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Era Kim 1 Karen Monsen , PhD, RN, FAAN 1,2 David Pieczkiewicz , PhD 1

Methods for Visualizing Omaha System Data. Era Kim 1 Karen Monsen , PhD, RN, FAAN 1,2 David Pieczkiewicz , PhD 1 1 U of M Institute for Health Informatics 2 U of M School of Nursing. In t roduction. Omaha System.

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Era Kim 1 Karen Monsen , PhD, RN, FAAN 1,2 David Pieczkiewicz , PhD 1

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  1. Methods for Visualizing Omaha System Data Era Kim1 Karen Monsen, PhD, RN, FAAN1,2 David Pieczkiewicz, PhD1 1U of M Institute for Health Informatics 2U of M School of Nursing

  2. Introduction Omaha System • Standardized classification for nursing interventions that consists of three components: problem, intervention, and outcome evaluation • Omaha System data: Rich and longitudinal

  3. Introduction Why Use Data Visualization? • Rapid and intuitive pattern detection • Human brain interprets graphical data more easily than numbers or text • Comprehensive and holistic data examination • Statistical analysis alone can mask hidden patterns • Generate research questions in a data-driven way Surveillance Case Management Teaching/Counseling/Guidance

  4. Income problem number P B 0 0 0 1 S S 0 1 0 1 Methods Problem number in the Omaha System Problem Signs and Symptom number for income problem in the Omaha System Signs and Symptoms Alpha-Numeric Codes for Omaha System • Simple mechanism for use of Omaha System terminologies electronically to facilitate data processing and communication • 6-digit alpha numeric code • 2-digit prefix • PB for Problem • SS for Signs and Symptoms • CG for Intervention Category • TG for Intervention Target • Example • Income problem, the 1st defined problem in Omaha System

  5. Methods Relational Database for Omaha System • Easy to query • Normalization and relations • Less dependency between entities and easy to maintain • Assured data consistency • Improved data quality • Used MySQL Workbench 5 • Designed a logical model • Exported a DDL (Data Definition Language) script to create physical database structure

  6. Methods CLEAN DATA Data Migration Strategy • Migrate data from spreadsheets into the Omaha System Database • Use SQL statements and Java programs • Steps • Clean data • Conduct visual data validation through the original spreadsheet data • Extract data • Load spreadsheets data into temporary tables • Transform & Load data into target tables • Convert wide-format to long-format • Assure referential integrity and data consistency • Validate data TRANSFORM & LOAD DATA INTO TARGET TABLE EXTRACT DATA VALIDATE DATA

  7. Methods Dynamic Java-based Web Application 3-Tier Architecture (Client-Middle-Database tier) MVC (Model-View-Controller) design pattern using Java Web Application Server (Apache Tomcat 7.0) HTTP JDBC Omaha Database (MySQL 5) • Stream Graphs drawn by • D3 • JavaScript • Interactivity • Highlights • Tooltips

  8. Visualizing Omaha System Data Stream Graph: by Intervention Category by Nurse • A type of stacked graph where the baseline is free • Layerindicates each category-target-problem triplet • Layer thickness is determined by the frequency of each triplet • Hue (base color), value (color brightness), and saturation (color deepness) encoded the Omaha System intervention category, intervention target, and problem, respectively Surveillance Teaching/Counseling/Guidance Number of Interventions Case Management Time Period between Patient Admission and Discharge

  9. Visualizing Omaha System Data High Case Management vs. Low Case Management Pattern differences between public health nurses were detected through visual inspection

  10. Visualizing Omaha System Data Stream Graph: by Problem by Patient • Hue encodes the Omaha System problem • Blue: Postpartum;Gray: Pregnancy;Magenta: Family Planning;Purple: Substance Use;Cyan: Abuse; Green: Caretaking/Parenting; Yellow: Mental Health; Orange: Residence;Red: Income • Value encodes the Omaha System intervention category • example surveillance surveillance case management case management teaching, guidance, and counselling Number of Interventions Time Period between Patient Admission and Discharge

  11. Visualizing Omaha System Data Income and parenting problems persist Most graphs revealed that income and parenting problems persisted over time and interventions for the problems were rarely reduced in frequency

  12. Visualizing Omaha System Data Sunburst Graph status rating: 5 behavior rating: 4 • A multi-level pie chart • Hue encodes the Omaha System problem • The first, second, and third circles from the center indicate patient knowledge, behavior, and status rating for a problem • Value encodes the Omaha System rating scores • The rim of the wheel indicates signs and symptoms of a problem and has a hierarchical relationship to the colored problem segment Knowledge Rating: 3 sadness/hopelessness/decreased self-esteem caretaking/parenting income family planning pregnancy residence abuse apprehension/undefined fear substance use postpartum mental health purposeless activities difficulty managing stress

  13. Visualizing Omaha System Data Mental Health Signs vs. No Mental Health Signs The presence of mental health signs and symptom tends to associated with more diagnostic problems and worse patient condition MORE RISK LESS RISK

  14. Further Studies Data Visualization as a Data-Driven Research Method • Review and sort visualized data using online card sorting tool to detect differences • Generate hypotheses based on visual observations • Test them statistically • Evaluate the effectiveness of data visualization

  15. Q & A THANK YOU

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