Staying Afloat:

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• Staying Afloat
• Extending Lifelines for HIM Practice

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• Dynamic Data Capture and Sharing
• Kathy M. Johnson, RHIA
• Care Communications, Inc.
• kmj_at_care-communications.com
• 312-229-7167

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Dynamic Data Capture Sharing

• Review data sources and data sets including
  limitations
• Understand the considerations for effective
  graphic data display
• Become familiar with data analysis tools
• Appreciate the role HIM leadership can
  effectively play meeting business intelligence
  and clinical practice management data needs
• Identify steps leading to collaborative work
  relationships with IT, Quality Improvement and
  service areas to achieve organization goals
• Apply analytic and reporting techniques with real
  world data management scenarios

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• We need research and development focused on
  information integrity, data standardization, data
  capture, and use.
• Linda L. Kloss, RHIA FAHIMA, CEO AHIMA

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• In God we trust.
• All others bring data.
• W. Edwards Deming

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SKILLS
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SKILLS

• Domain
• Health Information Literacy Skills
• Health Informatics Skills Using EHR/PHR
• Privacy and Confidentiality of Health
  Information
• Health Information/Data Technical Security
• Basic Computer Literacy Skills

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Certified Health Data Analyst

• A New Role

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Clinical Data Manager

• Clinical Data Manager will combine practical
  clinical experience in both outpatient and
  inpatients settings with experience in managing
  healthcare data and data quality from its
  creation in the real world through its electronic
  representation and manipulation to its
  presentation to health care practitioners and
  quality managers. Clinical Data Manager must
  have experience interfacing between clinical
  users and IT professionals for optimizing
  effectiveness.

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Clinical Data Manager Duties Responsibilities

• Be responsible for the implementation, operation
  and enhancement of clinical data validation
  systems and processes
•  Participate in ongoing clinical data analysis
  and documentation
•  Participate in the ongoing invention, testing
  and use of clinical data quality systems
•  Work closely with product management in clinical
  data areas
•  Work closely with clinical informatics
  specialists in knowledge representation areas

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Clinical Data Manager Required Experience and
Skills

• Bachelors Degree in a patient care or health
  information management discipline or work
  equivalent
• Experience working directly with physicians,
  nurses, and other healthcare personnel
• Minimum of 2 years of clinical data management
  experience
• Working knowledge of health information systems
• Working knowledge of standard clinical
  terminologies and coding systems
• Ability to analyze user requests, define
  requirements, develop project plans and report
  conclusions
• Ability to work creatively and flexibly, both
  independently and as part of a team
• Attention to fine details and work processes
• Desire and ability to learn new skills, systems
  and processes
• Good organizational, and written and oral
  communications skills

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Clinical Data Manager Desirable Experience and
Skills

• Working knowledge of hospital and physician
  practice operations
• Experience with relational database systems
• Experience with xml documents
• Experience with HL7 standards
• Experience with vocabulary browsers and authoring
  tools
• Experience with national performance improvement
  initiatives

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Lifelong Learning

• Ability to use life experiences to build
  skills and knowledge needed to achieve ones
  current and future goals.

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Business Data Analytics IIIJob Responsibilities

• Work with the business architect and other
  planners to assess current capabilities and help
  to decompose high-level requirements
• Identify and define application requirements and
  use cases
• Assist in translating requirements and use cases
  into test conditions and expected results for
  product, performance, and user acceptance testing

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Business Data Analytics IIIJob Responsibilities

• Participate in transitioning the requirements and
  use cases to the designers, and ensure a clear
  and complete understanding of the requirements
• Assist in translating requirements and use cases
  into test conditions and expected results for
  product, performance, and user acceptance testing

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Business Data Analytics IIIJob Responsibilities

• Participate in quality management reviews to
  ensure they fulfill the requirements
• Serve as a resource for the human performance
  architects as they evaluate training and
  performance support needs and design the training
  and performance support products

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Business Data Analytics IIIJob Responsibilities

• Determine all testing environment requirements
  and tools
• Work with the deployment lead to plan the
  application pilot
• Measure and monitor progress during each test to
  ensure that the application is tested, validated,
  and piloted on time and within budget and that it
  meets or exceeds expectations

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Business Data Analytics IIISkills Requirements

• Ability to develop product requirements based on
  input gathered from a variety of sources
  including analysis results and feedback from the
  user community
• Ability to analyze and design business processes

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Business Data Analytics IIISkills Requirements

• Ability to develop test conditions and expected
  results based on the application requirements
• Ability to review project deliverables for
  completeness and quality, and compliance with
  established project standards

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DATA COLLECTION

• Data Analytics Motto

• Data is only as valuable as its quality.

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DATA, DATA, DATA

• Diagnostic Data
• International Classification of Diseases, 9th
  Revision, Clinical Modification
• Diagnosis Related Group
• Major Diagnostic Category

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DATA, DATA, DATA

• Procedural Data
• ICD-9-CM
• Current Procedural Terminology (CPT)
• Healthcare Common Procedural Coding System
  (HCPCS)
• Ambulatory Payment Classification (APC)

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DATA, DATA, DATA

• Drug Data
• National Drug Code (NDC)
• http//www.fda.gov/cder/ndc/database/default.htm
• Therapeutic Classification (developed by the
  American Hospital Formulary Service (AHFS)
• http//www.ovid.com/site/products/ovidguide/diftdb
  .htmTC

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DATA, DATA, DATA

• Administrative Data
• Revenue Codes
• Place of Service Codes
• Claims Processing Codes
• Medicare Code Editor (MCE) and Outpatient Code
  Editor (OCE)

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DATA, DATA, DATA

• External Data
• Healthcare Effectiveness Data and Information Set
  (HEDIS)
• Medicare Provider Analysis and Review (MEDPAR)
• Part B Utilization Data
• http//www.cms.hhs.gov/MedicareFreeforSvcParts
  AB/04_ MedicareUtilizationforPartB.asp

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Data Capture Sharing

• Exercise 1

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CANCER REGISTRY DATA

• Evaluate patient outcome, quality of life,
  satisfaction issues and implement procedures for
  improvement
• Provide follow-up information for cancer
  surveillance
• Calculate survival rates by utilizing various
  data items and factors
• Provide information for cancer program activities
• Analyze referral patterns
• Allocate resources at the health care facility,
  the community, region or state level
• Develop educational programs for health care
  providers, patients and the general public
• Report cancer incidence as required under state
  law
• Evaluate efficacy of treatment modalities

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CANCER REGISTRY DATA

• Data Inquiry A surgeon who sits on the cancer
  committee and specializes in breast cancer
  surgery asks the cancer registrar to provide him
  with the case counts, stage of disease at
  diagnosis, and screening mammography data on
  breast cancer for the following years 2005-2008.
  The cancer committee wants to propose to
  administration development of an educational
  program on early detection of breast cancer with
  possible institution of a mammographic screening
  program to coincide with breast cancer awareness
  month in October of each year. This would be a
  quality improvement initiative of the cancer
  committee for 2010.

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CANCER REGISTRY DATA
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CANCER REGISTRY DATA
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CANCER REGISTRY DATA

• Analysis There is a large of stage IV breast
  cancer cases. The registrar investigated further
  and noted a large of the patients are Medicaid
  recipients. This population may be hampered in
  seeking health care and detection at an earlier
  stage of disease.

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Data Capture Sharing

• Exercise 2

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Data Capture Sharing

• All patients with a diagnosis of ketoacidosis AND
  Present On Admission Indicator (POA) of N since
  January 1 2009
• All patients with possible septicemia AND an
  unanswered physician query
• All do not final bill patient accounts over 60
  days

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DATA COLLECTION

• What are you, as Health Information Management
  (HIM) professionals, uniquely qualified to share
  about medical records and healthcare
  documentation that may help clinicians decide
  what data to collect and how to coordinate data
  collection for a special study including a
  pilot abstraction effort to assess feasibility
  and determine optimal use of available resources?

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Diagnosis Related Group Data Analysis

• Case mix index
• Mathematically the average of all DRG weights
  assigned during a period of time
• Provides an index of the DRG frequencies by
  relative weight
• Information about the patient mix treated

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Diagnosis Related Group Data Analysis

• Case Mix Index (CMI) is then calculated by
  averaging the MS-DRG weight of patients
  discharged within the calendar year, i.e., the
  sum of the MS-DRG weights divided by the number
  of patients.

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Diagnosis Related Group Data Analysis

• The Case Mix Index (CMI) can be used to adjust
  the average cost per patient (or day) for a given
  hospital relative to the adjusted average cost
  for other hospitals by dividing the average cost
  per patient (or day) by the hospital's calculated
  CMI. The adjusted average cost per patient would
  reflect the charges reported for the types of
  cases treated in that year.
• For example, if Hospital A has an average cost
  per patient of 1,000 and a CMI of 0.80 for a
  given year, their adjusted cost per patient is
  1,000 / 0.80 1,250. Likewise, if Hospital B
  has an average cost per patient of 1,500 and a
  CMI of 1.25, their adjusted cost per patient is
  1,500 / 1.25 1,200.
• Therefore, if a hospital has a CMI greater than
  1.00, their adjusted cost per patient or per day
  will be lowered and conversely if a hospital has
  a CMI less than 1.00, their adjusted cost will be
  higher.

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Diagnosis Related Group Data Analysis

• The Journal of Antimicrobial Chemotherapy (study
  published in 2008)
• Is there a correlation between case mix index and
  antibiotic use in hospitals?
• University Hospital, Zurich, Switzerland

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Diagnosis Related Group Data Analysis

• Antibiotic use correlated with CMI across various
  specialties of a university hospital and across
  different acute care hospitals.
• For benchmarking antibiotic use within and across
  hospitals, adjustment for CMI may be a useful
  tool in order to take into account the
  differences in hospital category and patients
  morbidities.

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Diagnosis Related Group Data Analysis

• Monitor CMI over time using a baseline to help
  track any deviations
• Is the CMI steady, increasing, or decreasing in a
  sharp or sudden decline?
• A common threshold of 2 change, either positive
  or negative in a given period may trigger further
  investigation

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DATA CAPTURE SHARING

• Exercise 3

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Diagnosis Related Group Data Analysis

• Request from VP Finance (CFO) in a 570 bed
  hospital
• In conjunction with the review of our clinical
  documentation improvement service contract we are
  trying to compare our coding and documentation
  performance with others.  Please obtain the
  Medicare case mix index of other community
  hospitals of like size (non-teaching).

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Hospital A CMI Comparison
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Hospital B CMI Comparison
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Hospital C CMI Comparison
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DRG DATA ANALYSIS

• MS-DRG Pair or Triplet Comparison
• N (CC MS-DRG) x 100 of CC MS-DRG
  
• N (CC MS-DRG) N (no CC MS-DRG)
• N the number of discharges in that MS-DRG

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DRG DATA ANALYSIS

• CC/MCC Capture Rate
• N(CC MS-DRG) N (MCC MS-DRG) x 100
• N (all MS-DRG categories)
• N the number of discharges in that MS-DRG

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DATA CAPTURE SHARING

• Exercise 4
• Internal Data
• Non-probability Sampling

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DATA CAPTURE SHARING

• Non-probability sampling
• Quota
• Convenience
• Judgment
• Probability Sampling
• Simple Random
• Stratified Random
• Systemic
• Cluster
• Statistical software for selecting a random
  sample (http//oig.hhs.gov/organization/oas/ratsta
  ts.asp)

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DATA ORGANIZATION TOOLS

• Microsoft Office Excel
• Relational database management
• Microsoft Office Access
• Data Dictionaries
• http//www.ahima.org/infocenter/practice_tools.asa
  p
• Structured Query Language (SQL)

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DATA ORGANIZATION TOOLS

• Systems are typically built by combining
  databases or moving data from one database to
  another using an interface engine.
• The interface process is also called data
  movement.

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DATA ORGANIZATION TOOLS

• Managing data movement entails
• Identifying the data to be moved
• Setting up file transfers from source system
  (starting point) to the target system (ending
  point)
• Monitoring the move into a new format or system
• Assess that the quality has been maintained
  through the use of profiling software

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Principles for Collecting Data in a Busy Clinical
Setting

• Seek usefulness, not perfection in the
  measurement.
• Use a balanced set of process, outcome and cost
  measures.
• Keep measurement simple think big, but start
  small.
• Use qualitative and quantitative data.
• Write down the operational definitions of
  measures.
• Measure small, representative samples.
• Build measurement into daily work.
• Develop a measurement team.
• See Nelson, Splaine, et al., Building
  Measurement and Data Collection into Medical
  Practice, Annals of Internal Medicine, March 15,
  1998.

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A Logical Approach to Planning Data Collection

• Write down the CRITICAL QUESTIONS that must be
  answered.
• Design DUMMY DATA DISPLAYS that will be used to
  answer your critical questions.
• Make a LIST OF VARIABLES that must be collected
  (to fill in the dummy data displays) and write
  down conceptual and operational definitions for
  each one.
• Write a SIMPLE PROTOCOL and follow it.
• See Nelson, Splaine, et al., Building
  Measurement and Data Collection into Medical
  Practice, Annals of Internal Medicine, March 15,
  1998.

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Design Principle

• Information System Design Principle Capture data
  at lowest level and aggregate up to higher levels
  for cascading metrics throughout system

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Cascading System of Measures
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A Cascading Set of Strategic Measures
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Take Home Points

• Goal should not be perfect data collection.
• Collect the best data you can within constraints
  of available resources.
• Use your data for multiple purposes.

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THE PURPOSE

• The purpose of data analytics is to
• answer critical questions and
• guide intelligent actions.