# Presentation: 053475

## Computer Applications for Improving Coded and Administrative Data: Implications for Research and Performance Measurement

- Jennifer Garvin, PhD RHIA CCS, Samar Jasser, MD, Dominic Roche, Mark Weiner, MD , Jo Ann Seppelt CRNP

## Session Objectives:

- Describe how the VALOR research database at the Philadelphia VA Medical Center was developed from data resulting from clinical care
- Describe current and potential uses of technology to improve data validation at the PVAMC
- Discuss at research studies that have used administrative and coded data from the VALOR database and how the accuracy of coded data affected determination of research subjects for at least one research study.
- Discuss how computer assisted coding technology could assist providers in accurate assignment of codes and thereby increase the accuracy of coded data used for multiple purposes.

## Introduction to Team Members

- Dominic Roche
- Mark Weiner, MD
- Samar Jasser, MD
- Jennifer Garvin, PhD RHIA CCS
- Jo Ann Seppelt, CRNP

## Overview of the Presentation

- VALOR database and it architecture
- Discussion of research done and lessons learned
- Processes associated with coded data
- Technologic tools and security
- Evaluation of current processes in light of future goals

## VALOR Database and Architecture

- Structure of VISTA
- Relationship to the Oracle Database
- Tools Available
- CacheSQL
- KBSQL
- MDE

## VALOR Database and Architecture

- Vista is an Operational database
- Tables (where is the MAP?)
- How are the tables developed
- Transformation of the data
- Valor and Austin contents
- (Study priorities) Its all about the details

## Database Use

- VA Potential
- Higher level uses of data
- Investigator specific data

## Improving Details

- Most Physician documentation is in the narrative which can not be used without a linguistic package
- Natural language processing vs. Required Value Forms
- Means to extract data from narrative
- Create a form to capture data (less attractive to physicians) vs. extracting data from the narrative

## Processes of Coding

- Codes generated by:
- Coding Staff
- Providers
- Administrative staff
- Use of codes
- VERA Patient Categories
- Reimbursement by Third Party Payers
- Quality Initiatives

## LocalVISNHDR

- Local Extractions
- QA
- Admin/Logistics
- Research
- VISN (Repeat from locals)
- HDR (Repeat from locals)

## Coding Staff

- All inpatient data is coded by professionally trained staff
- These coding professionals also have a few important tools:
- A well-developed encoder
- Current codes even though billing holds them for the VISTA update
- Coding reference material that is user-friendly

## Coding Staff

- Some outpatient data is reviewed and validated or recoded by professionally trained staff
- These coding professionals also have the important tools mentioned before:
- A well-developed encoder
- Current codes
- Coding reference material that is user-friendly

## Professional Coding Staff Summary

- Code (really check and recode) a minority of the outpatient records
- Training in the guidelines of ICD
- Access to:
- User-friendly encoders
- Coding references
- Significant education and training in ICD
- Current codes
- Coding staff use these tools as part of standard practice

## Provider

- Providers code the majority of the outpatient encounters
- However they have limited tools
- Limited training in coding
- Codes updates may be handled differently per each clinic
- Lexicode is used as the encoder but most providers use the encounter form rather than Lexicode.
- When the new codes are present in Lexicode they still may not be visible because the encounter form needs to be updated

## Use of Codes with Veteran Equitable Resource Allocation (VERA) 

- Priority groups for veterans seen at the medical center
- Coded data helps to determine which priority group the veterans is in
- So if we do not code a condition that would place the case into an appropriate priority group, this may result in less reimbursement for the medical center

## Coded Data is Used for Multiple Purposes

- We use the coded data for so many purposes:
- Reimbursement
- Resource allocation
- Performance measures
- Research
- We base all of these functions on data that in all likelihood has some degree of error.

## Case Identification Using VALOR 

- How accurate is VALOR?
- Accuracy = truth?
- If VALOR says a patient has asthma, then the patient has asthma
- Accuracy = true representation of the source data?
- If VALOR says a patient has asthma, then the source data includes a code for asthma for the patient

## How Accurate is VALOR?

- We have worked to make VALOR a true representation of the underlying data
- Physician patient communication/misunderstandings
- Busy doctors don’t code/document everything
- Idiosyncrasies of the clinical setting in which data is collected
- Ambiguity inherent in the practice of medicine
- Code creep-
- Early codes before diagnosis of gallstones is confirmed may suggest simple abdominal pain
- URI/bronchitis/tracheitis/pharyngitis/sinusitis all have similar symptoms

- Adapted and expended from  O’Malley KJ, Cook KF, Price MD,  KR, Hurdle JF,
-  Ashton CA Measuring Diagnoses: ICD Code Accuracy Health Services Research 2005. 40:1620-39.

## Idiosyncrasies in Data

- Research using VALOR must account for all of the realities inherent in the underlying data
- Absence of evidence is not evidence of absence
- Just because you don’t see evidence of a disease doesn’t mean the patient doesn’t have the disease
- To find patients with a certain disease, you need to consider all the ways the disease may be represented in diagnosis codes and ancillary test results
- The first instance of a disease in the database is not necessarily the time the disease first appeared

## Addressing the Idiosyncrasies

- Corrections for problems may increase the accuracy of the data, but make the analytical data set less generalizable
- Requiring an echocardiogram to make a diagnosis of CHF limits your cohort to people who were sick enough to require an echo – even among patients who turn out NOT to have CHF by echo
- Finding incident cases by limiting cohort to people who have existed in the system for a while without evidence for a disease of interest, and then suddenly a code for the disease appears.

## Research into Coding Data Accuracy

- Miller DR. Safford MM, Pogach LM. Who Has Diabetes? Best Estimates of Diabetes Prevalence in the Department of Veterans Affairs Based on Computerized Patient Data. Diabetes Care 27 (Suppl. 2):B10–B21, 2004
- Correlated different automated detection algorithms based on labs, medication use, and diagnosis codes with gold standard of self report in a national survey of veterans with diabetes.
- Concluded that the best method for determining prevalence of diabetes is a prescription for a diabetes medication in the current year and/or two or more ICD-9 codes in past 24 months form inpatient or ambulatory stays.
- Generalizability
- Can one automatically adapt this for other conditions (2 CHF diagnoses and/or a script for a CHF medication)?
- Does accuracy as a prevalence estimate work for cohort specification for quality assessment?

## Implications

- Feedback of the impossible or unbearable is needed.  Don’t be a lemming
- Where to do Research should be a No-Brainer
- VA has the data (Vista crushes all competition!)

## Important Issues to Consider

- There are many training requirements for researchers and there is a new initiative requiring an annual checklist completion to certify that research data is secure and private
- _[http://www.research.va.gov/about/default.cfm](http://www.research.va.gov/about/default.cfm)_

## Research Studies Using Coded Data

- Structured Opioid Therapy for Chronic Pain in Primary Care: Characteristics and Outcomes of Patients at High Risk for Substance Abuse *(Wiedemer, Moriarty, Mooar, Harden, Arndt, Gallagher)*

**Primary Aim** – To describe and compare the demographic, medical, *psychiatric*, and social profiles of two groups of primary care patients who were referred to an opioid renewal clinic, one with documented aberrant drug-taking behaviors, and one for other reasons.

**Method **– A retrospective study utilizing data extraction software (“MUMPS”) from the Center for Health Equity Research Program (CHERP) to identify comorbidities by ICD-9-CM coding incidents from a study patient’s electronic medical record.

**Result** **– **14.5% of the chronic pain study patients (in both groups combined) were labeled with the psychiatric comorbidity of schizophrenia.

****** ****HIGHLY UNLIIKELY RESULT**

## Research Studies Using Coded Data

**Follow-up Investigation: **To investigate the actual prevalence of schizophrenia in this population by conducting a manual chart review of the patients who were identified by ICD-9-CM coding incidents to have schizophrenia.

**Method **– Various data entry fields in a patient’s chart were examined by an independent reviewer for relevant clinical content.  These fields included:

******  ****Service-connection status and related disability **

******  ****Ongoing clinical “Problem List,” as (ideally) updated ****	****by any treating clinician **

******  ****Clinician progress notes containing narrative-style ****	****clinical data from a given encounter**

## Research Studies Using Coded Data

- Research Studies Using Coded Data

******  ****Those with fewer than 3 ICD-9 coded entries did ****	****not truly have schizophrenia**

******  ****58% (n=18) of the patients without schizophrenia, **
**	****but who had 3 or more coding incidents for a ****	****diagnosis of schizophrenia, were found to have ****	****diagnoses of cocaine abuse/dependence**

******  ****35% (n=11) of them were found to have a corrected ****	****diagnosis of PTSD **

## Research Studies Using Coded Data

- Research Studies Using Coded Data

-   IT-based automated data extraction resulted 	in an 	inconceivable prevalence of 	schizophrenia in this chronic pain population

-   This rate dropped significantly when charts 	were 	manually checked for clinical validity of 	this diagnosis.

## Research Studies Using Coded Data

**IT/Manual Review Discrepancies: **
**	****	****Potential Causes **

**  ****Clinician-centered error:**

****** ***Any single* coding incident of a given diagnostic label found 	*anywhere* within a patient’s electronic chart, entered by *any* 	caregiver (in the outpatient setting), is flagged by the Mumps 	extractor as relevant to a patient’s clinical history

******  **Traditional association of ICD-9-CM codes primarily with payment 	and bureaucratic considerations rather than definitive clinical 	assessment with strict parameters of selection

## Research Studies Using Coded Data

**IT/Manual Review Discrepancies: **
**	****	****Potential Causes **

**  ****Disease specific considerations**

****** **Frequently, an accurate psychiatric diagnosis requires ongoing 	refinement with longitudinal observation, incorporating stress 	reactions and treatment responses over time ** **

****** **EXAMPLE: Acute cocaine intoxication can clinically mimic the 	disorganization and hallucinatory symptoms of the acute psychosis
- 	of schizophrenia at a single point in time of observation

****** **Evolution of diagnostic criteria (e.g. PTSD)

## Research Studies Using Coded Data

**IT/Manual Review Discrepancies: **
**	****	****Potential Causes **

**  ****Method-specific limitations**

****** **Regardless of specialty or disease, there is a qualitative flexibility and 	complexity that is inherent to clinical assessment and 	diagnostic labeling that cannot be captured by simple binary 	identification ** **

****** **Accurate disease-patient identifications will likely require disease-by-
- 	disease basis formulation of coding criteria

## Coded Data in Primary Care  and Informatics 

- My roles :
- provider
- primary care informatics
- ICD and CPT codes are used for interfaces with quality measurement and clinical reminders
- Feedback regarding correct coding

## Clinical Informatics Related to Codes

- Miscoded diagnoses effect
- Individual Provider Reports
- EPRP results
- Quality Measurements
- National Reports
- IHD

## Difficulties in Primary Care

- There are multiple barriers to good coding:
- There may be a delay in the implementation of new codes
- There are individual lists created for each clinic and when new codes are added, each form must be individually updated
- Within the lexicon codes are available but difficult to locate

## Computer-assistive Coding Technology

- Computer-assisted coding
- Natural language processing
- Mapping
- Using a clinical reference terminology we can map terms to codes

## Computer-assistive Coding Technology

- Things to consider:
- In both of these instances we depend on the medical record
- Have we analyzed the document that we are reading via NLP or from which we are mapping?
- Have we evaluated the accuracy of the information in the text:
- Are “history of” in text equivalent to coded data history of?
- Are the conditions in the problem list accurate in terms of the definitions of the UHDDS?

## Does the HIPAA Privacy Rule permit the creation of a database for research purposes through an Institutional Review Board (IRB) or Privacy Board waiver of individual authorization?

**Answer**** --**Yes. A covered entity may use or disclose protected health information without individuals’ authorizations for the creation of a research database, provided the covered entity obtains documentation that an IRB or Privacy Board has determined that the specified waiver criteria were satisfied. Protected health information maintained by a covered entity in such a research database could be used or disclosed for future research studies as permitted by the Privacy Rule – that is, for future studies in which individual authorization has been obtained or where the Rule would permit research without an authorization, such as pursuant to an IRB or Privacy Board waiver.

- http://www.hhs.gov/hipaafaq/permitted/research/305.html

## Tenets of Using VA Data for Research

- Investigator and staff training
- Regulatory oversight
- IRB involvement
- Assurances about usage
- Auditing of who saw which data
- Physical and electronic security
- Special situations – VA data outside the VA??

## Investigator and Staff Training

- VHA Privacy Policy Training
- VA Cyber security Awareness Training
- VA Research Data Security & Privacy
- Good Clinical Practices/Human Subject Protection Training

## Regulatory Oversight

- IRB involvement early
- Even work preparatory to research that involves patient identifiers requires forms on file with the IRB
- For formal protocols, written assurances regarding access controls on data, storage plans and backup mechanisms are required
- Data security Checklist –
- I certify to the best of my knowledge that all VA sensitive information associated with the research study entitled “xxx” and approved by the Research and Development Committee on “mm/dd/yy” is being used, stored and security in accordance with the applicable VA and VHA policies and guidance.

## Mechanisms to Insure Data Protection

- Don’t store data on desktops or laptops
- Set up a central server in a secure space with all necessary data and analytical packages
- Investigators and analysts don’t have a need to remove data
- Access controls and audit logs are centralized
- If data must be transferred, ensure it is on a computer/storage device with FIPS 140-2 whole disk encryption
- Make sure you can document encryption of the device

## Can VA data be used outside the VA?

- Yes, but...
- Requires full transparency with IRB and involvement of Information Security Officer and Privacy Officer
- Full disk encryption of laptops and desktops
- Servers on which data is stored must be FISMA (Federal Information Security Management Act) certified, a detailed review by an outside agency that explores technical, physical and cultural aspects of data security

## Research Options

- Researchers need to give feedback about security to the ORO to make sure that the measures are feasible and not prohibitive to research.
- Academic affiliates can undertake the research  in other venues- we can not make the work so difficult that they find another venues than the VA

## Important Issues to Consider

- There are many training requirements for researchers and there is a new initiative requiring an annual checklist completion to certify that research data is secure and private
- _[http://www.research.va.gov/about/default.cfm](http://www.research.va.gov/about/default.cfm)_

## Important Issues to Consider

- VA Mission/Vision/Core values call for continual improvement and innovation:
**Mission Statement *****"To care for him who shall have borne the battle and for his widow and his orphan.“***
**Vision: **We will strive to meet the needs of the Nation’s veterans and their families today and tomorrow by: (1) becoming an even more veteran-focused organization... (3) continuously benchmarking the quality and delivery of our service... use innovative means and high technology to deliver world-class service... (4) fostering partnerships with veterans organizations, the Department of Defense and other federal agencies, state and local veterans organizations, and other stakeholders to leverage resources and enhance the quality of services provided to veterans.

## Important Issues to Consider

- With the VA Mission/Vision/Core values in mind the impact of information technology and related policies on research must be a central consideration
- Veteran’s consent to have research done because they believe that they and others derive benefit
- What are the repercussions on our veterans if good quality data is not derived?
- What impact does the impedance of research have on our veterans, their families, and the support we as a health care system strive to provide?

## Summation

- IT applications can assist better code accuracy
- When we use IT (CAC, Mapping) we must factor in the quality of source documents and realign our current practices
- IT and research are working evermore closely as we have more and more technology in play with care giving and healthcare operations
- Research is central to achieving our mission and vision
- It is vital that the impact of policies and strategic IT developments with regard to research be considered

## Questions? 

- Questions
- Thank you for your attention!
- Please contact _[jennifer.garvin@va.gov](mailto:jennifer.garvin@va.gov)__ if you have further questions_