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1.  A survey of factors affecting clinician acceptance of clinical decision support 
Background
Real-time clinical decision support (CDS) integrated into clinicians' workflow has the potential to profoundly affect the cost, quality, and safety of health care delivery. Recent reports have identified a surprisingly low acceptance rate for different types of CDS. We hypothesized that factors affecting CDS system acceptance could be categorized as relating to differences in patients, physicians, CDS-type, or environmental characteristics.
Methods
We conducted a survey of all adult primary care physicians (PCPs, n = 225) within our group model Health Maintenance Organization (HMO) to identify factors that affect their acceptance of CDS. We defined clinical decision support broadly as "clinical information" that is either provided to you or accessible by you, from the clinical workstation (e.g., enhanced flow sheet displays, health maintenance reminders, alternative medication suggestions, order sets, alerts, and access to any internet-based information resources).
Results
110 surveys were returned (49%). There were no differences in the age, gender, or years of service between those who returned the survey and the entire adult PCP population. Overall, clinicians stated that the CDS provided "helps them take better care of their patients" (3.6 on scale of 1:Never – 5:Always), "is worth the time it takes" (3.5), and "reminds them of something they've forgotten" (3.2). There was no difference in the perceived acceptance rate of alerts based on their type (i.e., cost, safety, health maintenance). When asked about specific patient characteristics that would make the clinicians "more", "equally" or "less" likely to accept alerts: 41% stated that they were more (8% stated "less") likely to accept alerts on elderly patients (> 65 yrs); 38% were more (14% stated less) likely to accept alerts on patients with more than 5 current medications; and 38% were more (20% stated less) likely to accept alerts on patients with more than 5 chronic clinical conditions. Interestingly, 80% said they were less likely to accept alerts when they were behind schedule and 84% of clinicians admitted to being at least 20 minutes behind schedule "some", "most", or "all of the time".
Conclusion
Even though a majority of our clinical decision support suggestions are not explicitly followed, clinicians feel they are of benefit and would be even more beneficial if they had more time available to address them.
doi:10.1186/1472-6947-6-6
PMCID: PMC1403751  PMID: 16451720
3.  Advanced Proficiency EHR Training: Effect on Physicians’ EHR Efficiency, EHR Satisfaction and Job Satisfaction 
The best way to train clinicians to optimize their use of the Electronic Health Record (EHR) remains unclear. Approaches range from web-based training, class-room training, EHR functionality training, case-based training, role-based training, process-based training, mock-clinic training and “on the job” training. Similarly, the optimal timing of training remains unclear--whether to engage in extensive pre go-live training vs. minimal pre go-live training followed by more extensive post go-live training. In addition, the effectiveness of non-clinician trainers, clinician trainers, and peer-trainers, remains unclearly defined. This paper describes a program in which relatively experienced clinician users of an EHR underwent an intensive 3-day Peer-Led EHR advanced proficiency training, and the results of that training based on participant surveys. It highlights the effectiveness of Peer-Led Proficiency Training of existing experienced clinician EHR users in improving self-reported efficiency and satisfaction with an EHR and improvements in perceived work-life balance and job satisfaction.
PMCID: PMC3540432  PMID: 23304282
4.  Improving Population Care with an Integrated Electronic Panel Support Tool 
Abstract
This study measured the impact of an electronic Panel Support Tool (PST) on primary care teams' performance on preventive, monitoring, and therapeutic evidence-based recommendations. The PST, tightly integrated with a comprehensive electronic health record, is a dynamic report that identifies gaps in 32 evidence-based care recommendations for individual patients, groups of patients selected by a provider, or all patients on a primary care provider's panel. It combines point-of-care recommendations, disease registry capabilities, and continuous performance feedback for providers.
A serial cross-sectional study of the PST's impact on care performance was conducted, retrospectively using monthly summary data for 207 teams caring for 263,509 adult members in Kaiser Permanente's Northwest region. Baseline care performance was assessed 3 months before first PST use and at 4-month intervals over 20 months of follow-up. The main outcome measure was a monthly care performance percentage for each provider, calculated as the number of selected care recommendations that were completed for all patients divided by the number of clinical indications for care recommendations among them. Statistical analysis was performed using the t test and multiple regression. Average baseline care performance on the 13 measures was 72.9% (95% confidence interval [CI], 71.8%–74.0%). During the first 12 months of tool use, performance improved to a statistically significant degree every 4 months. After 20 months of follow-up, it increased to an average of 80.0% (95% CI, 79.3%–80.7%). (Population Health Management 2011;14:3–9)
doi:10.1089/pop.2010.0001
PMCID: PMC3128445  PMID: 20658943
5.  Ambulatory Computerized Physician Order Entry Implementation 
As part of a broader effort to identify success factors for implementing computerized physician order entry (CPOE), factors specific to the ambulatory setting were investigated in the field at Kaiser Permanente Northwest. A multidisciplinary team of five qualitative researchers spent seven months at four clinics conducting observations, interviews, and focus groups. The team analyzed the data using a combination of template and grounded theory approaches. The result is a description of fourteen themes, clustered into technology, organizational, personal, and environmental categories. While similar to inpatient study results in many respects, this outpatient CPO investigation generated subtly different themes.
PMCID: PMC1560502  PMID: 16778992
6.  Case-Based Tutoring from a Medical Knowledge Base 
The past decade has seen the emergence of programs that make use of large knowledge bases to assist physicians in diagnosis within the general field of internal medicine. One such program, Internist-I, contains knowledge about over 600 diseases, covering a significant proportion of internal medicine. This paper describes the process of converting a subset of this knowledge base--in the area of cardiovascular diseases--into a probabilistic format, and the use of this resulting knowledge base to teach medical diagnostic knowledge. The system (called KBSimulator--for Knowledge-Based patient Simulator) generates simulated patient cases and uses these cases as a focal point from which to teach medical knowledge. It interacts with the student in a mixed-initiative fashion, presenting patients for the student to diagnose, and allowing the student to obtain further information on his/her own initiative in the context of that patient case. The system scores the student, and uses these scores to form a rudimentary model of the student. This resulting model of the student is then used to direct the generation of subsequent patient cases. This project demonstrates the feasibility of building an intelligent, flexible instructional system that uses a knowledge base constructed primarily for medical diagnosis.
PMCID: PMC2245258
intelligent computer-aided instruction; medical computer-aided instruction; medical knowledge base; patient simulation
7.  The Role of Causal Knowledge in Knowledge-Based Patient Simulation 
We have investigated the ability to simulate a patient from a knowledge base. Specifically, we have examined the use of knowledge bases that associate findings with diseases through the use of probability measures, and their ability to generate realistic patient cases that can be used for teaching purposes. Many of these knowledge bases encode neither the interdependence among findings, nor intermediate disease states. Because of this, the use of these knowledge bases results in the generation of inconsistent or nonsensical patients. This paper describes an approach for the addition of causal structure to these knowledge bases which can overcome many of these limitations and improve the explanatory capability of such systems.
PMCID: PMC2245078

Results 1-7 (7)