MedSocket: a personalized medical meta-search engine for questions at the point of care

In the U.S., healthcare is a $2.4 trillion industry, and health information technology costs exceed$65 billion per year. Healthcare expenditures are currently at 17% of the U.S. Gross Domestic Product and are expected to rise to 20% by 2017. Improving the information available to physicians and streamlining the delivery of quality care can help stem rising healthcare costs. Through enormous investment in research, the needed information is often available but located in a variety of sources that are difficult and time consuming to use. Due to time constraints, physicians are rarely able to conduct online searches to sufficiently answer patient-specific, clinical or administrative questions at the point of care. Existing search engines don't fit the particular physician needs and preferences giving either too little or too much information. MedSocket is an innovative and patent-pending search engine that implements an information retrieval system from a physician's point-of-view. Also accessible from mobile devices, it will provide convenient and useful access to medical information sources to answer all types of questions. MedSocket offers a single user interface and is highly customized to search medical information, enabling a simultaneous search of the best online resources; a user's personal digitized knowledge stored in notes, emails or documents; and a hospital or departmental intranet. To achieve an optimal search experience, it integrates into electronic health record systems and offers many levels of personalization. MedSocket will utilize the user's medical context, query different content sources (set by user), and deliver only results most relevant to the user. Allowing easy and instant access to current research results, MedSocket will speed the information delivery to the patient's bedside, which currently could take as long as 17 years. MedSocket has the capability to greatly improve care delivered by physicians and make a significant impact on the U.S. healthcare system. Potential Areas of Applications: * Point of Care * Medical Research Patent Status: Pending Inventor(s): Karl Kochendorfer, MD, FAAFP [email protected] - MedSocket LLC Contact Info: Paul Hippenmeyer, Ph.D., M.B.A. [email protected] (573)-882-047

Decision support system in a patient-centered medical home

Lack of sufficient primary care to manage chronic diseases has been quoted as a major drawback of the healthcare system within the United States. Patient-Centered Medical Home is a care delivery model to transform how primary care is delivered. The information technology revolution has brought about several advancements and solutions for medicine and care delivery, and medical homes are no exception to this. However, it is only through a robust decision support system that these medical homes can in fact provide truly coordinated and patient-centered care. The paper describes preliminary work that has been completed at the University of Missouri Health System and next steps in achieving high quality care delivery through a decision support system implementation. Originally presented at the IEEE HealthCon Medial Home conference in June 2011

Lessons Learned: Development of COVID-19 Clinical Staging Models at a Large Urban Research Institution

BACKGROUND/OBJECTIVE: The University of Illinois at Chicago (UIC), along with many academic institutions worldwide, made significant efforts to address the many challenges presented during the COVID-19 pandemic by developing clinical staging and predictive models. Data from patients with a clinical encounter at UIC from July 1, 2019 to March 30, 2022 were abstracted from the electronic health record and stored in the UIC Center for Clinical and Translational Science Clinical Research Data Warehouse, prior to data analysis. While we saw some success, there were many failures along the way. For this paper, we wanted to discuss some of these obstacles and many of the lessons learned from the journey. METHODS: Principle investigators, research staff, and other project team members were invited to complete an anonymous Qualtrics survey to reflect on the project. The survey included open-ended questions centering on participants' opinions about the project, including whether project goals were met, project successes, project failures, and areas that could have been improved. We then identified themes among the results. RESULTS: Nine project team members (out of 30 members contacted) completed the survey. The responders were anonymous. The survey responses were grouped into four key themes: Collaboration, Infrastructure, Data Acquisition/Validation, and Model Building. CONCLUSION: Through our COVID-19 research efforts, the team learned about our strengths and deficiencies. We continue to work to improve our research and data translation capabilities

MedSocket: connecting the world of medicine

No information for this entry was available at http://tmir.missouri.edu/mte2011/Technology.htmlBetween researching illnesses and consulting with patients, Doctors and healthcare professionals find themselves stretched for time. MedSocket is an online medical information discovery tool already in use at the University of Missouri-Columbia healthcare facilities. MedSocket drastically reduces time spent by healthcare professionals in researching medical information. Patent status: patent pending. This presentation was an elevator pitch at the Missouri Technology Expo 2011

Using semantic search to reduce cognitive load in an electronic health record

As electronic health records (EHRs) become more prevalent in health care further research is needed to understand the efficacy within clinical contexts from a human-computer interaction viewpoint. Participants (N=10) were given two authentic scenarios that required users to search for patient information. In the first scenario, participants responded to a patient-specific information need as they normally would. In the second scenario, participants were given a semantic search tool that indexed terms within a patient EHR. Upon completion, participants were then asked questions in a semi-structured interview about current usage of the EHR. Statistically significant results revealed that participants were able to more efficiently navigate through an EHR in terms of time (semantic search M=140 vs. browsing M=239 seconds) and number of clicks (semantic search M=11 vs. browsing M=35). This study suggests that semantic search capabilities may be a good way to reduce cognitive load within clinical settings for similar patient-specific information needs. © 2011 IEEE

I don\u27t have time to dig back through this : The role of semantic search in supporting physician information seeking in an electronic health record

The purpose of the study was twofold: to understand how usability affected physicians\u27 performance as they used an electronic health record (EHR) and to ascertain whether use of a semantic search feature would better support physician performance during an information-seeking task. Participants (n = 10) were asked to complete two search tasks to find pertinent patient information. In the first task, participants located the information as they normally would (through browsing the EHR). In the second task, participants employed a semantic search tool. Upon task completion, participants were interviewed to further understand their perceptions and information-seeking behavior in an EHR. Statistically significant results confirmed that participants were able to more efficiently navigate through an EHR in terms of time and number of clicks when using the semantic search feature. Moreover, participants were more confident in the accuracy of their answers when using semantic search compared with the browsing method. Implications for practice are discussed. © 2014 International Society for Performance Improvement

Biomedical elevator pitches

The slides for the panelists' presentations can be found by going to Presentations collection for the Missouri Technology Expo 2010: https://mospace.umsystem.edu/xmlui/handle/10355/9623This video presents the elevator pitches given in the field of biomedicine. Each elevator pitch consists of a presentation from the faculty/student innovator, followed by questions/answers from the audience

Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients

AIMS: There are no comprehensive machine learning (ML) tools used by oncologists to assist with risk identification and referrals to cardio-oncology. This study applies ML algorithms to identify oncology patients at risk for cardiovascular disease for referrals to cardio-oncology and to generate risk scores to support quality of care. METHODS AND RESULTS: De-identified patient data were obtained from Vanderbilt University Medical Center. Patients with breast, kidney, and B-cell lymphoma cancers were targeted. Additionally, the study included patients who received immunotherapy drugs for treatment of melanoma, lung cancer, or kidney cancer. Random forest (RF) and artificial neural network (ANN) ML models were applied to analyse each cohort: A total of 20 023 records were analysed (breast cancer, 6299; B-cell lymphoma, 9227; kidney cancer, 2047; and immunotherapy for three covered cancers, 2450). Data were divided randomly into training (80%) and test (20%) data sets. Random forest and ANN performed over 90% for accuracy and area under the curve (AUC). All ANN models performed better than RF models and produced accurate referrals. CONCLUSION: Predictive models are ready for translation into oncology practice to identify and care for patients who are at risk of cardiovascular disease. The models are being integrated with electronic health record application as a report of patients who should be referred to cardio-oncology for monitoring and/or tailored treatments. Models operationally support cardio-oncology practice. Limited validation identified 86% of the lymphoma and 58% of the kidney cancer patients with major risk for cardiotoxicity who were not referred to cardio-oncology

A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients

BACKGROUND: Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep learning approach to predict mortality among COVID-19 patients and updated the prediction in 6-h intervals during the first 72 h after hospital admission. METHODS: The process mining/deep learning model produced temporal information related to the variables and incorporated demographic and clinical data to predict mortality. The mortality prediction was updated in 6-h intervals during the first 72 h after hospital admission. Moreover, the performance of the model was compared with published and self-developed traditional machine learning models that did not use time as a variable. The performance was compared using the Area Under the Receiver Operator Curve (AUROC), accuracy, sensitivity, and specificity. RESULTS: The proposed process mining/deep learning model outperformed the comparison models in almost all time intervals with a robust AUROC above 80% on a dataset that was imbalanced. CONCLUSIONS: Our proposed process mining/deep learning model performed significantly better than commonly used machine learning approaches that ignore time information. Thus, time information should be incorporated in models to predict outcomes more accurately