Private practice outcomes: validated outcomes data collection in private practice.
BACKGROUND
Improved patient care is related to validated outcome measures requiring the collection of three distinct data types: (1) demographics; (2) patient outcome measures; and (3) physician treatment. Previous impediments to widespread data collection have been: cost, office disruption, personnel requirements, physician motivation, data integration, and security. There are currently few means to collect data to be used for collaborative analysis.


OBJECTIVE
We therefore developed an inexpensive, patient-centric mechanism to reduce redundant data entry, limiting cost and personnel requirements.


METHODS
Using an intuitive touch-screen kiosk interface program, all data elements have been captured in a private practice setting since 2000. Developed for small to medium sized offices, this is scalable to larger organizations. Questionnaire navigation is patient driven, with demographics shared with EMR and billing systems. Integration of billing and EMR with outcomes minimizes cost and personnel time. Data are deidentified locally and may be centrally shared. Since data are entered by the patients, minimal personnel costs are incurred. Physician disincentives are minimized with cost reduction, time savings and ease of use.


RESULTS
To date, we have collected high level data on most total joint patients, with excellent patient compliance.


CONCLUSIONS
By addressing impediments to broad application, we may enable widespread local data collection in all practice settings. Data may be shared centrally, allowing comparative effectiveness research to become a reality. Future success will require broad physician participation, uniformity of data collected, and designation of a central site for receipt of data and its collaborative comparative analysis.
Department of Orthopaedic Surgery, Miriam Hospital, Sports Medicine, Orthopaedics and Podiatry, Brown University, 400 Massasoit Avenue, Suite 200, East Providence, RI 02914, USA. jdgoldstein@automationmed.com
Jack
J
Goldstein
D016494:Computer Security; D003199:Computer Systems; D003625:Data Collection; D057225:Data Mining; D019992:Databases as Topic / Q000706:statistics & numerical data; D006801:Humans; D020407:Internet; D019637:Orthopedic Procedures / Q000706:statistics & numerical data; D010043:Outcome and Process Assessment, Health Care / Q000706:statistics & numerical data; D011331:Private Practice / Q000706:statistics & numerical data; D016730:Program Development; D015397:Program Evaluation; D015203:Reproducibility of Results; D016896:Treatment Outcome; D014584:User-Computer Interface