Graham Shaw

1. The use of ALN in tracking student-patient interactions during clinical rotations at geographically dispersed locations Graham Shaw School of Natural & Health Sciences and School of Graduate Medical Sciences, Barry University, Miami Shores, FL

2. Coauthors Stephen Morewitz, Ph.D Stephen J. Morewitz and Associates, Buffalo Grove, IL. Joel Clark, DPM Department of Surgery, California School of Podiatric Medicine, St Mary’s Medical Center, San Francisco, CA Journal of the American Podiatric Medical Association Vol 93. (2) 2003.

3. Contents • Context • Historical background • Traditional student logs • Limitations of traditional system - a need for change • Description of the web-based ALN used to track student-patient contact • Advantages of this model to the student (as well as to faculty/administrators) • Student perceptions of and attitudes toward the ALN as described. • Identify obstacles to the use of the ALN model. • Applications in other teaching disciplines

4. Podiatric Medical EducationThe curriculum • Years 1 and 2 • Basic Medical Sciences • Didactic Clinical Sciences • Some clinical rotations • Years 3 and 4 • Mostly monthly clinical rotations at distance from college • Learners are geographically dispersed

5. During these clinical rotations • Student – patient interaction = the learning situation. • Diversity across different clinical disciplines. • Onsite clinical faculty • Students collect data about their clinical encounters. • Traditionally in the form of a dairy or paper-based log.

6. Paper-based log systems • NO doubt that recording patient-student interaction is valuable to both student and school. • Variety of documentation (log) systems in place throughout U.S. • Case books • Optically scanable forms • Pocket-sized cards  Tabular data entry • Move toward computer– based systems • Hand held/Palm computers • Email communication of excel spreadsheets

7. Information traditionally collected using log system • Date • Location • Supervisor/Attending clinician • Student name • Degree of student responsibility • Patient name/number, age and gender • Diagnosis • Clinical procedures (performed, assisted or observed).

8. Sample# logbook 1 # Student name and patient details deleted

9. Sample# logbook 2

10. Limitations of paper-based systems • Handwritten patient-encounter logs often difficult to analyze. • Data processing requirements – “staggering”. Dr John Nelson: Dean of Clinics Barry University, SGMS

11. Need for change • Paper-based systems have limitations. • Student’s time on rotations is valuable. • Students need to have access to all their patient encounter data. • Faculty would benefit from access to current accurate student data. So what next?

12. A new systemALN used to track student-patient interactions • Clinical faculty collaborated with William F. Matheny and Associates. • Developed a web-based delivery system. • Incorporates drop-down menus of treatments and diagnoses. • Tested by small group of students. • Refined based on input from focus groups. • In operation for ~ 1 year.

13. Students log into the ALN during their clinical rotation

14. Student options following login

15. Students can enter their patient encounter data

16. Drop down menus allow rotation location to be entered

17. as well as a diagnosis

18. Students can access their activity log to monitor progress

19. This student* can access ALN and see that they saw 27 patients in the selected time frame Obs: Observation only GT 50% : Greater than 50% active involvement LT 50%: Less than 50% active involvement * Hypothetical student

20. Students can assess the patients they saw during a given rotation

21. This student* saw 6 patients during a third year diabetic clinic rotation * Hypothetical student

22. Contact logs can be printed out

23. Diagnostic and treatment codes and available for reference

24. Faculty view

25. Faculty view on login Lets click on student log reports to track a students patient encounters

26. Lets first select a class – say 2003

27. Then a particular student a rotation a location and a time frame

28. Data reproduced with permission

29. A total of seven contacts* at the 3rd year diabetic clinic * Personal data omitted

30. By selecting activity log usage report We can see how often each student uses the ALN system

31. Student* participation • For the Class of 2003 • 1/1/2001 – 6/1/2003 • Compliance 98.5% • Student use of the log varied from 1165 entries to 123 • Mean = 655 ± 224 *Names of students deleted from center column

32. Diagnosis & Treatment Comparison By Month Report

33. Lets see the diversity of patients seen by the class of 2003 at the 3rd year diabetic clinic

34. At the diabetic clinic in 2002 • The class of 2003 recorded 1254 patient contacts. • 105 different diagnoses from abdominal pain  walking difficulties. • Most common = 143 patients with diabetes w/neuropathy and 47 with Diabetes w/perif vasc dis. • 53 different treatment from casting  X rays.

35. At the CCPM core site • The class of 2003 recorded 7961 patient contacts. • 141 different diagnoses from abdominal pain  walking difficulties. • Most common = 767 patients with diabetes w/neuropathy. • 45 different treatments.

36. ALN student – patient log data • The frequency with which students used the log. • Number of diagnoses per student. • Mean number of treatments per student. • Mean number of a given type of treatment. • Mean number of diagnoses per dept/rotation.

37. Uses of ALN log data • Student assessment. • Evaluating the extent to which rotation objectives have been met. • Program evaluation and accreditation. • Program development.

38. Strengths of the ALN log system • The system provides a cumulative record for the student of actual clinical experiences. • Increased accuracy of data. • The data in the ALN can be viewed easily. • Facilitate report generation.

39. Preliminary observations from those using the ALN • Improvements in the following areas • Accessibility • Accuracy of data reporting • Ease of report generation • Security and privacy

40. Early student perceptions of ALN system • Helpful in documentation of patient interactions. • Easy to use. • Comprehensive. "The logs helped prepare me for completing the residency logs and keeping track of my patients during clinics." "A great idea that can be developed"

41. Possible obstacles to use of ALN system • Resistance to technology • Lack of computer knowledge/training • Lack of confidence • Access • Lack of time • Technical problems with the system "The student logs were time consuming to complete."

42. Applications of ALN tracking system to other disciplines • Any programs that involve students leaving campus to learn “in the field” • Nursing • Dietetics • Veterinary • Medicine • Anesthesiology • Cardiovascular perfusion Physician Assistant students at Barry University perform procedures under supervision (above). Students of cardiovascular perfusion observe open heart surgery (below)

43. Summary • The traditional paper-based approach to maintaining student-patient logs can suffer from limitations of accuracy, legibility and workload for students and faculty. • At this early stage it appears that the ALN system represents an improvement in all these areas and enables more effective student monitoring during clinical rotations and report generation. • Early student perceptions of the ALN are generally positive. • Though some students complained about the time needed to complete the contact data the anticipated obstacles to student use of the ALN system did not materialize. • ALN has applications in any learning environment that occurs at a distance to the central campus.

44. References and further reading • Shaw, G.P. et al., (2003). Journal of the American Podiatric Medical Association Vol 93. (2) 150 – 156. • Alderson, T St J and Oswald, N.T (1999). Medical Education. 33. 429 – 433. • Dolmans et al., (1999). Medical Education 33 (2). 89 – 94. • Vanek, E et al., (1993). Teaching & Learning in Medicine 5 (3). 164 – 168. • Asgari-Jirhandeh, N. and Heywood, J. (1997). Medical Education 31 (3). 225 – 9.