Workflow-Based Data Reconciliation for Clinical Decision Support-Case of Colorectal Cancer Screening and Surveillance

7/28/2019 Workflow-Based Data Reconciliation for Clinical Decision Support-Case of Colorectal Cancer Screening and Surveillance

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2012 MFMER | slide-1/21

Workflow-based Data Reconciliation for

Clinical Decision Support: Case of

Colorectal Cancer Screening andSurveillance

Kavishwar Wagholikar, MBBS PhD1, Sunghwan Sohn, PhD1,

Stephen Wu, PhD1,Vinod Kaggal1, Sheila Buehler, RN CNP2,

Robert A. Greenes, MD PhD3,8, Tsung-Teh Wu, MD4,David Larson, MD MBA5, Hongfang Liu, PhD1,

Rajeev Chaudhry, MBBS MPH6, Lisa Boardman, MD7

1Biomedical Statistics and Informatics, 2Internal Medicine,4Anatomic Pathology, 5Colon and Rectal Surgery, 6Primary Care

Internal Medicine, 7Gastroenterology and Hepatology,

Mayo Clinic Rochester, MN3Biomedical Informatics, Arizona State University, Scottsdale, AZ;

8Health Science Research, Mayo Clinic, Scottsdale, AZ

AMIA-CRI 2013,

San Francisco CA,

21 March 2013


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CDSS

Data quality

Workflow analysis

Data reconciliation

Formative evaluation


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Data Quality and CDS

Deficient data quality can impact the clinical

decisions and consequently health outcomes

Data quality: completeness, accuracy,

Stetson et al. Assessing Electronic Note Quality Using the Physician Documentation Quality Instrument

(PDQI-9). Appl Clin Inform. 2012;3(2):164-74.

Weiskopf et al. Methods and dimensions of electronic health record data quality assessment: enabling

reuse for clinical research. J Am Med Inform Assoc. 2012 Jun 25.


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Data quality and data use

Factors influencing data quality:

motivation or purpose for the document

time available for documentation

use of schemas

expertise and qualifications of the documenter

documentation tools

Deeper understanding of the document is critical todetermine if the data is suitable for a particular task.

e.g. diagnostic coding by abstractors for insurancereimbursement lack sufficient accuracy and completenessfor clinical decision making.

Reiser SJ. The clinical record in medicine. Part 2: Reforming content and purpose. Ann Intern Med. 1991 Jun

1;114(11):980-5.

Rosenbloom ST, et al. Data from clinical notes: a perspective on the tension between structure and flexible

documentation. JAMIA. 2011 Mar-Apr;18(2):181-6.


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Workflow study to determine data quality

use workflow analysis :

i) to identify data sources

ii) and to design data reconciliation processes

in a CDSS for colorectal cancer prevention


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Challenges for the CRC CDSS

i) several parameters and logical decision stepsare involved

ii) the documentation is of inadequate quality or

iii) is in free-text form

iv) high degree of decision accuracy

Wagholikar K, et al. Clinical Decision Support for Colonoscopy Surveillance Using Natural Language Processing. IEEE

Healthcare Informatics, Imaging, and Systems Biology Conference. University of California, San Diego, CA; 2012.
http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11


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h/o IBD

CRCancer

yes

Colonoscopy.

Preparation

poor,

inadequate

Repeat in 1 or 2

days

PolypType

adequate,

good,

excellentno

hyperplastic

Adenomatous:

tubular, serrated

adenomaAdenomatous:

tubulovillous,

villous, SSA

Risk

Colonoscopy now

no

yes

>=40y

>=50yAge

Age Colonoscopy now

h/o CRCancer

Colonoscopy at 3yrs after thetime CRcancer was detected

no

yes IBD-consult if no

addendum

no

yes GI consult

no

h/o CRC

=1yr

1 year

Hyperplastic

polyp

group2group1

RecentCRPath is recent

Colorectal Endoscopy Pathology

Recommendation for colonoscopy are

indicated in years from Recent CRPath

CRrisk

10y 5y

no yes

>=50y && =40y

Age atRecent

cololoscopy

+10y

Age at Recentcolonoscopy

+5y

h/o CRCancer

no

yes

3y

h/o CRCsyndromeyes GI-consult if no

addendum

no

8h/o CRC

since

no

yes

Recent.Poylpyes

Recurrentloop

=10 monthsTimeSincePreviousColonoscopy (withadequate prep)

Recent

Colonoscopy.

Time

reportAbsent

>0 years


8/182012 MFMER | slide-8/21

Methods

Studied workflow analysis

Designed several processes for data reconciliation

To determine the utility of these processes,quantitative analysis after selectively

adding/removing each of reconciliation processes.Two parameters:

i. accuracy gain of the patient parameter reconciledby the process, and

ii. accuracy gain of the recommendations computedby the CDSS.

106 cases

Unertl KM,et al. Traversing the many paths of workflow research: developing a conceptual framework of workflow

terminology through a systematic literature review. JAMIA. 2010 May-Jun;17(3):265-73.


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Demographics

Coded Problem List

Free text Problem section

in Clinical notes

Coded Patient response

Questionnaire System

Pathology report

templatized free-text

Endoscopists dictated

free-text procedure note

GI Dept.

system

Pathology dept. system

findings

indications

Patient

RFID tag on biopsy

Referring

Care

provider

EHR

Endoscopist

R.Nurse

Pathologist

during procedure

before procedure

Care

providers

Annual

Questionnaire

Registration

Previous

Hospital Visits

Colonoscopy

visit

Biopsy

specimen

CDSS

Natural Language

Processing

Structured Data

Guideline rules

Recommendation

Data reconciliation

Workflow


10/182012 MFMER | slide-10/21

Colonoscopy

TV

TV

Clipart from Microsoft Office


11/182012 MFMER | slide-11/21

Colonoscopy

Structured event information

Specimen label

TV

TV

20-30 mins



12/182012 MFMER | slide-12/21

Colonoscopy

Endoscopy

dictation

TV

TV



13/182012 MFMER | slide-13/21

Results


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Conventionally, software engineering practicesinvolve workflow studies, when the software isprimarily for workflow support.

Our study demonstrates that when there is lackof adequate data quality, workflow studies of thedocumentation phase can help discover datareconciliation strategies that can resolve the

data deficiencies


15/182012 MFMER | slide-15/21

Lack of NLP sensitivity can be overcome withmulti-source data reconciliation. e.g. NLP on theendoscopy note for polyp size was effectivewhen supplemented with structured GI findings.

Findings of the workflow analysis obviated thelaborious effort to develop NLP for someinformation. e.g. the RNs structured recording

obviated NLP for detecting number of polypsfrom endoscopy note.


16/182012 MFMER | slide-16/21

Conclusion

Workflow analysis was useful to identify datareconciliation strategies to addressdocumentation gaps, which improved CDSSperformance.

Workflow-based data reconciliation can play animportant role in designing new-generationCDSS based on complex guideline models and

NLP.


17/182012 MFMER | slide-17/21

Questions

Contact and related resources: waghskATgmail.com
http://scholar.google.com/citations?user=_MlaqMsAAAAJ&hl=enhttp://scholar.google.com/citations?user=_MlaqMsAAAAJ&hl=en


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References

Stetson et al. Assessing Electronic Note Quality Using the Physician Documentation Quality Instrument (PDQI-9).Appl Clin Inform. 2012;3(2):164-74.

Weiskopf et al. Methods and dimensions of electronic health record data quality assessment: enabling reuse forclinical research. J Am Med Inform Assoc. 2012 Jun 25

Reiser SJ. The clinical record in medicine. Part 2: Reforming content and purpose. Ann Intern Med. 1991 Jun1;114(11):980-5.

Rosenbloom ST, et al. Data from clinical notes: a perspective on the tension between structure and flexibledocumentation. J Am Med Inform Assoc. 2011 Mar-Apr;18(2):181-6.

Unertl KM,et al. Traversing the many paths of workflow research: developing a conceptual framework of workflowterminology through a systematic literature review. J Am Med Inform Assoc. 2010 May-Jun;17(3):265-73.

Wagholikar K, et al. Clinical Decision Support for Colonoscopy Surveillance Using Natural Language Processing.IEEE Healthcare Informatics, Imaging, and Systems Biology Conference. University of California, San Diego, CA;2012.

Wagholikar K, et al. Clinical decision support with automated text processing for cervical cancer screening. J AmMed Inform Assoc. 2012; 19 (5), 833-839,

Wagholikar K ,et al. Formative evaluation of the accuracy of a clinical decision support system for cervical cancerscreening. J Am Med Inform Assoc. 2013. (in press)
http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11http://www.ncbi.nlm.nih.gov/pubmed/22542812http://www.ncbi.nlm.nih.gov/pubmed/22542812http://jamia.bmj.com/content/early/2013/04/04/amiajnl-2013-001613.fullhttp://jamia.bmj.com/content/early/2013/04/04/amiajnl-2013-001613.fullhttp://jamia.bmj.com/content/early/2013/04/04/amiajnl-2013-001613.fullhttp://jamia.bmj.com/content/early/2013/04/04/amiajnl-2013-001613.fullhttp://jamia.bmj.com/content/early/2013/04/04/amiajnl-2013-001613.fullhttp://jamia.bmj.com/content/early/2013/04/04/amiajnl-2013-001613.fullhttp://jamia.bmj.com/content/early/2013/04/04/amiajnl-2013-001613.fullhttp://www.ncbi.nlm.nih.gov/pubmed/22542812http://www.ncbi.nlm.nih.gov/pubmed/22542812http://www.ncbi.nlm.nih.gov/pubmed/22542812http://www.ncbi.nlm.nih.gov/pubmed/22542812http://www.ncbi.nlm.nih.gov/pubmed/22542812http://www.ncbi.nlm.nih.gov/pubmed/22542812http://www.ncbi.nlm.nih.gov/pubmed/22542812http://www.ncbi.nlm.nih.gov/pubmed/22542812http://www.ncbi.nlm.nih.gov/pubmed/22542812http://www.ncbi.nlm.nih.gov/pubmed/22542812http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11http://dx.doi.org/10.1109/HISB.2012.11

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Workflow-Based Data Reconciliation for Clinical Decision Support-Case of Colorectal Cancer Screening and Surveillance