Clinical decision support in small community practice settings: a case study

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Using an eight-dimensional model for studying socio-technical systems, a multidisciplinary team of investigators identified barriers and facilitators to clinical decision support (CDS) implementation in a community setting, the Mid-Valley Independent Physicians Association in the Salem, Oregon area. The team used the Rapid Assessment Process, which included nine formal interviews with CDS stakeholders, and observation of 27 clinicians. The research team, which has studied 21 healthcare sites of various sizes over the past 12 years, believes this site is an excellent example of an organization which is using a commercially available electronic-health-record system with CDS well. The eight-dimensional model proved useful as an organizing structure for the evaluation.



MVIPA (Mid-Valley Independent Physician Association) is a representative of majority of ambulatory clinics in the country consisting of small and independent clinics mainly in rural settings. MVIPA implemented their community EHR in 2005 in partnership with NextGen Healthcare Information Systems. Nearly 60% of the providers within MVIPA were using EHRs by 2008. Since little is known about CDS implementation in ambulatory settings, the main goal of this study was to identify barriers and facilitators for CDS implementation and knowledge management at this site. [1]


Interview questions were developed by using the information from the Site Profile completed by the Medical Director of Information Systems, which is a checklist of types of CDS and list of questions about CDS management. An eight-dimensional model was used for studying socio-technical systems by a multidisciplinary team of investigators. The team used Rapid Assessment Process, which included nine formal interviews with CDS stakeholders, the MDIS, his staff members, and physicians, and observed 27 clinicians in 9 clinics.


For each of the below 8 dimensions, barriers (B) and facilitators (F) to CDS implementation was identified.

  • F- MVIPA uses an application service provider model and each clinic connects remotely to its own instance of NextGen. CDS content is standardized across MVIPA for primary care.
  • B- The largest hospital in the region uses a different EHR, which does not currently include an interface with MVIPA’s system.
Clinical Content
  • F-Clinical Content is reviewed/modified by local staff and standardized across primary care clinics.
  • B- Some clinicians still do not use templates in the exam room and prefer free text, so coded data are not captured.
Human-Computer interaction
  • F- Clinicians have choices about how they interact with the system touchscreen with templates or free-text.
  • B- Clinicians are often unaware that they can modify the severity level of alerts.
  • F- Each clinic has an identified clinical champion and a super user. Clinic managers are knowledgeable about the system and aware of changes.
  • B- Many clinicians are unaware of advanced CDS capabilities, such as protocols, reminders, and charting templates.
Workflow & Communication
  • F- Interruptive alerts are minimal; charting templates provide guidance without interference. Feedback is routed to the appropriate person for analysis and action.
  • B- There was a ‘valley of despair’ for 3 to 6 months after implementation in each clinic when workflow was disrupted whose intensity varied according to the extent of the workflow analysis.
Internal Organizational Features
  • F- The IPA board of 16 physicians is closely involved because most are users as well as decision-makers.
  • B- Clinicians value their independence and are hesitant to share patient-specific, clinical information beyond individual clinics.
External Rules & Regulations
  • F- The environment was scanned so that new clinical knowledge was integrated into the system to help meet outside quality reporting requirements.
  • B- Users cannot see patient data from other practices owing to privacy and data ownership concerns. This limited the ability of CDS interventions to fully reason over a patient’s entire clinical state.
Measurement and Metrics
  • F- Reports for individual clinics were produced and planning for community-wide quality care began.
  • B- Common metrics was required so that the effectiveness of the system’s CDS could be measured over time.


Three main reasons for their success was identified. First, many economics of scale was achieved by together agreeing to select, purchase and implement an EHR with CDS. Second, a centrally managed EHR provides solid clinical and financial workflow solution for all members of the IPA. Finally, EHR provides a solid foundation for the collection, storage, and transmission of data, which is essential for CDS. The eight-dimensional model proved useful as an organizing structure for the evaluation.


This study gives a good example of a successful implementation of an EHR with CDS in a small community healthcare setting. One way to improve CDS and clinical decision making in MVIPA is by making patient data available across clinics so that clinicians have a lot more patient information for decision support. Similar small ambulatory settings can also use this strategy to successfully implement an EHR with CDS.

Related Articles

Identifying Best Practices for Clinical Decision Support and Knowledge Management in the Field


  1. Ash, J. S., Sittig, D. F., Wright, A., McMullen, C., Shapiro, M., Bunce, A., & Middleton, B. (2011). Clinical decision support in small community practice settings: a case study. Journal of the American Medical Informatics Association: JAMIA, 18(6), 879–882.