patient safety monitoring initiative - · pdf fileinstitute of medicine’s definition:...

Patient Safety Monitoring Initiative

Introductory forum

Assumptions

No single indicator of quality

Indicators are indicators not absolutes

Look for poor and exceptional performance

Need to measure to manage

ContextHistoric- focus on activity descriptors how many, how much, how long

Now- lots of interest in clinical performance

Rapid rise of:

Performance indicatorsData sources

Aims of the initiative

Support monitoring

Identify opportunities for improvement

Using existing data

Aims of the dayIntroduce

What PSMI is

How the indicators and charts are created

How to use them

How we got here

Dr Vijaya Sundararajan

Steven McConchie

Agency for Healthcare Research Quality

and PSI Development


Overview

Administrative data and the developmentof AHRQ

Previous quality indicator sets

The Patient Safety Indicators

Administrative data1970-1980s

Administrative data and computers become widely available

Studies show “startling small area variationsin health care and practice patterns”

The outcomes movement is born.

AHRQ1989

The Agency for Health Care Policy and Researchis created (among other things) to promote the use of administrative data in determining outcomes, effectiveness and appropriateness of care

Overview

Administrative data and the developmentof AHRQ



Complications screening algorithmIezzoni, et al: First systematic exploration of the use of administrative data in quality and patient safety research

ICD-9-CM

27 potentially preventable in-hospital complications

Failure to RescueSilber, et al (1992) and Needleman (2002)

FTR is the rate of death among patients who developed pneumonia, shock or cardiac arrest, upper gastrointestinal bleeding, sepsis, and deep venous thrombosis in hospital

HCUP PSIs

1990s

Initial AHRQ attempt to support the Healthcare Utilisation Project

33 original QIs

To err is humanIOM 1999 report calls for medical error reporting systems & prompts renewed interest in tools for patient safety research that takes advantage of administrative data….

Readily availabile

Inexpensive

Computer readable

Encompass entire populations

OverviewAdministrative data and the developmentof AHRQ



Patient safety

Institute of Medicine’s definition:

“freedom from accidental injury due to medical care, or medical errors”

Medical error

“the failure of a planned action to be completed as intended or the use of a wrong plan to achieve an aim…[including] problems in practice, products, procedures and systems”

The PSIs

2002

Collaboration between AHRQ and the Evidence Based Practice Centre at the University of California San Francisco and Stanford University

5 step process

Literature review

Candidate list of indictors developed and their properties tabulated

326 articles from the Medline search.

A total of 34 indicators were retained.

Coding reviewExperts in ICD-9-CM codes reviewed each code for accuracy of capturing the complication and population at risk. In some cases, additional codes or other refinements to the indicators were suggested based on current coding guidelines.

Structured review by clinicians

RAND/UCLA Appropriateness Method

Initial independent assessment of each indicator by clinician panelists using an questionnaire

Conference call among all panelists

Final independent assessment by clinician panelists using the same questionnaire.

Face validityDoes the indicator capture an aspect of quality that is widely regarded as important and subject to provider or public health system control?

Consensual validity expands face validity beyond one person to the opinion of a panel of experts.

PrecisionIs there a substantial amount of provider variation that is not attributable to random variation?

Minimum biasIs there either little effect on the indicator of variations in patient disease severity and comorbidities, or is it possible to apply risk adjustment and statistical methods to remove most or all bias?

Construct validityDoes the indicator perform well in identifying true (or actual) quality of care problems?

Fosters real quality improvementIs the indicator insulated from perverse incentives for providers to improve their reported performance by avoiding difficult or complex cases, or by other responses that do not improve quality of care?

ApplicationHas the measure been used effectively in practice? Does it have potential for working well with other indicators?

Empirical testing

Statistical tests to examine precision, bias, and relatedness of indicators

Complications of Anaesthesia (PSI 1)

SourceOriginally proposed by Iezzoni as part of CSP

Compared with original definition, PSI excludes

poisoning due to centrally acting muscle relaxants accidental poisoning by nitrogen oxides

PSI includespoisoning by other and unspecified general anestheticsexternal cause of injury codes for “endotracheal tube wrongly place during anesthetic procedure”adverse effects of anesthetics in therapeutic use.

Panel ReviewConcerns

the frequency of coding of these complications (use of e-codes voluntary & varies widely among providers)

some of these cases would be present on admission (e.g., due to recreational drug use)

events assigned to the code for incorrect placement of endotrachialtube: true misplacement does represent medical error, but they were skeptical about whether this code would be limited to those situations

Ideally, this indicator would be used with a coding designation thatdistinguishes conditions present on admission from those that develop in-hospital.

Literature ReviewFocused on the validity of complication indicatorsResults

indicate no published evidence for the sensitivity or predictive value of this indicator based on detailed chart review or prospective data collection. no published evidence for this indicator that supports the following constructs:

that hospitals that provide better processes of care experience fewer adverse events; that hospitals that provide better overall care experience feweradverse events; and that hospitals that offer more nursing hours per patient day, better nursing skill mix, better physician skill mix, or more experienced physicians have fewer adverse events.

Empirical analysis: reliabilityThe signal ratio

measured by the proportion of the total variation across hospitals that is truly related to systematic differences (signal) in hospital performance rather than random variation (noise)is 75.7%, suggesting that observed differences in risk-adjusted rates likely reflect true differences across hospitals.

The signal standard deviation is 0.00187indicating that the systematic differences (signal) among hospitals is lower than many indicators and less likely associated with hospital characteristics.

The signal sharea measure of the share of total variation (hospital and patient) accounted for by hospitals. The lower the share, the less important the hospital in accounting for the rate and the more important other potential factors (e.g., patient characteristics. is 0.00563also lower than many indicators.

Empirical analysis: minimum biasAfter assessment of the effect of age, gender, DRG, and comorbidity risk adjustment on the relative ranking of hospitals compared to no risk adjustment &measurement of

the impact of adjustment on the assessment of relative hospital performance,

the relative importance of the adjustment,

the impact on hospitals with the highest and lowest rates, and

the impact throughout the distribution.

The detected bias is low, indicating that the measure is

likely not biased based on the characteristics observed.

Empirical analysisComplications of Anaesthesia

generally performs well on several different dimensions, including reliability, bias,

relatedness of indicators, and persistence over time.

AHRQ PSIs• Complications of anaesthesia • Death in low-mortality DRGs• Decubitus ulcer • Failure to rescue • Foreign body left during procedure • Iatrogenic pneumothorax• Selected infections due to medical

care • Postoperative hip fracture

postoperative haemorrhage or haematoma

• Postoperative physiologic and metabolic derangements

• Postoperative respiratory failure• Postoperative pulmonary embolism

or deep vein thrombosis • Postoperative sepsis • Postoperative wound dehiscence • Accidental puncture or laceration • Transfusion reaction• Obstetric trauma – caesarean

delivery • Obstetric trauma – vaginal with

instrument • Obstetric trauma – vaginal without

instrument • Birth trauma – injury to neonate

Translation process

Steven McConchie

TranslationMapping

Programming

Check

Refinement

Testing

Adaptation/Improvement

Morning Tea

What the indicators are

Refinement of Codes Jennie Shepheard

Numerator / denominator & codes

What VAED is Andrew Clarke

Programs Dr Vijaya Sundararajan

What the VAED is

Andrew Clarke

What is an Indicator “An indicator is

something that helps you understand where you are,

which way (how) you are goingand how far you are from where

you want to be.”

The VAED Victorian Admitted Episode Dataset

“The VAED comprises demographic, clinical, and administrative information

for every admitted episode of care occurring in Victorian acute hospitals.”

The VAED Manual 18th Edition


Demographiccountry of birthdate of birthgiven name/smarital statuspostcodegendersurname


Clinicalmajor diagnostic categories (MDCs)

Major diagnostic categories (MDCs) are 23 mutually exclusive categories into which all possible principal diagnoses fall.

The diagnoses in each category correspond to a single body system or aetiology, broadly reflecting the speciality providing care.


Clinicaldiagnostic related groups (DRGs)major diagnostic categories (MDCs)diagnosis codesprocedure codescare type


Administrativeadmission informationbiographical informationseparation informationaccount information

Uses of the VAED

Morbidity Monitoring

Casemix Funding

Performance Measurement

National Health Information Agreement

Refinement of Numerator & Denominator

Jennie Shepheard

Complications of AnaesthesiaNumerator: Specific codes representing poisoning by anaesthetic agent and external cause codes for adverse effects in therapeutic use (secondary codes only)

Denominator: Originally all surgical discharges >18 or MDC 14. Defined by list of OR codes and Surgical DRGs

Exclusions: Originally extensive list covering self harm and drug dependence or abuse

Death in Low-Mortality DRGsNumerator: No mapping of code involved

Denominator: No mapping of codes involved

Exclusions: Extensive lists provided as appendices to define trauma, immunocompromised and cancer states.

In-Hospital FractureNumerator:Originally only post-operative hip fracture defined by fractured neck of femur codes

Denominator: Originally all surgical patients defined by OR procedures and surgical DRGs

Exclusions: Extensive exclusions designed to eliminate fracture present on admission and cases where patient would be vulnerable to fall

Postoperative Haemorrhage or Haematoma

Numerator:T81.0 plus code for control of postoperative haemorrhage. Other haemorrhage codes not included.

Denominator:Anaesthetic procedure code eliminates complicated denominator logic and includes all eligible cases

Exclusions: ECT would be captured by denominator codes decreasing the rate

Postoperative Deep Vein Thrombosis or Pulmonary Embolism

Numerator:Easily mapped. Iatrogenic PE code removed from classification. Obstetric codes included

Denominator:Anaesthetic procedure code eliminates complicated denominator logic and includes all eligible cases

Exclusions: ECT would be captured by denominator codes decreasing the rate.

Obstetric Trauma – Vaginal and Caesarean Delivery

Numerator:Originally 3 PSIs that used same set of codes

Denominator:All vaginal or caesarean delivery episodes defined by vaginal and caesarean delivery DRGs.

Exclusions: None

Risk adjustment

Purpose

To level the playing field in making comparisons of outcomes,

either against a benchmark, or across individual providers,

hospitals, or provider groups.

Iezzoni LI. JAMA. 1997; 278: 1600-1607.

Complications of anaesthesia82 year old woman with

CCF, DM65 year old man with

HTN

Consequences of NOT leveling the playing field

Inappropriate inference of high quality:patients cared for by a given provider are “less sick” than those cared for by others – outcomes are better Not because of higher quality, but because patients were more likely a priori to have good outcomes

Inappropriate inference of low quality:patients cared for by a given provider are “sicker” than those cared for by others – outcomes are worse Not because of lower quality, but because patients were more likely a priori to have bad outcomes

Risk-Adjustment is necessaryWhen comparisons are being made from observational data:

Across providers

Between a provider and a benchmark result*

Over time, if selection changes

* unless the benchmark is an absolute

DefinitionRisk adjustment attempts to account for all factors, other than the process of care that could affect outcomes, for example health of patient before a given intervention.

Risk adjustment methods require the development of statistical models that explain the outcome variables of interest based on patient characteristics we wish to control.

Risk adjustment is necessary when susceptibility to outcomes varies across providers’ patients, especially if there is differential selection by risk-related factors

3 key factorsSociodemographic factors:Age, sex, race/ethnicity, SES, (genetic characteristics)

ComorbidityThe importance, number, (and severity) of co-existing conditions

Severity of IllnessAcute clinical stability; importance, extent, and severity of principal diagnosis

Comorbidity“Conditions present on admission that are not related directly to the main reason for hospitalisation, but that increase the intensity of resources used or increase the likelihood of apoor outcome”

Not the primary reason for admission

Not a complication of service during admission

Elixhauser comorbiditiesCode based version developed in 1998

Validated on more than 1 million hospitalisations in California, with subgroup analyses:

specific diseases (breast cancer, AMI, asthma, appendicitis, hernia, diverticulitis, biliary tract disease, LBP, pneumonia, complicated diabetes)

outcomes (in-hospital mortality, length of stay, hospital charges)

29 Comorbidities defined by Elixhauser• Solid tumor w/o metastases• Metastatic cancer• Lymphoma• AIDS• Chronic pulmonary disease• Valvular disease• CHF• Pulmonary circulation disorders• Peripheral vascular disorders• Hypertension (complicated &

uncomplicated)• Paralysis• Other neurological disorders• Liver disease• Rheumatoid arthritis/collagen

vascular diseases

• Diabetes (uncomplicated)• Diabetes (complicated)• Hypothyroidism• Peptic ulcer disease excluding bleeding• Renal failure• Coagulopathy• Blood loss anemia• Deficiency anemias• Alcohol abuse• Drug abuse• Psychoses• Depression• Obesity• Weight loss• Fluid & electrolyte disorders

Elixhauser’s findingsIf a comorbidity was associated with a statistically significant increase in length of stay or total hospital discharges, it usually increased the odds of dying in the hospital.

Twenty-six of the 30 comorbidities had at least one diagnosis subgroup for which the specific comorbidityhad an effect of 25% or more resource use or had aneffect of increasing the probability of death by 50% or more compared with those without the comorbidity.

A few comorbidities were consistently strong predictors of outcomes for the diagnosis subgroups:

congestive heart failure, diabetes withcomplications,renal failure, coagulopathy, weight loss, fluid and electrolyte disorders, blood loss anemia (except for mortality prediction)deficiency anemia (except for mortality prediction).

Of all comorbidities examined, seven were statistically significant predictors in six or more subgroups for all three outcome measures……

Those seven comorbidities were: congestive heart failure,cardiac arrhythmias, other neurologic disorders, renal failure, coagulopathy,weight loss, and fluid and electrolyte disorders.

How do we assess how well a model fits the data?

Our goal :

To develop model that accurately reflects patterns in the data that are valid when applied to data in other, comparable settings

Distinguish well between patients who have outcome and those whodo not

Match predicted and observed rates across the entire

spread of data.

Discrimination Calibration

Ability of a model to

The area under the ROC curve-ranges from 0 to 1.

Comparison of observed and predicted

outcomes over deciles of risk (akin to The Hosmer and Lemeshow’s

χ2-statistic).

A commonly used measure is the

Another way of looking at how the Area under the

ROC curve is measured

Create multiple pairs of subjects with/without outcome.

Compare the predicted probability of the outcome (from themodel) to the actual outcome status of subject (yes/no)

The percentage of pairs in which the predicted probability of the outcome is higher for the subject with the outcome is equal to the Area under the ROC curve.

Impact of different modelsOBSERVED

age age+ comorb age age+

comorb age age+ comorb

1 0.11 0.43 1 0.89 0.46 0.89 0.462 0.11 0.24 0 -0.11 -0.35 0.77 0.113 0.11 0.87 1 0.89 0.01 1.66 0.124 0.25 0.46 0 -0.25 -0.71 1.41 -0.595 0.11 0.35 0 -0.11 -0.46 1.29 -1.056 0.25 2.61 0 -0.25 -2.86 1.04 -3.917 0.25 0.01 0 -0.25 -0.26 0.79 -4.178 0.25 0.12 1 0.75 0.63 1.54 -3.549 0.11 3.71 0 -0.11 -3.83 1.43 -7.3710 0.25 91.32 1 0.75 -90.57 2.18 -97.9311 0.36 0.14 0 -0.36 -0.50 1.82 -98.4312 0.25 2.35 0 -0.25 -2.59 1.57 -101.0313 0.37 0.36 0 -0.37 -0.73 1.20 -101.7514 0.25 63.39 0 -0.25 -63.64 0.95 -165.4015 0.25 50.35 0 -0.25 -50.60 0.70 -215.9916 0.37 0.28 1 0.63 0.35 1.33 -215.6417 0.11 1.27 0 -0.11 -1.38 1.22 -217.0318 0.25 0.31 0 -0.25 -0.56 0.97 -217.5919 0.25 12.05 0 -0.25 -12.29 0.72 -229.88

VLAD

MODELS

EXPECTED

Observed OUTCOME

MODELS

O-E

MODELS

predicted probability of outcome

The statistics underlying the reports

Dr Nick Andrianopolous


OutlineLogistic regression

VLAD

VLAD limits

Dr. Nick Andrianopoulos, MBBS, MBiostat – Monash University, Centre of Research Excellence in Patient Safety (CRE-PS)

Logistic regressionAnalyses binomially distributed data of the form

Yi ~ B(ni,pi) for i = 1,…,mThe logits of the unknown binomial probabilities (i.e. the logarithms of the odds) are modelled as a linear function of covariates

logit(pi) = ln( pi / (1-pi) ) = β0 + β1x1,i + … + βkxk,i

The probability for an individual of an eventpi = 1/(1 + e-(β0 + β1x1,i + … + βkxk,i))

Logistic regressionVAED: 1st July 2004 – 30th June 2007,

>3.9 x 106 separations

Logistic regression based on the Queensland models predicting in-hospital mortality for:

Acute myocardial infarction (AMI) n=8924 eligible separations (27.4%)Heart failure (HF) n=11124 (40.6%)Stroke n=5857 (25.8%)

Multiple logistic regression - AMI2004-7 (N=8924) OR 95% CI PSexMale (reference) 1.00Female 1.21 1.04 – 1.40 0.013Age - years30 – 54 0.55 0.37 – 0.81 0.00255 – 59 0.54 0.34 – 0.85 0.00860 – 64 (reference) 1.00 - -65 – 69 1.17 0.83 – 1.66 0.37870 – 74 1.52 1.10 – 2.10 0.01175 – 79 2.21 1.63 – 2.98 <0.000180 - 84 2.95 2.19 – 3.97 <0.0001

Multiple logistic regression – AMI (cont.)2004-7 (N=8924) OR 95% CI PComorbiditiesHypotension/shock 3.99 3.42 – 4.65 <0.001Dysrrhythmias 3.03 2.62 – 3.50 <0.001Cerebrovascular disease 3.91 2.74 – 5.58 <0.001HF 1.30 1.11 – 1.52 0.001Renal Failure 1.62 1.37 – 1.91 <0.001Dementia 1.52 1.04 – 2.23 0.031Malignancy 2.16 1.48 – 3.16 <0.001Diabetes 0.98 0.83 – 1.16 0.833Hypertension 0.49 0.42 – 0.57 <0.001

Multiple logistic regression – AMI (cont.)

2004-7 (N=8924) OR 95% CI P

Year of separation

2004-5 (reference) 1.00 - -

2005-6 1.09 0.91 – 1.29 0.347

2006-7 1.09 0.92 – 1.30 0.311

Logistic regressionGoodness of Fit

Discrimination i.e. how well model distinguishes patients who survive from those who die

C-statisticMathematically equivalent to area under receiver operator characteristic (ROC) curve

Calibration i.e. how well model fits data overallHosmer-Lemeshow chi-square testCompares observed and expected counts for both deaths and survivals by partitioning observations into approximately equal groups of size e.g. decilesHigh P-value is an indication that the number of deaths are not significantly different from those predicted by the model, thus the overall model fit is good

Logistic regression - GOF

Year 2004-5 NArea underROC curve

Hosmer-Lemeshowchi2(8) P

AMI 2999 0.8344 13.77 0.0880

HF 3598 0.8260 8.90 0.3506

Stroke 1922 0.6768 6.16 0.5208

VLADVariable Life Adjusted Display

Plots cumulative expected (E) – observed (O) events

E calculated from 2004-5 logistic models

Graph from 1st July 2005 onwards

Calculated by de-identified hospital

∑∑==

−=n

ii

n

iin OEV

11

VLAD - LimitsUpper and lower risk adjusted Cumulative Sum (CUSUM) limits

rho – odds ratio between risk of mortality under the alternative and null hypothesesh – control limit signifying when the CUSUM signals

Resetting of limit with signalMethodology as per Sherlaw-Johnson1

1Sherlaw-Johnson C. (2005) A Method for Detecting Runs of Good and Bad Clinical Outcomes on Variable Life-Adjusted Display (VLAD) Charts. Health Care Management Science, 8: 61-65.

VLAD - LimitsFor the lower limit the CUSUM of the nth observation (Cn) with the corresponding weight is given by:

C0 = 0 and Cn = max {Cn-1 + Wn ,0}

For the upper limit the CUSUM of the nth observation (Cn) with the corresponding weight is given by:

C0 = 0 and Cn = min {Cn-1 - Wn ,0}where

Wn = Onlog rho – log(1+(rho-1)En)

VLAD - LimitsThe lower VLAD limit (Ln) can then be calculated by the expression

Ln = Vn + (Cn – h)/log rho

The upper VLAD limit (Ln) can then be calculated by the expression

Ln = Vn - (Cn + h)/log rho

Should Vn intersect with Ln, the limit is reset to Zn by

Zn = Ln+ h/log rho

VLAD - Limits

Lower limit: rho = 2.0, h = 3.0

Upper limit: rho = 0.5, h = 3.0

VLAD – Multiple LimitsARL = Average run length to false alarms Coory et. al.2

Improved performance

Worse performance

rho 0.70 0.50 0.25 1.30 1.50 1.75

h 2.6 3.6 4.9 2.8 3.7 5.0

ARL 229 682 2447 264 834 3118

2Coory M, Duckett S, Sketcher-Baker K. (2008) Using control charts to monitor quality of care with administrative data. International Journal for Quality in Health Care, 20: 31-39

VLAD – Multiple Limits

How do we assess how well a model fits the data?

Our goal :To develop model that accurately reflects patterns in the data that are valid when applied to data in other, comparable settings

Distinguish well between patients who have outcome and those whodo not

Match predicted and observed rates across the entire

spread of data.

Discrimination Calibration

Ability of a model to

The area under the ROC curve-ranges from 0 to 1.

Comparison of observed and predicted

outcomes over deciles of risk (akin to The Hosmer and Lemeshow’s

χ2-statistic).

A commonly used measure is the

Another way of looking at how the Area under the

ROC curve is measured

Create multiple pairs of subjects with/without outcome.

Compare the predicted probability of the outcome (from themodel) to the actual outcome status of subject (yes/no)

The percentage of pairs in which the predicted probability of the outcome is higher for the subject with the outcome is equal to the Area under the ROC curve.

Impact of different modelsOBSERVED

age age+ comorb age age+

comorb age age+ comorb

1 0.11 0.43 1 0.89 0.46 0.89 0.462 0.11 0.24 0 -0.11 -0.35 0.77 0.113 0.11 0.87 1 0.89 0.01 1.66 0.124 0.25 0.46 0 -0.25 -0.71 1.41 -0.595 0.11 0.35 0 -0.11 -0.46 1.29 -1.056 0.25 2.61 0 -0.25 -2.86 1.04 -3.917 0.25 0.01 0 -0.25 -0.26 0.79 -4.178 0.25 0.12 1 0.75 0.63 1.54 -3.549 0.11 3.71 0 -0.11 -3.83 1.43 -7.3710 0.25 91.32 1 0.75 -90.57 2.18 -97.9311 0.36 0.14 0 -0.36 -0.50 1.82 -98.4312 0.25 2.35 0 -0.25 -2.59 1.57 -101.0313 0.37 0.36 0 -0.37 -0.73 1.20 -101.7514 0.25 63.39 0 -0.25 -63.64 0.95 -165.4015 0.25 50.35 0 -0.25 -50.60 0.70 -215.9916 0.37 0.28 1 0.63 0.35 1.33 -215.6417 0.11 1.27 0 -0.11 -1.38 1.22 -217.0318 0.25 0.31 0 -0.25 -0.56 0.97 -217.5919 0.25 12.05 0 -0.25 -12.29 0.72 -229.88

VLAD

MODELS

EXPECTED

Observed OUTCOME

MODELS

O-E

MODELS

predicted probability of outcome

What to do with the reports

Andrew Clarke

Case studiesKirrily GilchristJill ButtyMary Draper

What to do with the reports

Andrew Clarke

What do we do with the reports?

Pyramid of investigation

Pyramid of investigationVerify accuracy ( or otherwise) of data

Consider nature of patient population

Check resource / structure changes

Explore processes of care

Identify carer – not usually necessary

Review of Patient Safety Indicators

Kirrily GilchristQuality Manager Acute Services Northern Health

Current monitoring

Northern Health KPI Booklet :Falls with major injury

Pressure Ulcers

ACHS Clinical IndicatorsClinical Risk Management KPI’sClinical Audits (divisional)Sentinel Event

Preliminary workReferred to VLAD’s for Dummies

Created a cheat sheet Risk Adjusting, Control limits & X axis

Discussions with Health Information ManagersLink the definitions with ICD-10-AM Data

Reported to Board Quality new initiative

Investigating the dataAccuracy of indicators against definitions:

Examined each indicator

Validate ICD-10-AM codes on PMI to AUSPSI definition (check)

Validate ICD-10-AM codes to documentation

Validation against internal incident reporting (Riskman)

Recorded basic information to allow review eg PDx, Complications and procedures

Review of indicatorsIndicator 6 - Obstetric Trauma

Total Events DHS – 4

Total Confirmed ICD-10-AM Coding PMI – 3

Total Confirmed documentation in MR - 3

Total Events Internal Reporting – 3

ICD-10- AM CodingC 001B – Caesarian with serve CCC 075.1 – Other Obstetric TraumaNil codes for 3rd & 4th degree tear DxNil codes for repair of 3rd or 4th degree tear

Validated ICD-10-AM coding to Documentation

Reported Case by UR to DHS

Review of indicators

Review of indicatorsIndicator 3 – In hospital Fracture

Total Events DHS – 0

Total Confirmed ICD-10-AM Coding PMI – 0

Total Confirmed documentation in MR – 0/1

Total Events Internal Reporting (Riskman) – 1

1 Event reported to Board Quality for in-hospital fracture

OutcomesErrors in DHS reports for events identified

Errors in comparison to internal reporting of indicators

Opportunities to improve documentation for Coded Information

Feedback to Health Information Managers onCoded Information

OutcomesEnhanced Clinical Review opportunities

Supports current reporting to Board

Increased benchmarking opportunities for

patient safety indicators

Improve comparability of reports

Patient Safety Indicators

Jill ButtyWerribee Mercy Hospital

Complications of anaesthesia

-8

-6

-4

-2

0

2

4

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000

Case number

Expe

cted

min

us O

bser

ved

Adv

erse

Eve

nts

VLAD Lower Control limit Upper Control limit

Investigate

Complications of anaesthesia

Complications of anaesthesia – VAED data

NumeratorEpisodes with an ICD-10-AM diagnosis code for complication of anaesthesia that has an associated flag representing status 'notpresent on admission'. (Vic Prefix=C, national condition onset flag=1)

DenominatorAll episodes with an anaesthetic procedure code (Appendix R).

ExclusionsNone.

Complications of Anaesthesia - Definition

InvestigationEpisode #1342 – 9 year old boy presented to ED with greenstick fracture of radius 15o angulation. Plan for reduction under ketamine.

Ketamine given – child developed rash over thorax-urticarial in response to ketamine. No complication of airways.

Coded C L27.1

Other admissions13371341 134713501355

No anaesthetic complication with thesecases.

Patient Safety Monitoring Initiative

VLADS and you thought casemix was trying!

Mary Draper Director, Clinical GovernanceThe Women’s

VLAD Graph Obstetric Trauma – Vaginal or Caesarian Delivery

-14

-12

-10

-8

-6

-4

-2

0

2

4

6

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800

Expected minus Observed Adverse Events

Case number

Obstetric trauma


Stage one – are there data issues?We ran comparison of two internal databases (the obstetric database and HIS) and the VLADS data. In the first quarter we found 16 cases identified by VLADS not in HIS data submitted to DHS. What mystery was this?Checking the medical records, we found:

8 or more women with intact perineums5 1st and 2nd degree tears2 uterine tears 1 woman with urinary retention

The majority of the women had emergency caesarean sections.

3rd and 4th degree tears as percentage of vaginal births (RWH)

2007 2008

Primiparae 6.5%

Multiparous 1.3%

Total 4.6% 3.9%Data Source: ROBIN database, RWH

Threshold performance<5% - source RCOG maternity dashboard

RWH monthly data for 20083rd and 4th degree tears by month against threshold

Data Source: ROBIN database, RWH

The Royal Women's HospitalSummary of Deliveries in 2008

Data Source: Information and Performance Unit, RWH

Tears as a proportion of normal vaginal births and instrumental births by month 2008

Data Source: ROBIN database, RWH

Risk factors for 3rd and 4th degree tears •birth weight over 4 kg (up to 2%)•persistent occipitoposterior position (up to 3%)•nulliparity (up to 4%)•induction of labour (up to 2%)•epidural analgesia (up to 2%)•second stage longer than 1 hour (up to 4%)•shoulder dystocia (up to 4%)•midline episiotomy (up to 3%)•forceps delivery (up to 7%)

•Additional risk factors (RCOG)

How should obstetric anal sphincter injurybe classified?

‘If there is any doubt about the grade of third-degree tear, it is advisable to classify it to the higher degree rather than lower degree.’

RCOG

Why identify 3rd and 4th degree tears?

It is an important morbidity associated with birth

It has consequences for women

Because there is a treatment pathway for early intervention to reduce long term morbidity

(Perineal Clinic)

Why the particular pattern?We have no idea!

Perhaps we it was about the move of the hospital

Perhaps we had high volume which contributed

Perhaps it was influenced by an educational drive on ascertainment in May 2008 – we will have another one in May 2009

Perhaps it’s a DHS data issue

Perhaps we had more women with risk factors in that period

Perhaps it’s about instrumental deliveries and supervision

Perhaps our risk factors are different from other hospital

Perhaps there is under ascertainment at other hospitals

Why the particular pattern?

What are we doing about 3rd and 4th

degree tears?Maternity Teams are provided with weekly and monthly monitoring data for discussion at weekly meetings.

Development of a comprehensive CPG for the prevention, ascertainment and management (immediate, ongoing and fosubsequent births) of women who sustain a 3rd / 4th degree tear.

An ‘education blitz’ in May/June on a new Third degree tear risk assessment tool, evaluated by audit and outcome data review.

Review of our data for 3rd / 4th degree tears found that nearly 50% occur during instrumental births. The hospital has implemented revised escalation processes to ensure that all instrumental births conducted by JMOs are supervised by a consultant until the JMO has been credentialed in this procedure.

Ongoing management of all women who sustain a 3rd / 4th degree tear by referral to the ‘Perineal clinic’.

What are we doing about 3rd and 4th

degree tears?

More VLADS to puzzle about!Obstetric Trauma

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Moving forward

Rule No 1 – if you want the attention of clinicians, the data needs

to be clinically meaningful

Where to from here

Steven McConchie

Where to form hereFor us

Easy, existing data

Make sure it works

For youHelp us improve for you

Use them/investigate

Keep track of what you find/share it

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