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Michael Wasdell, MAEpidemiologistEvaluation & Research [email protected]
Susan ChunickSusan ChunickDirectorDirector
Camille VirayCamille VirayEducation and Communications Education and Communications CoordinatorCoordinator
Dina ShafeyDina ShafeyResearch Ethics CoordinatorResearch Ethics Coordinator
Magdalena SwansonMagdalena SwansonResearch and Grant Research and Grant Development FacilitatorDevelopment Facilitator
Michael WasdellMichael WasdellEpidemiologistEpidemiologist
Department of Evaluation and Research ServicesDepartment of Evaluation and Research Services
1.1. Learn best practices in conducting a Learn best practices in conducting a retrospective chart review;retrospective chart review;
2.2. Understand quantitative data collection, Understand quantitative data collection, documentation and coding methods; anddocumentation and coding methods; and
3.3. Time permitting Time permitting -- Understand accepted Understand accepted approaches for analyzing and reporting approaches for analyzing and reporting data.data.
What is retrospective chart review What is retrospective chart review research?research?
Research methodResearch method utilizing information that utilizing information that was not originally collected for research was not originally collected for research purposespurposesMost common in:Most common in:
FeasibilityFeasibilityEthical considerationsEthical considerationsCostCostAvoid lag time in waiting for health outcomes Avoid lag time in waiting for health outcomes to occurto occurAccess to rare cases or occurrencesAccess to rare cases or occurrences
Hypothesis generationHypothesis generationSuitable method based on research Suitable method based on research goal/questiongoal/question
1Osteoporosis1Internal Medicine6Critical Care (ICU)
22Orthopaedics3Infectious Diseases1Chronic Care
7Oncology1Infection Control20Cardiology
1Occupational Therapy4ICU2Acute Programs
Active Studies by Department Area
Funding for ACTIVE StudiesType of Ethics Review for
ACTIVE Studies
104
115Full BoardDelegated
38
13
85
5
69
90
10
20
30
40
50
60
70
80
90
Sponsor FH Grants Non-FHGrants
Grant-in-AidUnfunded Other
28% involve retrospective data
Limitations of Retrospective Chart Limitations of Retrospective Chart Review ResearchReview Research
Internal ValidityInternal ValidityConfoundingConfoundingSelection biasSelection biasHistoryHistoryMaturationMaturationRegression toward the meanRegression toward the meanInstrumentation changeInstrumentation changeDifferential attritionDifferential attritionResearcher biasResearcher bias
External ValidityExternal ValidityGeneralizabilityGeneralizability of resultsof results
“We (the journal) are dubious about the integrity of retrospective chart review studies and therefore cannot accept your manuscript for publication.”
“We (the journal) are dubious about the integrity of retrospective chart review studies and therefore cannot accept your manuscript for publication.”
Quick FactsQuick Facts
PubmedPubmedSearch of terms (18 January 2011)Search of terms (18 January 2011)““retrospectiveretrospective”” = 419358 hits= 419358 hits““retrospective chartretrospective chart”” = 13913 hits= 13913 hits““retrospective chart methodologyretrospective chart methodology”” = 9124= 9124•• Limits to title only = 2Limits to title only = 2•• Review of article = 1 article proposing a Review of article = 1 article proposing a
methodology for retrospective chart reviewmethodology for retrospective chart reviewGearing et al. J Can Acad Child Adolesc Psychiatry 15:3 August 2006
Scientific Approach to Scientific Approach to Retrospective Chart ReviewRetrospective Chart Review
Nine step process (Gearing, 2006)Nine step process (Gearing, 2006)1.1. ConceptionConception2.2. Literature reviewLiterature review3.3. Proposal developmentProposal development4.4. Data Abstraction Instrument OrganizationData Abstraction Instrument Organization5.5. Abstraction Protocols and GuidelinesAbstraction Protocols and Guidelines6.6. Data abstractionData abstraction7.7. Sample size justificationSample size justification8.8. EthicsEthics9.9. Pilot StudyPilot Study
Gearing et al. J Can Acad Child Adolesc Psychiatry 15:3 August 2006
Comparison of ApproachesComparison of Approaches
Are you ready for a plate of spaghetti?
Comparison of ApproachesComparison of Approaches1.1. ConceptionConception2.2. Literature reviewLiterature review3.3. Proposal Proposal
developmentdevelopment4.4. Data Abstraction Data Abstraction
Instrument Instrument OrganizationOrganization
5.5. Abstraction Protocols Abstraction Protocols and Guidelinesand Guidelines
6.6. Data abstractionData abstraction7.7. Sample size Sample size
justificationjustification8.8. EthicsEthics9.9. Pilot StudyPilot Study
1.1. Generate ideaGenerate idea2.2. Conduct literature reviewConduct literature review3.3. Refine research questionRefine research question4.4. Plan research Plan research
methodologymethodology5.5. Create research proposalCreate research proposal6.6. Apply for fundingApply for funding7.7. Apply for ethics approvalApply for ethics approval8.8. Collect and analyze dataCollect and analyze data9.9. Draw conclusions and Draw conclusions and
1. Generate Research Idea1. Generate Research IdeaClearly stated research questionClearly stated research questionStatement of goal and hypothesisStatement of goal and hypothesisInput from peers/expertsInput from peers/experts
These factors can provide information related to These factors can provide information related to study feasibilitystudy feasibility
Is the information you are seeking available from the Is the information you are seeking available from the medical record and is the chart information likely to be medical record and is the chart information likely to be useful in answering your question?useful in answering your question?This step is often overlooked or not thoroughly This step is often overlooked or not thoroughly undertaken.undertaken.
1. Generate Research Idea1. Generate Research Idea
Clearly stated research questionClearly stated research question
1. Generate Research Idea1. Generate Research IdeaExample: General Clinical QuestionExample: General Clinical QuestionWhat method is best to manage obesity in patients with What method is best to manage obesity in patients with
diabetes?diabetes?
Example: Applying PICOExample: Applying PICOP = Adult patients with type II diabetesP = Adult patients with type II diabetesI = Lifestyle education and fitness trainingI = Lifestyle education and fitness trainingC = Weight reducing medicationC = Weight reducing medicationO = Body Mass IndexO = Body Mass Index
In adults with type II diabetes, is weight reducing In adults with type II diabetes, is weight reducing medication more effective than lifestyle education and medication more effective than lifestyle education and fitness training in reducing BMI?fitness training in reducing BMI?
1. Generate Research Idea1. Generate Research IdeaAssessing the research goal:Assessing the research goal:
Describe Describe –– when little is known about the characteristics of a when little is known about the characteristics of a problem, patient group, health care providers or a health problem, patient group, health care providers or a health service/system.service/system.
Associate Associate –– when you want to assess if certain factors might go when you want to assess if certain factors might go hand in hand with a well described problem.hand in hand with a well described problem.
Predict Predict –– when you want to understand the extent to which when you want to understand the extent to which certain factors contribute to or cause a problem.certain factors contribute to or cause a problem.
Compare Compare –– when you wish assess the impact of an intervention when you wish assess the impact of an intervention or to determine if there are differences between interventions or to determine if there are differences between interventions or characteristics of various groups.or characteristics of various groups.
1. Generate Research Idea1. Generate Research IdeaStatement of goals/hypothesis Statement of goals/hypothesis –– linked to state of linked to state of
knowledge and research questionknowledge and research question
YesYesDo different levels of x Do different levels of x predict y?predict y?
PredictPredict
YesYesIs x different from y?Is x different from y?CompareCompare
YesYesIs there a relationship Is there a relationship between x and y?between x and y?
AssociateAssociate
NoNoWhat are the characteristics What are the characteristics of x?of x?
DescribeDescribe
HypothesisHypothesisTypes of QuestionsTypes of QuestionsGoalGoal
1. Generate Research Idea1. Generate Research Idea
Input from peers/expertsInput from peers/expertsMay help in identifying data available/not May help in identifying data available/not available in the medical recordavailable in the medical recordMay identify ways in which to enhance May identify ways in which to enhance internal validity based on knowledge of internal validity based on knowledge of contents of the medical recordcontents of the medical record•• ConfoundsConfounds•• Instrumentation changeInstrumentation change•• Selection of cases Selection of cases –– i.e.., ICD codesi.e.., ICD codes
2. Literature Review2. Literature Review3. Refine Research Question3. Refine Research QuestionIdentify available evidenceIdentify available evidence
What is already known What is already known –– what interventions have what interventions have been studied?been studied?How is the patient sample defined?How is the patient sample defined?What are the commonly reported outcome measures?What are the commonly reported outcome measures?What methodologies have been used What methodologies have been used –– what are the what are the limitations?limitations?
Helps to refine research question.Helps to refine research question.Conducting literature reviews take time, but this Conducting literature reviews take time, but this is a very important step in the research process.is a very important step in the research process.The Fraser Health library can help you with your The Fraser Health library can help you with your search. search.
Make it FINERMake it FINER
Confirmed through literature search and review
3. Plan Research Methodology3. Plan Research Methodology
Sample and justification Sample and justification Data Abstraction Instrument Data Abstraction Instrument OrganizationOrganizationAbstraction Protocols and GuidelinesAbstraction Protocols and GuidelinesData AbstractionData Abstraction
Choosing Your Study SampleChoosing Your Study Sample
How many charts do you How many charts do you need to look at?need to look at?
All within a certain time frame?All within a certain time frame?A random sample?A random sample?TIP: Consult a statistician TIP: Consult a statistician EARLY!EARLY!
Sample Size Sample Size -- StatisticalStatisticalIf you want your results to be statistically valid, If you want your results to be statistically valid, your sample size is critical. your sample size is critical. If the sample is too small, the random variability If the sample is too small, the random variability will be too large, and the results will be limited in will be too large, and the results will be limited in their applicability.their applicability.Sample size table for identifying a representative Sample size table for identifying a representative sample from a population.sample from a population.Consult with epidemiologist if you wish to obtain Consult with epidemiologist if you wish to obtain statistically valid estimates of association, statistically valid estimates of association, prediction or comparisons/differences.prediction or comparisons/differences.
Sample Size Table Sample Size Table –– For Descriptive ResearchFor Descriptive Research
Random SamplingRandom SamplingSimple Random SamplingSimple Random Sampling
Each unit has an equal probability of being chosen.Each unit has an equal probability of being chosen.Enumerate list, then choose with random numbers.Enumerate list, then choose with random numbers.
Systematic Random SamplingSystematic Random SamplingSelection of the sample using an interval Selection of the sample using an interval ““kk”” so that so that every every ““kk”” unit in the population is selectedunit in the population is selected
1. Number the units in the population from 1 to N.1. Number the units in the population from 1 to N.2. Decide on the n (sample size) that you want or need. 2. Decide on the n (sample size) that you want or need.
k = N/n = the interval size. k = N/n = the interval size. 3. Randomly select an integer between 1 and k. 3. Randomly select an integer between 1 and k. 4. Then take every 4. Then take every kthkth unit.unit.
The nuts and bolts of data quality
3. Plan Research Methodology3. Plan Research Methodology
Data Abstraction Instrument OrganizationData Abstraction Instrument OrganizationAbstraction Protocols and GuidelinesAbstraction Protocols and GuidelinesData abstraction Data abstraction -- Validity and ReliabilityValidity and ReliabilityPilot TestingPilot Testing
Validity of chart review dataValidity of chart review data
Pan et al. (2005) Journal of Clinical Pan et al. (2005) Journal of Clinical EpidemiologyEpidemiology
77.5% to 96.9% accuracy depending on 77.5% to 96.9% accuracy depending on clinical area (lowest in ICU)clinical area (lowest in ICU)55.1% to 97.4% accuracy depending on chart 55.1% to 97.4% accuracy depending on chart section (lowest in diagnosis and surgical)section (lowest in diagnosis and surgical)19% errors were missing data19% errors were missing data
Reducing error in chart reviewReducing error in chart review
Data abstractionData abstractionCodingCodingCleaningCleaningValidationValidationVerificationVerification
Data Abstraction Tool (DAT)Data Abstraction Tool (DAT)
Minimum standardsMinimum standardsDesign the data abstraction tool (DAT) to Design the data abstraction tool (DAT) to collect the data specified by the project collect the data specified by the project protocol. protocol. Document training of personnel on the Document training of personnel on the protocol, DAT completion instructions and protocol, DAT completion instructions and data submission/storage. data submission/storage.
Data Abstraction Tool (DAT)Data Abstraction Tool (DAT)Best practicesBest practices
Design the DAT along with the project protocol to assure Design the DAT along with the project protocol to assure collection of all data specified in protocol. collection of all data specified in protocol. Keep questions, prompts and instructions clear and Keep questions, prompts and instructions clear and concise. concise. Design the DAT to follow the data flow from the Design the DAT to follow the data flow from the perspective of the person completing it, taking into perspective of the person completing it, taking into account the flow and organization of data in a medical account the flow and organization of data in a medical record. record. Avoid referential and redundant data points. Avoid referential and redundant data points. Design the DAT with the primary measures of interest in Design the DAT with the primary measures of interest in mind as the main goal of data collection. mind as the main goal of data collection. Design the DAT with accompanying data collection Design the DAT with accompanying data collection instruction book, and library of codes.instruction book, and library of codes.
DAT Design ConsiderationsDAT Design Considerations
A.A. Feasible Feasible –– capable of being completedcapable of being completed
B.B. Acceptable Acceptable –– resulting data is usefulresulting data is useful
C.C. Reliable Reliable –– consistency of information consistency of information abstractedabstracted
A. FeasibleA. Feasible
Feasible Feasible –– capable of being completedcapable of being completedLogical orderLogical orderMatch with the order and type of Match with the order and type of information in the chartinformation in the chartAbstraction can be done within time limitsAbstraction can be done within time limits
Feasible Feasible –– Order and Type of DataOrder and Type of Data
StaticStaticSnapshot in timeSnapshot in timeDemographicDemographicMedical historyMedical history
EvolvingEvolvingInformation is collected Information is collected over timeover timeRepeated measurementRepeated measurementVital signsVital signsDaily Medication ordersDaily Medication orders
CumulativeCumulativeCollected over time, but Collected over time, but not linked to specific not linked to specific points in timepoints in timeMedication errorsMedication errors
Single record -single page
Series of single records/pages per time interval
Single page cumulative log linked with time interval
Single page cumulative log Feasible – capable of being completed
Logical orderMatch with the order and type of information in the chartAbstraction can be done within time limits
Feasible Feasible –– OtherOther
Consider Consider ‘‘modulesmodules’’ to match chart to match chart information (e.g.., lab values, pharmacy)information (e.g.., lab values, pharmacy)DAT designed to accommodate different DAT designed to accommodate different chart or report formats (e.g.., charting from chart or report formats (e.g.., charting from different sites)different sites)Time to completeTime to complete
B. AcceptableB. Acceptable
Acceptable Acceptable –– resulting resulting data is usefuldata is usefulMinimization of missing Minimization of missing informationinformationLow data abstraction Low data abstraction error rateerror rateUse of validated or Use of validated or accepted measuresaccepted measuresReduction of biasReduction of bias
Acceptable Acceptable –– Missing Information Missing Information
Minimization and management of missing Minimization and management of missing informationinformation
PrePre--screen charts for availability of informationscreen charts for availability of information•• Missing information may impact sample size Missing information may impact sample size
precision and feasibility precision and feasibility
Detailed instructions for recording potentially Detailed instructions for recording potentially ambiguous informationambiguous informationDistinguish between missing, not appropriate, Distinguish between missing, not appropriate, not done and 0 valuesnot done and 0 values
Error MinimizationError MinimizationLimit manual entry of numbers or textLimit manual entry of numbers or textUse boxes/shapes to enter information (standardize Use boxes/shapes to enter information (standardize formats)formats)Limit use of circled items or checkmarks (use Limit use of circled items or checkmarks (use XX))Limit use of skip patternsLimit use of skip patterns
•• Make skip patterns salient by bolding, highlighting or use of Make skip patterns salient by bolding, highlighting or use of graphicsgraphics
•• Instruct where to skipInstruct where to skip toto, and not , and not whatwhat to skipto skip
Easy to readEasy to readSufficient spaceSufficient space
Minimize Data Abstraction Error Minimize Data Abstraction Error LDL cholesterol value _____LDL cholesterol value _____
LDL cholesterol value _____mg/LDL cholesterol value _____mg/dLdL
LDL cholesterol value _____mg/LDL cholesterol value _____mg/dLdL or ______or ______mmolmmol/L/L
LDL cholesterol value ______LDL cholesterol value ______ mg/mg/dLdLunit if different ______unit if different ______
Well designed Well designed DATsDATs use cues for different response use cues for different response formats:formats:
TextTextSite of injury (e.g. left shoulder)____________Site of injury (e.g. left shoulder)____________
NumericNumericAge [___]Age [___]
DateDate__/____/____/____/__dddd mmmmmm yyyy
Standardize DAT Response FormatsStandardize DAT Response Formats
Standardize DAT Response FormatsStandardize DAT Response Formats
Skip PatternSkip Pattern
Acceptable Acceptable –– ValidValid
Use of validated or accepted measuresUse of validated or accepted measuresSelect measures that are consistent with accepted Select measures that are consistent with accepted standards for reportingstandards for reportingConsider changes over time in normative dataConsider changes over time in normative data
•• e.ge.g, lab normal ranges, lab normal ranges
Content validityContent validityDoes the DAT capture all the relevant information?Does the DAT capture all the relevant information?
Concurrent validityConcurrent validityDoes the information collected in the DAT correspond Does the information collected in the DAT correspond with observations?with observations?
•• Outcome measures in charts may not correspond with Outcome measures in charts may not correspond with healthcare processes.healthcare processes.
Acceptable Acceptable –– ValidValid
What about the person abstracting data?What about the person abstracting data?Experience in retrospective research or the content area.Blind to the study hypothesis.
C. ReliableC. ReliableClear and unambiguousClear and unambiguous
Training of data abstractorsTraining of data abstractorsData collection guideData collection guide
•• Glossary of terms and acceptable alternatesGlossary of terms and acceptable alternates•• Decision rules for ambiguous informationDecision rules for ambiguous information
Guidance information on the DATGuidance information on the DATTestTest--retest reliabilityretest reliability
To assess consistency within the same personTo assess consistency within the same person•• To assess drift over timeTo assess drift over time•• To assess errors during learning phaseTo assess errors during learning phase
InterInter--raterrater reliabilityreliabilityDo any two data abstractors record the same Do any two data abstractors record the same information?information?
•• Test data abstractors against each otherTest data abstractors against each other•• Test data abstractors against gold standard exampleTest data abstractors against gold standard example
Additional tools: DAT ProtocolAdditional tools: DAT Protocol
Reference manualReference manualClear instructions for how to collect Clear instructions for how to collect the required informationthe required informationListing of each variable, location in Listing of each variable, location in chart, method for transcribing from chart, method for transcribing from chart to DATchart to DATDATsDATs with explicit protocols with explicit protocols increase interincrease inter--raterrater reliabilityreliability
CodebookCodebook
What is a codebook?What is a codebook?A codebook is a log of your DAT fields and how you A codebook is a log of your DAT fields and how you will code them for data entry to a spreadsheet.will code them for data entry to a spreadsheet.
Why use a codebook?Why use a codebook?A codebook will help everyone understand the coding A codebook will help everyone understand the coding schemes to ensure that they are on the same page!schemes to ensure that they are on the same page!Coded data can be entered into a spreadsheet, which Coded data can be entered into a spreadsheet, which will help when analyzing data.will help when analyzing data.Data from DAT should be coded numerically for ease Data from DAT should be coded numerically for ease of analysis.of analysis.
ageage ageage 1,2,3,4,51,2,3,4,5 1=101=10--20 years 20 years 2=212=21--30 years 30 years 3=313=31--40 years 40 years 4=414=41--50 years 50 years 5=51+ years5=51+ years
Spreadsheet ExampleSpreadsheet ExampleID# Age Sex Happiness
1 1 1 2
2 2 2 2
3 3 1 2
4 57 2 2
5 45 2 3
6 66 2 3
7 2 2 3
8 88 2 3
Pilot Testing Pilot Testing –– for Designfor Design
Consider looking at a few chartsConsider looking at a few chartsSee what is documentedSee what is documented•• Will tell you what you can captureWill tell you what you can capture
Get an idea of what the hurdles are?Get an idea of what the hurdles are?•• E.g. details of when antibiotic reached the ward E.g. details of when antibiotic reached the ward
were not availablewere not available
Find out where the information actually isFind out where the information actually is•• i.ei.e what section of the chartwhat section of the chart•• This helps in planning time to complete the workThis helps in planning time to complete the work
Pilot Testing Pilot Testing –– for Validityfor ValidityOnce DAT is designed and Codebook is in place Once DAT is designed and Codebook is in place –– test test out the data abstraction and coding process on a few out the data abstraction and coding process on a few charts:charts:
Are key pieces of information missing?Are key pieces of information missing?Does the information translate easily from chart to DAT?Does the information translate easily from chart to DAT?Are there classifications, codes or other information that need Are there classifications, codes or other information that need refining?refining?
A formal pilot study to test the feasibility, reliability and A formal pilot study to test the feasibility, reliability and validity of the data abstraction process is recommended.validity of the data abstraction process is recommended.
Sample size of up to 10 percent of the intended sampleSample size of up to 10 percent of the intended sample
More Spaghetti?More Spaghetti?
Comparison of ApproachesComparison of Approaches1.1. ConceptionConception2.2. Literature reviewLiterature review3.3. Proposal Proposal
developmentdevelopment4.4. Data Abstraction Data Abstraction
Instrument Instrument OrganizationOrganization
5.5. Abstraction Protocols Abstraction Protocols and Guidelinesand Guidelines
6.6. Data abstractionData abstraction7.7. Sample size Sample size
justificationjustification8.8. EthicsEthics9.9. Pilot StudyPilot Study
1.1. Generate ideaGenerate idea2.2. Conduct literature reviewConduct literature review3.3. Refine research questionRefine research question4.4. Plan research Plan research
methodologymethodology5.5. Create research proposalCreate research proposal6.6. Apply for fundingApply for funding7.7. Apply for ethics approvalApply for ethics approval8.8. Collect and analyze dataCollect and analyze data9.9. Draw conclusions and Draw conclusions and
See Past and Future WorkshopsSee Past and Future Workshops
5.5. Create research proposalCreate research proposal6.6. Apply for fundingApply for funding7.7. Apply for ethics approvalApply for ethics approval8.8. Collect and analyze dataCollect and analyze data9.9. Draw conclusions and relate findingsDraw conclusions and relate findings
Data ValidationData Validationchecking that data is sensible and checking that data is sensible and possible possible compare data against applicable rulescompare data against applicable rules
•• identify inappropriate or out of range valuesidentify inappropriate or out of range valuesData VerificationData Verification
check that data is entered correctly and check that data is entered correctly and that there are no transcription errorsthat there are no transcription errors
•• confirm that missing data is indeed missingconfirm that missing data is indeed missing
Data CleaningData Cleaning
Data cleaning is aimed at identifying and Data cleaning is aimed at identifying and correcting data entry errors.correcting data entry errors.Double data entry can help identify data Double data entry can help identify data entry errors.entry errors.Comparison of Excel spreadsheets using Comparison of Excel spreadsheets using built in functions will identify data entry built in functions will identify data entry errors.errors.
=Entry1!A2-Entry2!A2
Entry 1 Entry 2 Entry 1 – Entry 2
Errors
Data Validation with ExcelData Validation with ExcelNumbersNumbers
Specify if entry is a whole number or a decimal number. Specify if entry is a whole number or a decimal number. Set a minimum or maximum.Set a minimum or maximum.Exclude a certain number or range.Exclude a certain number or range.Use a formula to calculate whether a number is valid.Use a formula to calculate whether a number is valid.
Dates and times Dates and times Set a minimum or maximum.Set a minimum or maximum.Exclude certain dates or times.Exclude certain dates or times.Use a formula to calculate whether a date or time is valid.Use a formula to calculate whether a date or time is valid.
Length Length Limit how many characters can be typed in a cell.Limit how many characters can be typed in a cell.Require a minimum number of characters.Require a minimum number of characters.
List of values List of values Specify choices from a list Specify choices from a list —— egeg., small, medium, large.., small, medium, large.
Data ValidationData ValidationFull Tutorial athttp://www.contextures.com/xlDataVal01.html
Data VerificationData Verification
Clinical trials researchClinical trials researchMost rigorous verification methodsMost rigorous verification methodsCompare all abstracted data with source Compare all abstracted data with source documentsdocumentsLabour intensive and costlyLabour intensive and costlyNot feasible for most health careNot feasible for most health care--based chart based chart review activitiesreview activities
Verification in the health care Verification in the health care settingsetting
Goal Goal –– 95% accuracy95% accuracyVerify a sample of Verify a sample of DATsDATs with chartswith chartsSample based onSample based on
•• Experience and training of data abstractorsExperience and training of data abstractors•• Familiarity of data abstractors with care areaFamiliarity of data abstractors with care area•• Complexity of careComplexity of care•• Type of informationType of information
Verify data collected early in the chart audit/reviewVerify data collected early in the chart audit/reviewCompute percent accuracy overall and for Compute percent accuracy overall and for subsections (demographics, diagnostics, surgical subsections (demographics, diagnostics, surgical procedures)procedures)Identify sections falling below 80% accuracy for full Identify sections falling below 80% accuracy for full verificationverificationVerify all missing information if possibleVerify all missing information if possible
ActivityActivityOrganize into groups of 3 or 4Organize into groups of 3 or 4Read/scan the Harry Potter study handout (focus on methods)Read/scan the Harry Potter study handout (focus on methods)Discuss and complete the Retrospective Chart Review ChecklistDiscuss and complete the Retrospective Chart Review ChecklistReport back with group ratingsReport back with group ratings
Discussion/Questions?Discussion/Questions?
Accepted approaches for analyzing and reporting data.
