Joy Mammen MD

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Structured Data as the Foundation for Evidence
Informed Medicine

• Adding Value To Anatomic Pathology Reporting

Joy John Mammen MD Fellow, Pathology
Informatics Department of Pathology Laboratory
Medicine Henry Ford Hospital Detroit
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Objectives

• Understand the changing perspectives in the
  practice of evidence based medicine
• Discuss the impact of changing healthcare
  scenario on the practice of laboratory
  communication
• Outline how standardization of language has
  contributed to acquisition and retrieval of valid
  data, error reduction and quality improvement

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Introduction

• Philosophical origins of evidence based medicine
• Examples
• Cow pox vaccine 1770 1794 (Jenner)
• Louis Pasteur (1822 1895)
• Kochs postulates (1884 1890)
• The Scientific methodology
• Evidence in medicine

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Evidence Based Medicine

• Definition Evidence based medicine is the
  conscientious, explicit, and judicious use of
  current best evidence in making decisions about
  the care of individual patients. (Sackett BMJ
  199631271-72 (13 January))
• Evidence
• Evidence is derived from data
• Levels of evidence
• Lack/Absence of evidence
• EBM ? Cookbook medicine
• EBM Evidence Clinical Expertise Contextual
  factors
• Practice requires an environment that promotes EBM

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Evidence informed policy making and management

• Policy that is evidence informed
• Medical education that promotes this way of
  thinking
• Cyclic process

Intuitive thought
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Knowledge Translation (KT) Research

• Definition a dynamic and iterative process
  that includes synthesis, dissemination, exchange
  and ethically sound application of knowledge to
  improve the health , provide more effective
  health services and products and strengthen the
  health care system
• Canadian Institutes of Health Research
  (http//www.cihr-irsc.gc.ca/e/35195.html)
• Domains in knowledge translation
• Laboratory
• Clinical Research
• Healthcare
• Types
• Type 1 Lab Clinical Research
• Type 2 Clinical Research Health Care

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Knowledge Translation (KT)
KT Type 1
KT Type 2
Lab data driving/gating research
bio-specimens, patients
Lab data providing evidence on which policies
and funding decisions are based
Based on Hulley et al. Designing Clinical
Research, 2007, p 23
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Knowledge Translation
Lab
Clinical Research
Healthcare
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Knowledge Translation
Lab
Clinical Research
Healthcare
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Knowledge Translation (ideal)
Lab
Clinical Research
Healthcare
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Many ways of arriving at evidence

• It has been long known
• It is believed
• It is generally believed
• Typical results are shown
• It is hoped that this study will stimulate
  further study in this area

• I didnt look up the original reference
• I think
• Two other people also think so
• This is the prettiest graph
• I quit

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Data Acquisition

• Data drives synthesis of evidence
• Until the 20th century episodic, intuitive,
  empirical
• Ideally data collection should be
• Unbiased
• Systematic
• Inclusive and detailed
• Easy
• Background task - incorporated into daily
  practice
• Laboratory Information System

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Contrasts in data acquisition in the Clinical and
AP laboratory

• There are inherent differences to the variables
  that are measured
• The output quantitative/qualitative/interpretati
  ve vs. diagnostic
• Contextual issues
• Methodologies
• Automation and computerization
• NOT EASY

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Data acquisition in the Clinical Laboratory

• Traditionally easier - relatively speaking
• There are some obvious reasons for this
• Numerical values
• Automation
• Ranges Reference range,
• Could define normal or abnormal
• However there remain gray areas

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Data acquisition in the Anatomic Pathology
Laboratory

• Traditional free text anatomic pathology report
  (Art Literature) Science
• Definitions
• Normal
• Abnormal
• Biological variation as confounder
• Variations in terminology also contributes to
  data acquisition problem

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An example of a data point (AP)
Prosection
Reporting
Data feeds
Maximum dimension of tumor max_dim_tr
Greatest tumor dimension gr_tr_dim
Tumor, largest dimension tu_lr_dim

• The name of an entity affects intra-institutional
  and inter-institutional data transfer

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Data acquisition in modern Laboratory Medicine

• New modalities
• Genetic tests
• Molecular tests
• Cross-over disciplines
• Moves to integrate existing disciplines
• New challenges

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Standardizing Data in Laboratory Reports

• Which then brings us to the situation that
  Standardization is the key
• Diagnosis Art Science
• Communication Business
• Formatting issues retain underlying standardized
  skeletal structure
• Applying coding and data standards to data that
  has already been acquired OR vice versa

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The example of a data point in AP
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Why do we need standards

• Data translation and communication between
  systems
• Lab gt EMR gt Lab
• Lab gt RHIO/HER/PHR/HIE
• Lab gt Public health reporting
• Lab gt Data warehouses
• EMR Electronic Medical Record
• RHIO Regional Health Information Organization
• PHR Personal Health Record
• HIE Health Information Exchange

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What standards?

• Existing ones as appropriate - synergize
• Vocabulary/terminology
• UMLS
• SNOMED
• WHO
• Data exchange
• HL7 v3
• DICOM
• LOINC

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Structured reports vs. Structured data

• Structuring reports (formatting) traditionally
  using section headers, creating a synopsis
• Templates
• Structuring data -
• Using standardized terminology and vocabularies
• Using predetermined and pre-coded entities
• Categories
• Elements

Data Standardization requires Post processing
Data already Standardized at acquisition
(Pre-processing)
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Structuring data in Pathology

• Category
• Histopathological type (CUI)
• Element
• Adenocarcinoma (CUI)
• Squamous carcinoma (CUI)
• Define data properties
• Free text
• Integer

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Standardize

• Applying coding and data standards PRIOR to data
  acquisition
• Use available standards
• Terminology
• Data exchange

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End-User Acceptance

• Using GUI interfaces that users already encounter
  in daily computing
• Pick lists
• Radio buttons
• Check-boxes
• Make it routine practice
• Integrate
• Phased implementation
• Realize the added value

Data checking Forcing functions Logical
control Calculations/formulae
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Structured Data and Quality

• Benefits of structured data in reports are known
• Lack of ambiguity and consistency
• in our reports (efficient communication)
• Leads to facilitating best practice
• (evidence based)
• How much is too much?
• How soon?

Quality
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Conclusion

• Structuring data facilitates effective
  communication and aids knowledge translation
• There is true value addition quality
• Increased relevance with potential integration of
  other modalities
• Image analysis (Qualitative, Quantitative)
• Molecular and genetic assays
• Radiology Pathology integration
• Leveraging your data structuring tool to realize
  the full potential of your LIS

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References

• Sackett DL et al Evidence based medicine what it
  is and what it isnt BMJ 199631271-72 (13
  January)
• Zerhoui EA. US biomedical research basic,
  translational and clinical sciences. JAMA
  20052941352-1358
• Bowen S, Zwi AB (2005) Pathways to
  evidence-informed policy and practice A
  framework for action. PLoS Med 2(7) e166.
• Simunovic M Baxter NN Lymph Node Counts in
  Colon Cancer Surgery, MD, MPH JAMA, November 14,
  2007Vol 298, No. 18 (Reprinted)
• Wong SL et al Hospital Lymph Node Examination
  Rates and Survival After Resection for Colon
  Cancer. JAMA. 2007298(18)2149-2154
  doi10.1001/jama.298.18.2149)
• Glasziou P Evidence based medicine does it make
  a difference? Make it evidence informed practice
  with a little wisdom BMJ 200533092 (8 January),
  doi10.1136/ bmj. 330. 7482.92-a