office visits)• ANOVA P-Value = 0.000• High standard deviation, hence low R-Squared = 4.11%
15
Staff Variation as a Constant “Red X”
• ER Throughput – by ER physician
• Radiology Reporting – by radiologist and transcriptionist
• Cath Lab On Time Starts – by cardiologist• Meal Delivery – by meal delivery tech• Lab Use Per Patient – variation by ordering
physician shown to be not really significant, when adjusted for patient factors
16
Patients
17
Patient Inputs
Socio-economic – probably most profound– Patients presenting to ER’s and hospitals are not
random cross-section of American communities– Poor wealth is highly correlated with poor health– Health care literacy varies significantly
• Literacy: below basic – 14%; basic – 29%• Quantitative: below basic – 22%; basic – 33%
– Language• Non-literate in English – 5%
– Culture Impact - examples: deference to authority; process of dying and death
18
Patient Inputs (Con’t)
• The core input to healthcare – the Patient – is an incredibly complex system– Acute presenting problems: about 350 DRG groups– Major chronic conditions
• Significant: kidney, cardiac, pulmonary, others• Aging: neurologic, orthopedic, peripheral vascular
– Multiple medications (10+ is not unusual)– Average age of 64 (excluding deliveries/newborns)
19
Patient Inputs (Con’t)
• Risk Factors – the specific factors vary depending on output being measured (mortality, complications, infection, etc.)
• Risk Adjustment methodology is critical factor behind numerous report cards– Usually logistic regressions– Example:
• CMS Mortality and Readmissions• Some registries (e.g. STS for open hearts)• Vendors: HealthGrades, Top 100 Hospitals, US News
20
Example: Root Cause Analysisfor Surgical Site Infections
• Were able to measure 11 possible root causes• 3 of 6 statistically significant factors are related to patient
risk factors
Risk Increase
P- Value
1a Pt Condition: diabetes 71% 0.0031b Pt Condition: obesity 155% 0.0001c Pt Condition: smoking 58% 0.0036 Abx Choice (SCIP) 729% 0.0017 Abx Timing (SCIP) None -1.0008 Vagina Prep (Abd Hysters) None -0.3049 Skin Prep Agent Choice (all surg) 36% 0.291
F PACU Care 19b Temp Control (SCIP) None -0.83721c Excessive bleeding - Transfusion 98% 0.00022a Glucose Control (POD #1) 217% 0.04622b Glucose Control (POD #2) None -1.000
PROCESS POSSIBLE ROOT CAUSES
APre-op Patient
Condition
D Short Stay & ORDA
G Post-op Care
E OR
21
Community Inputs
All of our communities bring different resources and problems
• If you have watched “Jamie Oliver’s Food Revolution” on ABC, you now know that Huntington, WV is the fattest city, in the fattest state, in the fattest country in the world
• Counties vary greatly by economics, cancer rates, co-morbidities, etc.
• Hospital inputs (patients), therefore also vary widely
22
0
50
100
150
200
250
300
Diseases of theHeart
MalignantNeoplasms
CerebrvascularDisease
Chronic LowerRespiratoryDiseases
Diabetes Influenza andPneumonia
HIV Infection
Cause of Death
Dea
ths
per 1
00,0
00 P
opul
atio
n
White
Black
All Persons
Source: National Center for Health Statistics. (2008). Health, United States, 2008 with Chartbook on Trends in the Health of Americans. Hyattsville, MD.(1) Racial categories include individuals of both Hispanic and non-Hispanic origin.
(1)
(1)
Community Inputs:Example: Racial Disparity
23
Overweight
Obese
10%
20%
30%
40%
50%
60%
70%
1960-1962 1976-1980 1988-1994 2001-2004 2003-2006
Per
cent
of
Adu
lts A
ges
20-7
4
(2)
Source: National Center for Health Statistics. (2008). Health, United States, 2008 with Chartbook on Trends in the Health of Americans. Hyattsville, MD.(1) Data are age-adjusted to 2000 standard population.(2) Overweight includes obesity.
Community InputsExample: Over Time
24
• John Wennberg and others at the Dartmouth Institute for Health Policy and Clinical Practice
• Show widespread variation geographically – Starting in 1970’s and consistently through to today– Based on hospitals, beds, RNs, doctors– Procedures or patient days per capita
• Often called “Dartmouth Atlases”
• Conclusions – variations in use of healthcare is often not associated with variation in value
Community Health CareVariation in Resource Use
25
Example of Variation by Procedure
Based on 330 HRR’s (≅ MSSA) which combines all hospitals in that area. Hence true variation is even greater.
Fisher E S et al. Ann Intern Med 2003;138:273-287
Increases in Spending Did NotCorrelate with Increases Quality
27
Example of Variationin Expenditures
Dartmouth Atlases show variation in health care use in many measures of healthcare utilization
28
Health Care Variation andCommunity Norms
• Atul Gawande, “The Cost Conundrum –What a Texas town can teach us about health care”, New Yorker, June 1, 2009.
• Studied McAllen Texas, where Medicare costs are twice the national average at $15,000 per enrollee.– In 1992, it was $4900, about average
• Issues:– Doctors owned hospital and services– Hospital administrators did not know their
costs; don’t know long term outcomes – “It about the culture of money”
29
Simplified Healthcare SystemTwo Other Input Variable
MedicalPractitioners(Providers)
Insurers(including
Gov’t)Suppliers
Patients
http://www.pmforum.org/library/papers/2009/PDFs/aug/FP-ShlichterThomas-HealthcarePM.pdf
PharmaceuticalsAnd Devices;
Payments
Premiums
Payments
Care andProcedures
Risk Reduction
Co-pays &Out of pocket
30
Healthcare Insurance
• The role of healthcare insurance is fundamental to the healthcare industry
• Originally, consumers desired insurance to reduce risk of huge out of pocket expenses.
• However, insurance distorts the value proposition, so that healthcare is unlike most other consumer purchases (e.g. cars, TVs, gasoline)
31
Healthcare InsuranceDistortions
• First, the cost of insurance has now been shifted from individuals to either employers or government
• Second, poor health (and generally higher costs) can be a results of poor genetics and congenital birth defects, bad luck, and poor personal health behaviors
• Third, the small level of co-pays can encourage greater use by consumers. (If someone else paid for 90% of your car purchase, wouldn’t you buy a new car more often?)
• Fourth, maintenance of healthcare has the least insurance coverage, but can dramatically change the long term use and costs of healthcare
32
Pharmaceuticals and Device Manufacturers
• Behind the scenes, the pharmaceuticals and device makers have experienced huge growth over the last three decades
• Insurance and economics plays a big role– Consumers don’t foot much
of the bill– Ordering doctors aren’t
accountable
33
Supplier Value Proposition
Medical Device Example:• You are 70 years old, and getting a
hip replacement. One model will last 30 years, a second product only 20. Which do you want? How much extra do you pay?
• You are the orthopedic surgeon. Which product do you insist that your hospital carry? What impact does this decision have on your practice?
Consider similar example for two drugs
34
Source: Centers for Medicare & Medicaid Services, Office of the Actuary. Data released January 5, 2010.(1) CMS completed a benchmark revision in 2006, introducing changes in methods, definitions and source data
that are applied to the entire time series (back to 1960). For more information on this revision, see http://www.cms.hhs.gov/NationalHealthExpendData/downloads/benchmark.pdf.
(2) Expressed in 1980 dollars; adjusted using the overall Consumer Price Index for All Urban Consumers.
Total Prescription Drug Spending1980 – 2008
35
Source: Centers for Medicare & Medicaid Services, Office of the Actuary. Data released January 5, 2010.(1) CMS completed a benchmark revision in 2006, introducing changes in methods, definitions and source data
that are applied to the entire time series (back to 1960). For more information on this revision, see http://www.cms.hhs.gov/NationalHealthExpendData/downloads/benchmark.pdf.
Spending for Prescription Drugs1988 – 2008
36
Hospital Outputs:Who Are the Real Customers?
OUTPUTS CUSTOMERSPay Checks
Caring GratificationWorkshop
Education/ResidencyProcess Perceptions
Clinical OutputsEducation
Ongoing Care (Rx, PT)"Value"
Change in Health StatusResearch/Knowledge
Community
Employees
Physicians
Patient
37
Are Employees Customers?
• The American Hospital Association (and state associations) make great efforts to show the economic impact of hospitals on communities– Often the Hospital is the largest employer in town
• But, the hospital was not created to provide employment, pay checks and benefits– No more than GM and Toyota exist to provide
employment– Ultimately, employee involvement is a side benefit of
quality programs, but again not the reason for these efforts
38
The Physicians’ Workshop
• Hospitals in the US were originally “closed” to all physicians except a small “house” staff.
• As of 1873, only about 2% of physicians had hospital privileges. And they could not charge patient fees, since hospitals were completely charity operations
• By 1930, >80% of physicians had hospital privileges, and charging fees was the norm
• Since that time, from the medical (and legal) perspective, hospitals became the physicians’ “workshop”– Efforts by AMA and particularly in some states to require
hospitals to take all qualified medical staff
39
So, Are Physicians Customers?
• Where does this idea originate? – Because “doctors bring us our patients”? That makes them a supplier!!
• However, in many processes, the physician is the critical customer.– Example: outpatient radiology results– In many cases, STAT results to the ordering physician for
inpatient care directly impacts patient outcomes– In the first example the physician is an “external” customer, but in
the second they are “internal”
• Ultimately, just as with employees, hospitals are not in place for the benefit of the medical staff– This is surely the intent of Stark anti-referral laws
40
Sociology of Physiciansand Health Care Workers
• Professional autonomy has been a long term ideal of organized medicine– As medical residencies in many ways parallel the middle ages’
craftsmen training, physicians practice based on how they were trained, and will defer to another’s different training approach
– “Only doctors can judge other doctors”– This thinking has often been extended to other healthcare
occupations (nursing, therapists, etc.)• Often this is expressed as a cultural resistance to
standardization– “I’ve passed meds this way for 20 years and never had a
medication error”• Likewise, experiential variation predominates –
unfortunately 1 bad case can influence all future care by some clinicians
41
Standardization asHistorical Development
• First major attempts were in the 1990’s – Often called practice guidelines or “clinical paths”– Were derided by some as “cook book” medicine, and
taking the “art” out of medicine
• As of 2010, the medical world has changed– “Evidenced based medicine” is ascendant– One of the six core competencies required by the
ACGME (residency accrediting agency) is “systems thinking”
42
Control versus Influence
• Many Quality Initiatives hinge on cooperation by physicians
• Though outside of our “control”, they are almost never outside our “influence”
• Make visible their impact– Compute costs of differences– Post variable results
• Probably no more resistant to change than other hospital staff
• Remember WIIFM (What’s In It For Me)
Sphere ofControl
Sphere of Influence
43
Is the “Community”a Customer?
• Most hospitals in the country are not-for-profits or local community owned
• Majority of hospitals are also small– 32% < 50 beds– 53% < 100 beds
• “Community” is probably in the name, mission statement, or vision of >50% of all US hospitals
44
Communities asCustomers
• Problems with Communities as customers– By legal precedent, we are to serve the needs of each individual
patient– It is hard to measure community health– We are not paid to improve community health
• In many Western countries, Public Health drives healthcare delivery design– This approach has been largely thwarted by medicine, hospitals
and suppliers here in the US
• Increasing legal requirements for not-for-profits to meet community need standards– Health care reform bill require NFP Hospitals to complete a
“Community Needs Assessment” every three years
45
The Patient is THE Customer
• Employees are not the customer• Physicians bring in patients, but are not the
customer• The Community as customer is growing• Ultimately, the PATIENT is the customer
– There is definitely a growing recognition, if not a consensus, by healthcare leaders
– There is some competitive factors at play (in some areas, the best quality hospitals will thrive)
– All quality systems (Deming, Six Sigma, TPS) will always come back to this
46
Healthcare System SIPOC
SUPPLIERS INPUTS PROCESS OUTPUTS CUSTOMERSTraining Influences Diagnoses Pay Checks
Age Disparity Medical Care Caring GratificationExperiences Other: education Workplace
Individual Differences Other: documentation Education/ResidencyAcute Problems Patient Entry Process Perceptions
Chronic Conditions Registration Clinical OutputsSocio-economics Assessments Education
Other Risk Factors Diagnostics Ongoing Care (Rx, PT)Health Status Discharge, transitions "Value"
Local Health Resources Coding, Billing, AR Change in Health StatusSociety Healthcare "System" Environment: Food Research/Knowledge
Payment Model Service, Engineering Bills InsurersQuality P4P HousekeepingProducts & Payments Suppliers
Pharmaceuticals
Employees
PhysiciansStaff
Communities
Patient
Suppliers
Patients
Insurers
Community
• Consider all suppliers and inputs to your quality projects• Find “wins” for employees, physicians, and the
community, but the Patient is the ultimate customer
Part 2Hospital Quality
•Quality History
•Quality Measurement•Quality Regulators & Agencies
•Quality Department Organization
48
Quality HistoryMeasurement; Standards
• Florence Nightingale (1854): Military & hospital mortality• Ernest Codman (1910): “End Results Idea”
– Track results in OR. Update status in one year. Publish publicly.
• Avedis Donabedian (1966): Quality Domains• John Wennberg (1983): geographic variation in
healthcare use• Robert Brook and Mark Chassin (Rand Corporation)
(1987): consensus criteria for surgery• Paul Ellwood (1988): “Managed Competition” and
outcomes research
49
Quality HistoryTQM; Patient Safety
• TQM/QI/CI Movement: 1989-95– Donald Berwick; Nelson, others– HCA Hospitals: FOCUS-PDCA (Batalden)– Team Handbook, 7 Basic Quality Tools
• Patient Safety: 2000-present– Institute of Medicine (IOM): To Err Is Human (2000); Crossing the
Quality Chasm (2001)– Reason: Human Factors; Swiss Cheese Model– Lucian Leape; Peter Pronovost– Robert Helmreich; J Bryan Sexton – aviation safety– Kaiser Permanente: Lawrence, Leonard - SBAR– VA System: FMEA
• Institute for Healthcare Improvement (IHI) - Berwick
Quality HistorySix Sigma; Lean
• Six Sigma in Healthcare– Six Sigma was launched by Commonwealth Health (Bowling
Green, Kentucky) in 1998 and Mount Carmel Health System (in Cincinnati) in 2000.
– Used across many hospitals; no clear epicenters
• Lean in Healthcare– Virginia Mason Medical Center (Seattle) with Gary Kaplan, MD as
CEO was the first to adopt The Toyota Production System in 2001.
– The Pittsburgh Regional Health Initiative began promoting TPS in2001.
– Thedacare (Appleton, WI) started in TPS journey in 2003. Created Thedacare Center for Healthcare Value; collaborating with the Lean Enterprise Institute
51
Hospital quality can be measured in three areas:
1. Structure – the resources assembled to deliver care, such as physical equipment and units, staffing, capacity, etc.
2. Process – the ability to deliver a consistent, error-free care process
3. Outcomes – the valued results of care (such as mortality, complication rate)
Avedis Donabedian, 1950
Quality MeasurementTheory
52
Care of Heart Attack Patients
Structure Process Outcomes
•Hospital locations•Pre-Hospital Transport•Presence of Cardiac Cath Lab•Presence of Cardiac Care Unit (CCU)•Presence of various cardiology specialists
•Transport time•Time to EKG (diagnosis)•EKG to Cath Lab time•Early aspirin therapy•Smoking and dietary education•Discharge medications
•Mortality rate•6 month mortality•Cath lab complication rate•1 year re-stenosisrate•Patient Satisfaction•Total Charges
Donabedian Model Example
53
Comparing OutcomesRisk Adjustment
When comparing outcomes (e.g. mortality rates), there needs to be adjustment for patient differences
• Example earlier of impact of diabetes, smoking and obesity on surgical site infections
• The risk should be accessed prior to the care being compared
• Most common sources:– Clinical record abstraction– Coding: DRG and Diagnosis codes
• Present on Admission Diagnosis coding began in September 2008
– Billing Data: Age and Gender; Insurance; Admission Source
• Risk may be related to a use factor: central line “days”
54
Coding and Billing is often substituted for the Medical Record Review which is not equivalent to
the actual patient and care
MedicalRecord
Patient Care
Signs andSymptoms
Diagnostics
Diagnosis
Therapy
Care Results
Billing System•Demographics
•Charges•Outcomes
MedicalRecordsCoding
+≠≠
Surrogates for theClinical Experience
55
• The record does not completely describe the patient care experience– Physician, nurses and staff incompletely document– Staff use different language for same clinical conditions
• It’s impractical to Use Medical Record for Summarizing Patient Care– Medical record averages 150 to 200 pages– Possibly Can’t Read Medical Record
• At hospital, medical record is still the key document for reviewing quality of care concerns (e.g. medical staff peer review; sentinel event)
Medical Record Problems
Medical Record Coding
PrimaryDiagnosis &Procedure
SecondaryDiagnoses &Procedures
DiagnosisRelated Group
(DRG)+ =
25 Major Diagnostic Categories (MDC’s) Surgical and Medical DRGs746 DRGs in approximately 350 groupings
CC = Co-morbidity or ComplicationMCC = Major Co-morbidity or Complication
Type ICD-9 CodesNon-CC 8,232CC 4,221MCC 1,096Total 13,549
57
• Medical Record coding is limited to using physician documentation– Doctor must use specific language to count (↓K ≠ hypokalemia)– Coders not allowed to “diagnose” (Ex. a positive culture plus
physician prescribed antibiotics ≠ Infection, unless stated by physician)
• Codes are combined to create a Diagnostic Related Group (DRG), which has become the “de facto” method for hospital payment– More Complications and Co-morbidities = “higher” DRG = higher
hospital payment– Creates bias to “optimize” (the legal term for over-code)
• Individual coder and hospital quality varies widely
Coding Problems
58
Impact of Coding on Average Mortality & Payment
CC = Co-morbidity or Complication; M = Major
• Respiratory Infections: most commonly aspiration, Klebsiella, Staph or Legionella pneumonias
• Pneumonia: generally all other pneumonias
PneumoniaRespiratory Infections Pneumonia
Respiratory Infections
Without CC 0.7% 3.2% 4,896$ 6,961$ With CC 1.5% 3.9% 6,883$ 10,253$ With Major CC 6.3% 13.6% 9,921$ 14,133$ Total 1.5% 8.0% 6,878$ 11,453$
Average Mortality Reimbursement
59
Registries as AlternativesFor Quality Measurement
• Registries have developed that are designed to resolve some of the problems of coding
• Most are focused on a specific subset of care (open heart surgery, NICU, etc.)
• They require abstraction of specific information from the medical record that are more germane to measuring quality (in their domain) and adjusting for risk
• Often proprietary; rarely in the public domain
Mandatory
1. Atlas →PHC4 (State Outcomes)
2. CMS Core Measures (Inpatient and Outpt)
3. Joint Commission Core
4. CMS Carotid Stent
5. ACC-NCDR – Cath Lab
6. ACC-NCDR/CMS - ICD
7. STS – Open Heart
8. GTWG – Stroke
Voluntary1. CMS/Premier HQID
2. Door-to-Balloon Alliance
3. Vermont-Oxford –NICU
4. NDNQI – Nursing
5. Project Impact –Critical Care
6. PQRI-Physician Office
7. Blue Cross P4P
8. UPMC Healthcare P4P
Registries at Saint Vincent
61
CMS Public Reporting
• Core Measures (Process): AMI, CHF, PN, SCIP, Outpatient SCIP– Coming: VTE, Stroke, Open Heart (STS),
• Outcome Measures: (Risk Adjusted)– Mortality Rates: AMI, CHF, PN– Readmission Rates: AMI, CHF, PN– Coming: Nursing Care (NDNQI), Patient Safety events (HACs)– Possibly: AHRQ PSI and IQI metrics
• Patient Perceptions: HCAHPS Survey• FY2011?: ER Throughput Times, Infections, COPD,
Diabetes, Cancer (ACOS)• Value Based Purchasing: Formula for providing bonus or
penalty payments based on quality
62
Proprietary PublicQuality Rating Systems
• Although CMS measures are substantial (and growing), CMS does no aggregation, and hence it is hard to make value judgments
• Numerous agencies and private enterprises have taken national data, plus added their own information, to create hospital comparative ratings.– HealthGrades– Thomson Reuters 100 Top Hospitals– US News and World Reports– Others: Subimo, Delta Group CareChex, Consumer Reports
• Hospitals must pay in order to market the ratings
63
CMS as Regulator
• Regulation– Conditions of Participation (COPs) – rules all hospitals
have to follow• More prescriptive and arcane than Joint Commission• Hot Buttons: restraints, patient complaints/grievances
– Typically CMS pays state DOH’s to do surveys on non-accredited hospitals
• Fraud and Abuse – CMS has a whole other system. See your corporate compliance officer. OIG uses multiple methods:– Recovery Audit Contractors (RACs)– Anti-referral (Stark Law) compliance– Etc., etc.
64
State Licensing
State Department of Health (DOH)• Issue state regulations • Provides license• Survey hospitals (frequency depends on state)• Survey on behalf of CMS• Manage patient complaints• Administer other functions: CON• May manage data reporting (may be separate
agency) – patient safety, infection control, outcomes (mortality)
65
• 1910: Ernest Codman – Proposes “end results system of hospital standardization”
• 1917: American College of Surgeons (ACS) develops the Minimum Standards for Hospitals
• 1918: Only 89 of 692 hospitals pass their first survey• 1951: ACS, American College of Physicians, the AMA
and the AHA create the Joint Commission on Accreditation of Hospitals (JCAH) with primary purpose is to provide voluntary accreditation
• 1965: Medicare – Hospitals accredited by JCAH or the AOA are “deemed” to be in compliance with the Medical Conditions of Participation (COPs)
The Joint CommissionHistory
66
• 1986-2007: The Joint Commission under Dr. Dennis O’Leary dramatically increased scope adding a for-profit consulting arm, an international division and expanding into many sectors of healthcare– 1987: Accreditation extends beyond hospitals. Name change to
Joint Commission for Accreditation of Healthcare Organizations (JCAHO)
• 1997: Beginning of reporting “Core” measures• 2002: Survey process moves to patient tracers versus
previous department “white glove” reviews• 2007: Lost automatic deeming; had to re-apply to CMS• 2007: Changes name to “Joint Commission”• 2008: New president: Mark Chassin, MD
The Joint CommissionHistory (Continued)
67
Joint Commission
• Hospital Accreditation– Voluntary– Provides “deemed” status with CMS – Surveys to a higher standard (“Gold Standard” for
hospitals)
• Disease Certification – Stroke, CHF, CKD, VAD, etc.– Ensures quality of program– Marketing value
68
1. Hospital Manual: Prescriptive Standards• 18 Chapters; 286 Standards; 1500 Elements of
Performance
2. Periodic Performance Review (PPR): Annual Self Assessment of all 1500 EP’s
3. Hospital Survey– Unannounced Survey – 18 to 39 months– Issue “Requirements For Improvement” (RFI’s) that
require “Evidence of Standards Compliance” (ESC) that may need “Measurement of Success” (MOS)
4. Require submission of “Core Measures”
Joint Commission Program
Typical HospitalQuality Functions
Department RolesQuality Compliance JCAHO, State DOH and CMS regulatory complianceQuality Informatics Electronic data collection; chart abstraction; data
management, analysisMedical Staff Quality
Support peer review; complication and outcome tracking (OPPE for JCAHO)
Nursing Quality Regulatory compliance; nursing measuresInfection Control Surveillance and reductions of infectionsPatient Safety Reduce incidents (falls, med errors) and sentinel
eventsCase Management Management of high intensive patients; real time EBP
complianceUtilization Mgmt Facilitate insurer payments; reduce excessive LOSSatisfaction Manage patient satisfaction collection and improveLean Six Sigma Train in Lean, Six Sigma or PI. Mentor projects. Lead
QI projects.
FTEs: 5.0 11.5 19.5 (exc. SW) 4.0 2.0 1.0
Total of 43 FTE’s
• Medical Staff Quality
• JCAHO Readiness
• Outside Agency Liaison
• Lean Six-Sigma Deployment
• Case Management
• Utilization Management
• Discharge Planning/ Social Work
• Data capture, abstraction, entry
• Data analysis• Database
administration
• IT/EMR Integration Liaison
• Patient Safety
• Infection Control
• DOH Compliance
Saint Vincent Coordinated“Quality Department”
CEO
CQO CNO
VP, Quality Compliance & Improvement
DirectorQuality Informatics
DirectorCare Coordination
Director/PSOPatient Safety &Infection Control
Senior VPMarketing
DirectorPatient Relations
Senior VPHR
DirectorOrg Development
& Educ Svcs
CMO
• Patient Satisfaction
• Patient Relations
• Complaints & Grievance
• Leadership Training
• PI Training
• Staff Education
Part 3Implications for Lean Six Sigma
Practitioners
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Saint Vincent’s LSS Program:Deployment Summary
• FY07: 12 hours of LSS training for all leaders• FY07 and FY08: Departments selected “LSS” projects. Internal
black belts available for consultation. Project quality varied great• FY09: Department LSS projects done through PDSA class format• FY09-10: Stopped Green Belt class; shift to Black Belt projects
DeplymtYear Fiscal
Black Belt
Green Belt
Dept LSS Comment
0 FY05 RFI; Hire Consultant1 FY06 4 Hired 2 Black Belts2 FY07 14 42 Consultant GB Course3 FY08 10 16 Internal GB Course4 FY09 8 10 PDSA Class5 FY10 11 Patient Flow Focus
Projects Started
73
Lean Six Sigma Project Results
LSS Projects: FY06 to FY09
58%
20% 23%
0%10%20%30%40%50%60%70%
Met Goal Some Impmt Not Started orLittle Impmt
104 projects started. 8 closed at baseline. 4 still open.Total of 92 projects with results.
74
Saint Vincent’s LSS Program:ROI Results
• Project returns tracked for 3 years, starting with Control Phase• Hard returns at 2 to 3 times LSS costs in last two ½ years• Community benefits significant ($880,000 of $2.8 million total)
-$50,000
$0
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
Q1
FY
06
Q3
FY
06
Q1
FY
07
Q3
FY
07
Q1
FY
08
Q3
FY
08
Q1
FY
09
Q3
FY
09
Q1
FY
10
Hard Soft Actual Exp Comm Savings
75
Self AssessmentEntering FY11
• Quality is really still only Project Based
• Lack of embedding quality culture; no Continuous Improvement mentality
• No real daily “quality management” system• Standardization is building block of both Lean
and Six Sigma, and we don’t teach this well
• Students want short courses in half-day format
76SVHS: Mission, Vision, Values, Strategic Plan
STANDARDIZATION
PROCESSFLOW
PROBLEMSOLVING
CONTINUOUS IMPROVEMENT: Pursuing Perfection
HumanCentered
Work
EXCELLENCE IN HEALTHCAREQuality, Satisfaction, Value
VOICE OF THECUSTOMER
Our “House of Quality”
77
Developing Course Curriculum
• E-Learning Modules (90 Minutes)– Voice of the Customer– Change Management
• Seminars in two half-day format with project work– Standardization (including Audits, rounding, visual
metrics; role of middle and senior managers)– Flow (VSMs, Spaghetti Charts, Level Loading)– Problem Solving (using PDSA/A3)
78
Healthcare Six Sigma Book of Knowledge
• Generally not Relevant to Healthcare:– Full Gage R&R (possibly a Gage R)– Sub-group sampling: R charts– Capacity Analysis with Bilateral Specifications
• Hence no need for CpK, PpK; nor Short term vs. Long term issues
– DOE
• This is 70% of the content that separates Six Sigma from traditional TQM– Therefore, I think there should be a completely
different BoK and curriculum for healthcare
X
79
Healthcare Lean Book of Knowledge
• Problems with Lean in Healthcare– ER and Inpatient care is closer to a custom
engineering firm, than a mass-producer– Inventory focus, SMED, TPM– Fixation on Toyota roots; Japanese terminology– Failure to appreciate existing quality methods
• What Healthcare Needs from TPS/Lean– Management roles (they view quality like accounting
or supply chain or any other non-core function)– CI fixation; Culture
80
LSS Book of Knowledge: Key Areas
• Process mapping, swim lanes, value stream mapping
• Demand versus Capacity of services (e.g. ER, registration, nursing)
• Statistics: t-test; Chi-Square; ANOVA• Taguchi/Robust Process Design
• Regression Analysis– Control for noise variables– Logistic regression to model mortality & infections
81
Basic Quality Model
The basic quality model suggests that variation in Inputs or the Process will result in variation in the Outputs
INPUT PROCESS OUTPUT
82
Taguchi Model
In the Taguchi model, the noise factors are Input Variables that also influence the output, but are considered to be nonnon--controllablecontrollable
SIGNAL(Controlled)
PROCESS OUTPUT
NOISEFACTORS
83
Noise Factors andRobust Process Design
• Many factors in healthcare are not controllable (in the short run) by the process owners– Patient variation– Community variation
• Taguchi suggests designing processes to work across a wide range of inputs in the non-controllable (or noise) factors– For example, our patient medication reconciliation
should work regardless of the patient’s age, language, economic status, admission source, etc.
84
Case Study: Lab Use/PatientRoot Cause Hypotheses
Cause Class Importance Measurable
1 LOS Very High Yes2 DRG High Yes3 Co-Morbidities Low Yes4 Hospitalist (Y/N) Very High Yes5 Attending Service Medium Yes6 Attending Name Low Yes7 Admit Thru ER High Yes8 Hospital Transfer Low Yes9 Critical Care Days Low Yes
10 Legal Environment Low No11 Regulatory Requirements Low No
Environment
POSSIBLE ROOT CAUSES
Manpower
Patient("Material")
Pt Flow("Process")
85
Case Study: Lab Use/Pt DayVariation by Discharging Doctor
901181
701011
651083
611160
504126
504092
501247
445312
445288
445270
445254
445148
441238
441121
392670
391748
311233
311217
33779
31179
31161
31062
31047
31013
13920
13086
11361
11346
11262
11171
40
30
20
10
0
Attend Phys
Tests Per DayBoxplot of Tests Per Day
Project: Untitled; 4/9/2010 4:19:22 PM
ANOVA: P-Value = 0.000; R-Sq = 27.3%
86
Case Study: Lab UseSingle Variable Results
Cause Class ControllableSingle
Factor R Sq1 LOS No 39.1%2 DRG No 39.3%3 Co-Morbidities No 43.1%7 Admit Thru ER No 0.2%8 Hospital Transfer No 0.6%9 Critical Care Days No 13.2%4 Hospitalist (Y/N) Yes 0.8%5 Attending Service Yes 9.8%6 Attending Doctor (Name) Yes 27.3%
Pt Flow("Process")
POSSIBLE ROOT CAUSES
Manpower
Patient("Material")
Results using ANOVA or T-Test singly
87
Case Study: Lab UseRegression Results
• Best Multiple Regression: DRG, LOS, Critical Care Days, 5 secondary codes (all factors P-Value = 0.000)– Adj R-Sq = 65.6%– These” noise” factors are non-controllable by the LSS
Team
• Add Attending Physician to above Regression:– Adj R-Sq = 68.1%– Physician adds 2.5% to model predictiveness– Is this a significant factor to pursue as a “Red X”?
LSS: Ongoing JourneyNot a Destination
• Healthcare is a very diverse business, with enumerable system complexities
• It is hard to measure quality in healthcare• There is probably no other industry in so
much need for improvements in quality as is healthcare
• Traditional Six Sigma or Lean does not translate well to healthcare
• Deployment will vary depending on willingness to tackle culture early versus showing “wins” from improvement
• Have patience and enjoy the journey
89
Questions