Towards Understanding the Usage Pattern of Web-based Electronic Medical Record System

1. Towards Understanding the Usage Pattern of Web-based Electronic Medical Record System Xiaowei Li Vanderbilt University

2. Outline • Background • System Description • Usage Pattern Study • System-wide characteristics • User behaviors • Patient record access • Summary

3. What is EMR System • A computerized system that maintains patient data, connected to other clinical components, e.g., laboratory, pharmacy, decision support, etc. • Benefits: • Facilitate medical information sharing; • Reduce documentation errors; • Improve healthcare service delivery; • Research Issues: • Patient data privacy, comply with HIPPA and other policies. • Web portal security, e.g., web attacks, insider threats. • Performance optimization, etc.

4. Understanding Usage Pattern • First essential step towards building robust, secure, efficient web-based EMR system • Performance: component deployment, load balancing is related with system utilization, user request pattern, etc. • Security & Privacy: established system profiles, including usage pattern, help build anomaly detection system to defend against intrusion and insider threats. • Interface design: user navigation pattern. • A comprehensive study on the usage pattern of a large-scale web-based EMR system.

5. Outline • Background • System Description • StarPanel system • Web session model • Usage Pattern Study • Summary

6. StarPanel System • An integrated, longitudinal EMR system, deployed at Vanderbilt University Medical Center for over a decade. • Aggregates a number of patient data sources across clinical domains, including diagnosis, lab tests, radiology reports, etc. • A variety of user groups, including residents, physicians, technicians, clinic support staff, etc.

7. Web Session Model • Model definition: • Raw trace entry: { timeStamp, ipAddress, userid, actionModule (.cgi), parameters (patient record number)} • s(u, t): user u initiates session s at time t. • A: action set, R: record set. • Γs = (Γt1, Γt2,…, Γtn,): operation sequence in session s, whereΓ = (a, r), a ∈ A, r ∈ R ∪ {-}. Indicates an instance of clinical workflow. • Session extraction: • Starts with a login action, ends with another login by the same user. • Measure “active” session duration from login action to the last action performed.

8. Outline • Background • System Description • Usage Pattern Study • System-wide characteristics • User behaviors • Patient record access • Summary

9. System-wide Characteristic • overall system usage • Server workload, user population, data access correlate with each other showing a strong weekly pattern; • The system usage is highly consistent over the year, especially for the user population. • Indicates effective DoS detection system can be established based on accurate load prediction.

10. User Behavior (1) • Users behave differently • Examine user behaviors in terms of the number of sessions, the number of distinct actions, the number of patient record accesses. The variances across users are extremely large. • User profiles should be established for individuals or groups of users, based on user role or department affiliation information.

11. User Behavior (2) • User actions are different across sessions • Actions in user session is encoded into action vector. • Use cosine similarity to examine “distances” between consecutive sessions. • Indicates individual sessions cannot be used for training user profile.

12. User Behavior (3) • User actions are consistent within certain session window. • Let be aggregated action vector, starting from user session s with window size w • A stable user profile can be built over a carefully selected time frame and updated with time.

13. Patient Record Access (1) • Sessions target at a small group of records • Most users are using the system to service specific patients in each session. • User-Record pairs are sparse • Echoes the stable patient-caregiver structure. • Indicates fine-granularity access control policies can be established.

14. Patient Record Access (2) • Records have quite different “popularity” • The variances between the “popularity” of records are large. • Deviates from Zipf distribution (web object popularity model) • There is no “interest hotspot”, since the record access is based on the medical status and treatment of patients, rather than the “interest” of the caregiver.

15. Outline • Background • System Description • Usage Pattern Study • System-wide characteristics • User behaviors • Patient record access • Summary

16. Summary • The workload of EMR system is highly consistent and predictable over time. • EMR users behave quite differently. • For an individual user, his/her behavior exhibits fluctuation across consecutive sessions. Yet, the aggregated behavior over certain time frame is consistent. • Patient records have different access patterns and “popularity”. Pairing between users and records is extremely sparse. • We observe that general web-based system performance optimization and anomaly detection system cannot fully utilize EMR-specific system features, thus less efficient and effective. • One of future directions can be incorporating EMR-specific behaviors into anomaly detection system.