SCOOP - The Social Collaboratory for Outcome Oriented Primary Care

Many primary care clinics have transitioned from paper-based record keeping to computer-based Electronic Medical Record (EMR) systems. This transition provides opportunities for computer-based data analytics in support of practice improvement and more evidence-based clinical research. Unfortunately, the data in primary care EMRs is often not readily accessible to researchers, who often have to overcome significant political, organizational and technical hurdles before gaining access to such data. As a consequence, knowledge discovery and translation has been slow and burdensome in this area. Primary care research networks (PCRN) have been proposed as a way to addressing these limitations. This paper reports on the development of a PCRN in British Columbia, referred to as SCOOP (The Social Collaboratory for Outcome Oriented Primary Care). We describe its technical architecture and draw comparisons to related and previous initiatives.

[1]  David D. Clark,et al.  A Comparison of Commercial and Military Computer Security Policies , 1987, 1987 IEEE Symposium on Security and Privacy.

[2]  L. Green,et al.  Potential of practice-based research networks: experiences from ASPN. Ambulatory Sentinel Practice Network. , 1994, The Journal of family practice.

[3]  P. Nutting,et al.  Practice-based research networks answer primary care questions. , 1999, JAMA.

[4]  P. V. Biron,et al.  The HL7 Clinical Document Architecture. , 2001, Journal of the American Medical Informatics Association : JAMIA.

[5]  David K. Hsiao Federated databases and systems: Part I—A tutorial on their data sharing , 2005, The VLDB Journal.

[6]  Kevin A Peterson,et al.  Primary Care Practice-Based Research Networks: Working at the Interface Between Research and Quality Improvement , 2005, The Annals of Family Medicine.

[7]  Amnon Shabo,et al.  Model Formulation: HL7 Clinical Document Architecture, Release 2 , 2006, J. Am. Medical Informatics Assoc..

[8]  Nich Wattanasin,et al.  Integration of Hive and cell software in the i2b2 architecture. , 2007, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[9]  Karim Keshavjee,et al.  Building a Pan-Canadian Primary Care Sentinel Surveillance Network: Initial Development and Moving Forward , 2009, The Journal of the American Board of Family Medicine.

[10]  Clay Shirky,et al.  Collecting and sharing data for population health: a new paradigm. , 2009, Health affairs.

[11]  Sanjay Ghemawat,et al.  MapReduce: a flexible data processing tool , 2010, CACM.

[12]  R. Platt,et al.  Distributed Health Data Networks: A Practical and Preferred Approach to Multi-Institutional Evaluations of Comparative Effectiveness, Safety, and Quality of Care , 2010, Medical care.

[13]  R Platt,et al.  Comparative‐Effectiveness Research in Distributed Health Data Networks , 2011, Clinical pharmacology and therapeutics.

[14]  Paul Gallagher,et al.  Potentially inappropriate medications defined by STOPP criteria and the risk of adverse drug events in older hospitalized patients. , 2011, Archives of internal medicine.

[15]  Jennifer Prestigiacomo Asking population health's unanswered questions. , 2012, Healthcare informatics : the business magazine for information and communication systems.

[16]  Marsha A Raebel,et al.  Design considerations, architecture, and use of the Mini‐Sentinel distributed data system , 2012, Pharmacoepidemiology and drug safety.

[17]  Anders Andersen An implementation of secure multi-party computations to preserve privacy when processing EMR data , 2013, 2013 Eleventh Annual Conference on Privacy, Security and Trust.

[18]  M. Kahn,et al.  Data Quality Assessment for Comparative Effectiveness Research in Distributed Data Networks , 2013, Medical care.

[19]  Wilson D. Pace,et al.  Electronic health record functionality needed to better support primary care , 2014, J. Am. Medical Informatics Assoc..

[20]  P. Cinciripini,et al.  “Meaningful use” provides a meaningful opportunity , 2014, Cancer.