Abstract Background The complex effect of multiple influencing factors in the idiosyncrasies of how people frame a purpose, and the decisions and actions they carry out to reach a goal have a strong impact on repeated-occurrence behaviors, such as smoking cessation, where the perceived benefit of behavioral change may wane in response to difficulties or setbacks. Purpose We applied a modeling cycle methodology to implement a knowledge-based ABM to assess the impact of individual health preferences, and societal factors on modifiable behaviors to identify time-related opportunities for non-pharmaceutical interventions to sustain the desired behavioral changes. Methods We gathered and encoded information about patient beliefs, preferences, and societal factors as simple rules to roughly represent patient behaviors. Through ABM simulations we looked at idiosyncratic patterns stemming from the complex effect of multiple influencing factors. Results Marked smoking/non-smoking fluctuations of women vs. slower, steadier decline in smoking men highlight the complex effect of multiple influencing factors in the idiosyncrasies of how people frame a purpose, and the decisions and actions they carry out to reach a goal. Unintentional patterns of segregation underline the impact surrounding neighbors’ have on an agent’s behavior, blurring the line between individual motivation and collective influence, leading agents to align with the surrounding majority. Conclusions ABMs provide insights on the impact multi-factorial, dynamic individual behaviors, and societal factors have on repeated-occurrence modifiable behaviors, e.g., smoking cessation, and pinpoint opportune educational and motivational adjuvant interventions to counteract negative, more ingrained behaviors, and external factors to improve compliance and success.

Objective We sought to ascertain perceived factors affecting women’s career development efforts in the American Medical Informatics Association (AMIA) and to provide recommendations for improvements. Materials and Methods Data were collected using a 27-item survey administered via the AMIA newsletter and other social channels. Survey questions comprised 3 demographics, 15 Likert-scale, and 9 open-ended items. Likert-scale responses were summarized across respondent ages, career stages, and career domains, and open-ended responses were thematically analyzed. Results We received survey responses from 109 AMIA women members. Our findings demonstrate that AMIA had made strides in promoting career development, and the most effective AMIA efforts included social events (83%), panel discussions (80%), and scientific sessions (79%). However, despite these efforts, women members perceived that gender-specific challenges persisted within AMIA, and recognized the need for increased networking opportunities (96%), raising awareness of gender-specific challenges (95%), and encouraging gender proportional representation in leadership (92%). Discussion International and national biomedical informatics professional communities have put forth efforts to address gender-specific issues in career development. Yet, our study identified that some of these, including the deep-rooted gender power hierarchy and bias, are still perceived as profound in AMIA. Conclusion Even though existing career development efforts for women are highly effective, important perceived gender-specific career development issues require further attention and investigation to improve existing AMIA activities.

Medical expert systems (ESs) aim to apply computer technology to emulate human decision-making and provide computerized clinical decision support to clinicians, patients, and other individuals with suitable information and knowledge at appropriate times to improve the quality and safety of health care. This chapter first presents the significance and a brief history of this field. It then describes a common architecture and major components of medical ESs and introduces different knowledge representation and reasoning techniques. This chapter will also show some examples of medical ESs, including computer-assisted diagnosis systems, medication alert systems, reminder systems, and so on. Lastly, it discusses the advantages and disadvantages of existing approaches as well as major issues and challenges related to system implementation, evaluation, maintenance, and distribution and points out some of the directions for future research and development.

Phillips, Andrew B. PhD, RN, FAMIA; Sordo, Margarita PhD, MSc, FAMIA; Wood, Lisa J. PhD, RN, FAANEditor(s): Alexander, Susan DNP, ANP-BC, ADM-BC Author Information

OBJECTIVE:To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30-day readmissions.STUDY SETTING:A multihospital academic health system in southeastern Massachusetts.STUDY DESIGN:An observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics.DATA COLLECTION/EXTRACTION METHODS:All-payer claims, EHR data, and physician notes extracted from a centralized clinical registry.PRINCIPAL FINDINGS:All seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD-9 codes and structured EHR data to 16.0 percent. Compared to an 18.6 percent baseline readmission rate, risk-adjusted analysis showed higher readmission risk for patients with housing instability (readmission rate 24.5 percent; p < .001), depression (20.6 percent; p < .001), drug abuse (20.2 percent; p = .01), and poor social support (20.0 percent; p = .01).CONCLUSIONS:The seven social risk factors studied are substantially more prevalent than represented in administrative data. Automated methods for analyzing physician notes may enable better identification of patients with social needs.

OBJECTIVES:The Common Formats, published by the Agency for Healthcare Research and Quality, represent a standard for safety event reporting used by Patient Safety Organizations (PSOs). We evaluated its ability to capture patient-reported safety events. MATERIALS AND METHODS:We formally evaluated gaps between the Common Formats and a safety concern reporting system for use by patients and their carepartners (ie friends/families) at Brigham and Women's Hospital. RESULTS:Overall, we found large gaps between Common Formats (versions 1.2, 2.0) and our patient/carepartner reporting system, with only 22-30% of the data elements matching. DISCUSSION:We recommend extensions to the Common Formats, including concepts that capture greater detail about the submitter and safety categories relevant to unsafe conditions and near misses that patients and carepartners routinely observe. CONCLUSION:Extensions to the Common Formats could enable more complete safety data sets and greater understanding of safety from key stakeholder perspectives, especially patients, and carepartners.

In theory, the logic of decision rules should be atomic. In practice, this is not always possible; initially simple logic statements tend to be overloaded with additional conditions restricting the scope of such rules. By doing so, the original logic soon becomes encumbered with contextual knowledge. Contextual knowledge is re-usable on its own and could be modeled separately from the logic of a rule without losing the intended functionality. We model constraints to explicitly define the context where knowledge of decision rules is actionable. We borrowed concepts from Semantic Web, Complex Adaptive Systems, and Contextual Reasoning. The proposed approach provides the means for identifying and modeling contextual knowledge in a simple, sound manner. The methodology presented herein facilitates rule authoring, fosters consistency in rules implementation and maintenance; facilitates developing authoritative knowledge repositories to promote quality, safety and efficacy of healthcare; and paves the road for future work in knowledge discovery.

About 1 in 10 adults are reported to exhibit clinical depression and the associated personal, societal, and economic costs are significant. In this study, we applied the MTERMS NLP system and machine learning classification algorithms to identify patients with depression using discharge summaries. Domain experts reviewed both the training and test cases, and classified these cases as depression with a high, intermediate, and low confidence. For depression cases with high confidence, all of the algorithms we tested performed similarly, with MTERMS' knowledge-based decision tree slightly better than the machine learning classifiers, achieving an F-measure of 89.6%. MTERMS also achieved the highest F-measure (70.6%) on intermediate confidence cases. The RIPPER rule learner was the best performing machine learning method, with an F-measure of 70.0%, and a higher precision but lower recall than MTERMS. The proposed NLP-based approach was able to identify a significant portion of the depression cases (about 20%) that were not on the coded diagnosis list.

Two frequently occurring tasks in the clinical workflow where the health care provider and the computer communicate are during clinical and in computerized-provider order entry (CPOE). These tasks are facilitated by grouped knowledge elements, i.e. structured documentation templates and order sets, respectively. A structured documentation template is an organized collection of data items relevant to a particular clinical context that can be used to collect or present codified information about a patient. An order set, similarly, is an organized collection of actions that can be ordered by a health care provider for the care of a patient in a specific clinical context. These grouped knowledge elements may be considered as vehicles for providing clinical decision support. Those who design the groupings of knowledge elements can use them to drive the behavior of the health care professional user. Additionally, order items and documentation items can be dynamically presented based on the context. By anticipating needs for data entry or access, or for orders, such grouping not only provides CDS but also facilitates workflow, by eliminating extra steps that would otherwise be needed. The specification of an order set’s or a document template’s structure and content is a form of knowledge. Standards are being developed for such specification to encourage the collection of higher quality, more interpretable, more comprehensive data, and to encourage reuse of document specifications, or parts thereof, where appropriate. This chapter reviews those efforts, in terms of their degree of maturity and harmonization, and how they relate to clinical decision support.