Requirements for Useful Data Common data models Standardized - - PDF document

1

Afternoon Breakout Session:

Using Big Data to Evaluate Clinician-sensitive Outcomes

Connie White Delaney, PhD RN, FAAN, FACMI – Objective: To identify, in this more “nuts and bolts” session, key strategies and tactics that investigators should consider in using big data to evaluate clinician-sensitive outcomes.

Requirements for Useful Data

Common data models Standardized coding of data Standardized queries

http://www.pcornet.org/resource-center/pcornet-common-data-model/

2

4

Vision – Inclusion of Nursing & Other Interprofessional Data

Clinical Data NMDS Management Data NMMDS Other Data Sets

Continuum of Care

Example Flowsheet Flowsheet Data Challenges

Volume of data There are multiple measures for the same concepts – Different people building screens – Software upgrades – Discipline/ practice specific needs

No information models exist

– Data driven information modeling required

3

Information Model Development Process

Identify Clinical Data Model Topic Identify Concepts Map Flowsheets to Concepts Present Validate

UMN – Academic Health Center CDR

Flowsheets constitute 34% of all data

• 14,564 measure types
• 2,972 groups
• 562 templates
• 1.2 billion observations
• 2,000 measures cover

95% of observations

Sample Data Source

• Clinical Data Models

T

562

Groups

2,696

Flowsheet Measures 14,550 Data Points 153,049,704

• Flowsheet Data from

10/20/2010 - 12/27/2013

• 66,660 patients
• 199,665 encounters

4

Development Process Details

Identify clinical topic important to researchers/

• perations

Develop a list of concepts from research questions, clinical guidelines and literature Search for concepts in templates/groups/measures – Search associated groups for additional concepts Add matched concepts to running list Categorize into assessment and interventions Organize into hierarchy Combine similar concepts that have similar value sets Validated by a second researcher Pain Neuromusculoskeletal System Falls/ Safety Respiratory system Peripheral Neurovascular (VTE) Vital Signs, Height & Weight Genitourinary System/ CAUTI Aggression and Interpersonal Violence Pressure Ulcers Psychiatric Mental Status Exam Cardiovascular System Substance Abuse Gastrointestinal System Suicide and Self Harm

Flowsheet Information Models Example Information Model

5

What is i2b2?

Informatics for Integrating Biology and the Bedside (i2b2) Framework for research cohort discovery

14 information models - approximately 81 million new rows i2b2 OBSERVATION_FACT table I2b2 – every row has to be unique

Create Flowsheet Ontology in i2b2

Informatics Issues Encountered

Redundancy – flowsheet and value sets

– 7 blood pressure and 10 heart rate measures – Mapped multiple flowsheet measures to same concept

Variations in value sets

– Created a unique list of all for same concept

Measures with similar names represented different concept – i.e. search “display name” – Urine Output

– R IP URINE FOLEY – URINE OUTPUT – URINE OUTPUT.MODIFIED ALDRETE – R NEPROSTOMY URINE OUTPUT – URINE OUTPUT (ML) 0-unable to void and uncomfortable 1-unable to void but comfortable 2-has voided, adequate urine

• utput per device, or not

applicable

6

Technical Issues Encountered

Free text response

– Included name of measure, no data included in i2b2

Multi-response items

– Created a separate row OBSERVATION_FACT table

Choice list - comment or “other” option

– Created a row for each type of comment

Numeric response measures - units of measure not clearly identifiable

– Modified name to include unit of measure

Mapping issues

– Changed names to exclude “* | / \ “ < > ? %” – Constructed synthetic value item id’s

Names must be unique within first 32 characters

– Changed from fully specified names to multiple levels

Certified WOC Nurses – Incontinence & Wounds

Outcome Variables Description Pressure Ulcers Total number of pressure ulcers (M0450 a-e) Stasis Ulcers Total number stasis ulcers (M0470/ M0474) Surgical Wounds Total number of surgical wound (M0484/

M0486)

Urinary Incontinence Presence/management of urinary incontinence or need for a catheter (M0520) Urinary Tract Infection Treated for UTI in past 14 days (M0510) Bowel Incontinence Frequency of bowel incontinence (M0540)

Improved/ Not Worse (Stabilize) Outcomes

Score Bowel Incontinence Frequency Improved Not Worse (Stabilize) Very rarely /never has BI or has ostomy for bowel elimination 1 Less than once weekly 2 One to three times weekly 3 Four to six times weekly 4 On a daily basis 5 More often than once daily

7 Individual Patient Outcomes

Using Data Visualization to Detect Client Risk Patterns

Monsen, K. A. et al., 2014 Each image (sunburst) was created in d3 from public health nursing assessment data for a single

• patient. Data were generated by

use of the Omaha System signs and symptoms and Problem Rating Scale for Outcomes Key:

• Colors = problems
• Shading = risk
• Rings = Knowledge, Behavior, and

Status

• Tabs = signs/symptoms

Documentation patterns suggest a comprehensive, holistic nursing assessment. Kim et al. found that the presence

• f mental health signs and

symptom tends to be associated with more diagnostic problems and worse patient condition Kim, E., Monsen, K. A., Pieczkiewicz, D. S. (2013). Visualization of Omaha System data enables data-driven analysis of outcomes. American Medical Informatics Association Annual Meeting, Washington D. C. Funded by a gift from Jeanne A. and Henry E. Brandt.

Using Data Visualization to Detect Nursing Intervention Patterns

Each image (streamgraph) was created in d3 from longitudinal public health nursing intervention data for a single patient. Data were generated by use of the Omaha System in clinical documentation Key:

• Colors = problems
• Shading = actions (categories)
• Height = frequency
• Point on x-axis = one month

From 403 images, 29 distinct patterns were identified and validated by clinical experts Documentation patterns suggest both a unique nurse style and consistent patient- specific intervention tailoring Monsen, K.A., Hattori, K., Kim, E., Pieczkiewicz, D. (In review). Using visualization methods to discover nurse-specific patterns in nursing intervention data. Streamgraph development funded by a gift from Jeanne A. and Henry E. Brandt. Monsen, K. A. et al., 2014

8

Do PHNs Tailor Interventions?

Public Health Nurses Signature Styles? Challenges of Secondary Analysis of Big Data

Database/Data Dictionaries Data Extraction

– Feature selection

Data Cleansing

– Missing values, outliers, errors, redundancies, transformation…

Analysis

– Exploratory

• Statistics, data mining

– Predictive

• Machine learning
• Algorithms

Model Evaluation

– Testing on new data Extraction Cleansing Analysis Testing

9

Requirements for Useful Data

Common data models Standardized coding of data Standardized queries

Vision for Data in a Clinical Data Warehouse

Clinical Data NMDS Management Data NMMDS Other Data Sets

Continuum of Care

Celebrating our foundation for “Big Data/Data Science”

Global standards eMeasures EHRs Magnet, etc Resources Workforce

10

Nursing Minimum Data set (NMDS) National Standard – SNOMEDCt, LOINC

Werley, HH & Divine, E., & Zorn, C. (1988). Nursing Minimum Data Set Data Collection

• Manual. University of Wisconsin, Milwaukee,

WI

Huber D, Schumacher L, Delaney C. Nursing management minimum data set (NMMDS). J Nurse Adm. 1997;27(4):42-48.

30

z.umn.edu/bigdata