Browsing by Subject "Health Informatics"

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Athletic Training Students' Academic Preparation in Healthcare Documentation
(2015-05) Brugge, Amy
Documentation is fundamental to all patient encounters across health professions, including athletic trainers. The athletic training education competencies delineate five competencies and one clinical integration proficiency specific to documentation knowledge, skills, and abilities. There is little research regarding athletic training students� preparation in performing patient documentation and suggestion that recent graduates and employers have identified the domain of healthcare administration as a perceived deficit in professional preparation. A descriptive study was undertaken to ascertain students� reports of their preparation in healthcare documentation in didactic, laboratory, and clinical education. The purpose of this study was to examine the ways in which final-year athletic training students report having received instruction, having rehearsed, and having been assessed on the documentation-related competencies in athletic training. An electronic survey was sent to final-year athletic training students across the United States currently enrolled in professional programs accredited by the Commission on Accreditation of Athletic Training Education (CAATE). A 16.9% response rate was obtained via 185 survey participants. These participants were from all ten districts of the National Athletic Trainers’ Association. Findings suggest that final-year students report appropriate levels of instruction, rehearsal, and assessment of their knowledge and skills in medical terminology and the security, privacy, and confidentiality of medical records, but that foundational knowledge in the use of procedural and diagnostic coding and performance of third party reimbursement activities may be lacking. Only 7% of final-year students reported having used their documentation to communicate with insurers and bill for services. Additionally, students enrolled in professional programs at the post-baccalaureate degree level reported the inclusion of academic electronic health records in didactic coursework at statistically significant greater level than their baccalaureate degree peers. Athletic training educators should consider the timing and placement of documentation-related competencies in program curricula in order to allow for adequate instruction, rehearsal reinforced through clinical education experiences, and appropriate assessment of documentation knowledge, skills, and abilities prior to graduation. The future of the athletic training profession is dependent upon a workforce that excels in documentation in order to support outcomes-based clinical research and successfully obtain payment for services rendered.
Bone marrow diagnostic discordance determination: a foundation for clinical decision support.
(2010-05) Pitkus, Andrea Renee’
Bone marrow testing by the hematopathology, flow cytometry and cytogenetics laboratories provides valuable information utilized in the diagnosis, prognosis and treatment of leukemias. Not much is known about unexpected informatics issues which arise during the analysis of bone marrow, which impact information about the patient's hematological status. This status needs to be clearly communicated to the clinician since it impacts clinical decision making and patient care. This research addresses whether bone marrow diagnostic discordance can be utilized as an indicator of issues in the bone marrow information process, providing the foundation for clinical decision support tool development. The study first measures disagreement in the diagnoses reported by the three laboratories, on bone marrow specimens collected at the same time, to determine lexical diagnostic discordance. Semantic diagnostic discordance is determined utilizing the 2001 World Health Organization leukemia classifications. Statistical significance of diagnostic discordance is measured with Cohen's Kappa statistic. The second research phase categorizes factors contributing to the discordances found in the first phase to further understand the etiology of the discordances. It is important to distinguish discordances due to expected testing process limitations from unexpected discordances due to other etiologies. It is also vital to denote which are clinically significant and likely to impact patient care. These factors are critical in designing an effective decision support tool which alerts the clinician appropriately. Results of the first research phase show lexical and semantic discordance can be measured successfully from three laboratories reporting on bone marrows. Cohen's Kappa statistic also provides an automatic means of detection and measurement of semantic discordance. Categorization of discordances distinguishes which discordances are due to limitations in laboratory testing. Categorization also indicates where in the testing process interventions such as a decision support tool are optimally placed in alerting pathologists of problems in the information process needing further assessment.
CURRENT STATE AND BARRIERS THAT EXIST IN INFORMATICS USAGE AND ADOPTION WITHIN LONG TERM CARE FACILITIES
(2016-05-03) Felder, Gary
The HITECH ACT of 2009 created reimbursement incentives that are using technology (such as EHRs) in meaningful ways. LTC facilities were excluded from the incentives and hence have been mostly force to bear the cost on their own for adopting newer technologies. Many facilities at the time sighted the high cost and limited revenue stream to support such endeavors as implementing information technology solutions. Now that many hospitals and providers have join the EHR bandwagon as well as being a participant in data interchange, Long Term Care facilities have lagged behind and now cause a major data gap in management of health population data. This study aims to review underlying issues that are causing Health Information Technology (HIT) adoption barriers within the LTC setting. Additionally we will study those facilities where adoption has taken place and observe how strives have been made to improve patient outcomes and safety.
Developing a Predictive Model for Hospital-Acquired Catheter-Associated Urinary Tract Infections Using Electronic Health Records and Nurse Staffing Data
(2016-08) Park, Jung In
There are a number of clinical guidelines and studies about hospital-acquired catheter-associated urinary tract infections (CAUTIs), but the rate of CAUTI occurrence is still rising. Hospitals are focusing on preventing hospital-acquired CAUTI, as the Centers for Medicare and Medicaid Services (CMS) does not provide payment for hospital-acquired infections anymore. There is a need to explore additional factors associated with hospital-acquired CAUTI and develop a predictive model to detect patients at high risk. This study developed a predictive model for hospital-acquired CAUTIs using electronic health records (EHRs) and nurse staffing data from multiple data sources. Research using large amounts of data could provide additional knowledge about hospital-acquired CAUTI. The first aim of the study was to create a quality, de-identified dataset combining multiple data sources for machine learning tasks. To address the first aim of the study, three datasets were combined into a single dataset. After integrating the datasets, data were cleaned and prepared for analysis. The second aim of the study was to develop and evaluate predictive models to find the best predictive model for hospital-acquired CAUTI. For the second aim of the study, three predictive models were created using the following data mining method: decision trees (DT), logistic regression (LR), and support vector machine (SVM). The models were evaluated and DT model was determined as the best predictive model for hospital-acquired CAUTI. The findings from this study have presented factors associated with hospital-acquired CAUTI. The study results demonstrated that female gender, old adult (≥56), Charlson comorbidity index score ≥ 3, longer length of stay, glucose lab result > 200 mg/dl, present of rationale for continued use of catheter, higher percent of direct care RNs with associate’s degree in nursing, less total nursing hours per patient day, and lower percent of direct care RNs with specialty nursing certification was related to CAUTI occurrence. Implications for future research include the use of different analytic software to investigate detailed results for LR model, adding more factors associated with CAUTI in modeling, using a larger sample with more patients with CAUTI, and patient outcomes research using nursing-sensitive indicators. This study has important implications for nursing practice. According to the study results, nurse specialty certification, nurse’s education at the baccalaureate level or higher, and more nursing hours per patient day were associated with better patient outcomes. Therefore, considerable efforts are needed to promote possession of nurse specialty certification and higher level of nursing education, as well as enough supply of nursing workforce.
Development of a Neuroinformatics Pipeline and its Application to Gene-environment Interaction in Neurodegenerative Disease
(2020-12) Overgaard, Shauna
BackgroundAlzheimer’s disease is a neurodegenerative disease and the sixth-leading cause of death in the United States. While there has been a greater understanding of Alzheimer’s disease (AD) processes in the last two decades, clinical trials in AD have not been successful, suggesting that further research is needed to understand key questions pertaining to the underpinnings of the disease. Alzheimer’s disease is a complex disease with significant heterogeneity in the disease progression and expression of clinical symptoms. The presence of the ϵ4 allele of the Apolipoprotein (APOE4) increases the risk of Alzheimer’s disease and is associated with earlier onset of Alzheimer’s disease pathology. On the other hand, variability in “Resilience,” i.e., the ability to cope with Alzheimer’s disease pathology, is associated with the differences in the expression of clinical symptoms. Objective The work presented draws on the methodological strengths of health informatics, biostatistics, and neuroscience, to achieve two specific aims: (1) Deployment of a neuroinformatics pipeline for the replicable collection, manipulation, and analysis of AD data (2) Employment of the neuroinformatics pipeline to evaluate the potential impact of specific allele carriership on what is recognized as a resilience mechanisms in the context of AD. Specifically, the neuroscience questions are: (1) Does the effect of cognitive reserve on GMD differ by APOE4 genotype? (2) Does APOE4 carrier status impact clinical functioning, and is this effect mediated by global efficiency? (3) Do APOE4 carriers as compared to non-carriers demonstrate differences in network recruitment (specifically, global efficiency of the default mode network)? Methods To evaluate the complex interplay of gene-environment interactions in AD, we investigated the impact of APOE4 and education on brain structure in the first study. In our second study, we used a core construct from graph theory to compute global efficiency on single-subject 3T MRI scans and evaluated the interplay between pathology, APOE4, education, and global efficiency and their impact on clinical functioning. In our third study, we applied causal inference models to investigate the causal relationships between pathology, APOE4, education, and global efficiency, considered drivers of clinical functioning in AD. Conclusion This work uniquely contributes to health informatics through the construction of a neuroinformatics pipeline that combines multimodal biomedical data (neuroimaging, genomics, cognition, and clinical), employs database management, automated computing, graph theory, and biostatistics to answer clinical questions. This work contributes to science by proposing a method to measure and monitor brain health, providing additional insight into the mechanistic underpinnings of APOE4 allele carriership underlying AD pathology.
Development of a personalized web-based data management system for physiological and metabolomic data integration in systems biology research.
(2010-11) Tokachichu, Priyaranjan
The Surgical Critical Care laboratory at the University of Minnesota conducts pre-clinical research that involves integration of physiological and metabolomic data. Managing the data using flat file data management systems has become increasingly difficult as the research progressed. Diversity of research groups and differences in data generation timeline makes data sharing between research groups difficult. This project explains the development of a data management system that can fulfill the data needs of the Surgical Critical Care laboratory at the University of Minnesota, and other research with similar data needs. Each systems biology research project has unique data needs and requires a personalized data management system. A web-based data management system would be independent of any computer operating system and makes data access and sharing easier. A relational database management system manages enormous amounts of data more effectively than a flat file data management system. These are some of the factors that influenced the development of a personalized web-based data management system with a back end relational database management system. Multi-disciplinary teams’ involvement, short-term goals, need for continuous changes/development, pre-clinical research, etc., are some of the factors that have driven the hybridization of open-source/commercial software products, and the usage of a health informatics model in combination with an agile software development model, in developing this software product named c-Surge. The c-Surge software was tested for user satisfaction using a survey, and to find any differences in the real-time data collected by the software and the manually collected data. Out of 12 members who used the software or participated in its development, 10 members have participated in the survey. Most of the users were satisfied/extremely satisfied with several functions of the software, or the overall software. A paired t-test was conducted to compare the real-time and manual data of each subject, and there was no statistically significant difference between the data types. The results show that home grown applications like c-Surge data management system are useful in fulfilling the data needs of a systems biology research project. The c-Surge data management system’s development process shows easier ways to develop a home grown application that can fulfill unique data needs of a systems biology research project within a reasonable economic expense.
Electronic Health Record-Integrated Handoff Notes: Content, Implementation, and Analysis
(2020-12) Arsoniadis, Elliot
Handoff is the process by which the care of a patient is transferred from the responsibility of one provider (or team of providers) to another. Handoff that occurs between physicians in training, or resident physicians, has become the focus of numerous quality and safety initiatives, especially since the introduction of work-hour restrictions for resident physicians that have increased the number of handoffs taking place during a hospitalization. The Handoff Note is the (traditionally paper) artifact accompanying the Handoff Process, and in its most basic form consists of the names of patients being transferred from the care of one clinician to another. Other data is often included, such as demographic data, room number, and a brief summary. Increasingly, Handoff Notes are becoming integrated into the Electronic Health Record (EHR). However, there remains no universally accepted standard for EHR-Integrated Handoff Note content, nor standards for what content is appropriate for automatic entry from the EHR versus manual entry by providers. Further, there have been few efforts to elucidate resident physician preferences for Handoff Note content, structure, or format, despite being the principal users of these tools. Although many data elements can be automatically entered into the Handoff Note by the EHR, certain key elements will likely remain manually entered by clinicians as narrative text. Analysis of these free text elements in Handoff Notes may reveal important insights for informaticians, safety and quality experts, and those involved in graduate medical education. In the first part of this study we look to key stakeholders for standards surrounding optimal Handoff Note content, and after choosing one with greatest buy-in, compare it to content contained in individual EHR-Integrated Handoff Notes described in the literature. The chosen standard covered 67% of Content Headings described in the literature, and thirteen more unique Content Headings were found in the literature to add to this standard. Using these findings from the literature and guided by prior semi-structured interviews with resident physicians, we performed a large-scale survey on resident physician preferences for Handoff Note content, structure, and format. We found that some of the most important and trustworthy elements of the Handoff Note were narrative text data manually entered by other clinicians, including “Plan”, “Illness Severity”, and “Patient Summary”. Based on these insights, we then designed and implemented an EHR-Integrated Handoff Note within our institution. Years after implementation we found that nearly all “primary” service teams and many “consult” service teams continued to utilize the Handoff Note, including resident physicians is such different specialties as medicine, pediatrics, behavioral health, obstetrics/gynecology, neurology, surgery, and critical care. Analysis of the narrative text portions of the Handoff Note showed that “Patient Summary” and “To Do” text boxes were updated 1.0 and 1.6 times per day, respectively. The majority of these updates occurred between 12 pm – 5:59 pm, likely indicating preparation of the Handoff Note for the evening Handoff Process. However, many changes also take place between 6 am – 11:59 am, indicating possible use of the Handoff Note to aid team rounding activities. We also analyzed narrative text for errors, using progress notes and other data from the EHR as gold standard. We found at least one error in 65% of Handoff Notes. The majority of errors were related to the omission of key data, rather than the entry of incorrect data. Increased errors were found with increasing hospital day, as well as with authors in early stages (medical students, PGY-1 physicians) and later stages (>PGY-4 or attending physician) of training. While the integration of the Handoff Note within the EHR and the automatic entry of many data elements into the note will prove useful, manual entry of certain narrative text continues to be critical. Future work on the parts of informatics and usability experts should focus on ways to make composing and updating these notes easier and encourage accuracy and frequency of updates. Members of the graduate medical education community should also make it a priority to formalize training surrounding accurate and complete Handoff Note composition as an important adjunct to existing training surrounding the Handoff Process. These tools have the potential to greatly improve patient safety and quality of care.
Evaluating the impact of Inpatient Pain Management Interactive Systems
(2019-08) Aldekhyyel, Raniah
The management of patients’ pain is essential for improving the overall quality of patient care. Equally important, is the patient’s role in managing their pain and the health system’s role in creating the ideal environment that supports high quality patient-centered care. Accordingly, many hospitals have and are investing in patient engagement technology systems aimed in supporting patients in their pain management care process. Despite the decade-plus existence of pain interactive entertainment systems, which are designed to distract patients from pain during treatment, their role in the management of pain remains understudied. Some of these interactive systems, in addition to their entertainment features, also include other functions to deliver standardized patient education and support the integration of patient-reported pain assessments into the electronic health record (EHR). However, despite technological advances that support this integration, this functionality is rarely implemented and researchers have rarely studied the effects of adopting interactive pain management systems (IPMS) in the inpatient setting. The objectives of this body of research are to address this gap in knowledge by evaluating various aspects of IPMS. The study was conducted in four phases: 1) examining the current evidence around and the state of IPMS, 2) evaluating the effect of a novel IPMS at the University of Minnesota Masonic Children’s Hospital (UMMCH), 3) characterizing user experience and satisfaction with use of IPMS and 4) understanding the population that utilizes the inpatient IPMS for the management of pain. We conducted a systematic literature review across seven databases to understand the current state of IPMS in an inpatient setting and examine their clinical outcomes. Out of the reviewed full-text articles, 17 were eligible and included in the final qualitative synthesis. Overall, there were two main types of IPMS within the inpatient setting; stand- alone systems and integrated platform systems. Reports examined a variety of outcome measures, including changes in patient-reported pain levels, patient engagement, user satisfaction, changes in clinical workflow, and changes in documentation. In our second study, we conducted a mixed methods case study approach to describe the development of a IPMS at the UMMCH and to evaluate the impact of implementation on clinical workflow, patient use, and compliance with nursing documentation of their pain reassessments. We employed a retrospective analysis of 56,931 patient records covering pre- and post- implementation. Despite nursing pain reassessment documentation being relatively low, implementation of the system led to a statistically significant increase in the overall nurse documentation and resulted in patient access to nonpharmacologic strategies to eliminate pain. In our third study, benefits and challenges on the use of an inpatient IPMS were identified by parents and nurses. Overall, there was a cohesive agreement among users regarding the impact of the IPMS in engaging and empowering patients/families, increasing patient satisfaction, and creating a communication platform, with the most usefulness feature being “Support of Timely Pain Reassessments”. Thematic content analysis was conducted to analyze nurse responses and identify high level themes. Six themes emerged related to “Benefits” from using the system: Phone Reminders, EHR Automatic Documentation, Decision Support, Patient Empowerment, Sense of Connection and Non-Medication Resources. There were also 12 “Challenges”: Uncertainty of Patient Rating Scores, Training Needs, Distraction, Discourage Best Practice, Low Utilization, Low Utilization Due to Environmental Factors, System Design Limitations, Pain Scale Discrepancy, Low Utilization Due to Patient Factors, Patient/Family Dissatisfaction, Workflow and Duplicate Charting Requirement. The ability to identify user experience associated with the use of these systems, potentially assists in designing IPMS to maximize positive impact on clinical outcomes and care quality. Finally, by conducting a retrospective analysis of inpatient records, our fourth study demonstrated differences in the patients’ IPMS usage among different hospital units based on the care and medical service these units provide and an increased usage was associated with the time of medication administration. Overall, this research collectively demonstrated the benefits of IPMSs and showed the potential of these systems in improving the patient and provider experience and the quality of care. Evaluating the effects of these systems on clinical outcomes, patient satisfaction, hospital workflow, and barriers and facilitators associated with the use of these systems is an important component in developing meaningful health information technology (HIT) systems to engage patients and address pain.
Experiences of health information managers with 20+ years of experiences in the complex and ever-changing healthcare environment
(2014-01) Valerius, Joanne Dorothy
This hermeneutical study examined the lived experiences of health information managers with 20+ years of experience in the complex and ever-changing environment of healthcare. The purpose of this research was to gain a deeper understanding of the experiences of credentialed health information managers with 20+ years of experience who have experienced moving from a paper-based medical record system to an electronic health record system. Eight credentialed health information managers were interviewed. They shared their experiences over the past 20+ years in the health information management profession. I conducted individual interviews of each participant. Four themes emerged and were emailed to the participants for verification. Four major themes were agreed upon: 1). Commitment to Data Quality, 2). Managing a Workforce in the Electronic Health Record environment, 3). Gender and Sexual Orientation Bias Experiences, and 4). Commitment to Collaboration. The knowledge gained in this study may help practitioners who are implementing electronic health record systems, other healthcare personnel who are implementing electronic health records, human resource development practitioners working in healthcare environments, and educators working with students in accredited health information management programs.
Finding Integrative Biomarkers from Biomedical Datasets: An application to Clinical and Genomic Data
(2015-08) Dey, Sanjoy
Human diseases, such as cancer, diabetes and schizophrenia, are inherently complex and governed by the interplay of various underlying factors ranging from genetic and genomic influences to environmental effects. Recent advancements in high throughput data collection technologies in bioinformatics have resulted in a dramatic increase in diverse data sets that can provide information about such factors related to diseases. These types of data include DNA microarrays providing cellular information, Single Nucleotide Polymorphisms (SNPs) providing genetic information, metabolomics data in terms of proteins and other metabolites, structural and functional brain data from magnetic resonance imaging (MRI), and electronic health records (EHRs) containing copious information about histo-pathological factors, demographic, and environmental effects. Despite their richness, each of these datasets only provides information about a part of the complex biological mechanism behind human diseases. Thus, effective integration of the partial information of any of these genomic and clinical data can help reveal disease complexities in greater detail by generating new data-driven hypotheses beyond the traditional hypotheses about biomarkers. In particular, integrative biomarkers, i.e., patterns of features that are predictive of disease and that go beyond the simple biomarkers derived from a single dataset, can lead to a customized and more effective approach to improving healthcare. This thesis focuses on addressing the key issues related to integrative biomarkers by developing new data mining approaches. One very important issue of biomarker discovery is that the models have to easily interpretable, i.e., integrative models have to be not only predictive of the disease, but also interpretable enough so that domain experts can infer useful knowledge from the obtained patterns. In one such effort to make models interpretable, domain information about disease relationships was used as prior knowledge during model development. In addition, a novel metric called I-score was proposed using medical literature to quantify the interpretability of the obtained patterns. Another key issue of integrative biomarker discovery is that there may be many potential relationships present among diverse datasets. For example, a very important types of relationship in biomarker discovery is interaction, which are those biomarkers spanning multiple datasets, whose combined features are more indicative of disease than the individual constituent factors. In particular, the individual effects of each type of factor on disease predisposition can be small and thus, remain undetected by most disease association techniques performed on individual datasets. Different types of relationships are explored and an association analysis based framework is proposed to discover them. The proposed framework is especially effective for discovering higher-order relationships, which cannot be found by the existing prominent integrative approaches for the biomarker discovery. When applied on real datasets collected from three different types of data from schizophrenic and normal subjects, this approach yielded significant integrated biomarkers which are biologically relevant. Disease heterogeneity creates further issues for integrative biomarker discovery, biomarkers obtained from clinicogenomic studies may not be applicable to all patients in the same degree, i.e., a disease consist of multiple subtypes, each occurring in different subpopulations. Some potential reasons responsible for disease heterogeneity are different pathways playing different roles in the same disease and confounding factors such as age, ethnicity and race, or genetic predisposition, which can be available in rich EHR data. Most biomarker discovery techniques use full space model development techniques, i.e., they assess the performance of biomarkers on all patients without finding the distinct subpopulations. In this thesis, more customized models were built depending on patient\'s characteristics to handle disease heterogeneity. In summary, several data mining techniques developed in this thesis advance the state-of-the art in integration of diverse biomedical datasets. Moreover, their applications on large-scale EHR yield significant discoveries, which can ultimately lead to generating new data-driven hypotheses for inferring meaningful information about complex disease mechanism.
The impact of data fragmentation on high-throughput clinical phenotyping.
(2012-02) Wei, Weiqi
Subject selection is essential and has become the rate-limiting step for harvesting knowledge to advance healthcare through clinical research. Present manual approaches inhibit researchers from conducting deep and broad studies and drawing confident conclusions. High-throughput clinical phenotyping (HTCP), a recently proposed approach, leverages the machine-processable content from electronic medical record (EMR) for this otherwise inefficient process making subject selections scalable and practical. However, the ability to capture a patient’s medical data is often limited because of commonly existing data fragmentation problems within current EMR systems, i.e. different data types (structured vs. unstructured), heterogeneous data sources (single medical center vs. multiple healthcare centers), and various time frames (short time frame vs. long time frame). The role of data fragmentation on HTCP remains unknown. In this dissertation, by taking advantage of the REP patient-record-linkage system and the richness of EMR data at Mayo Clinic, I provide a multidimensional and thorough demonstration of how data fragmentation affects HTCP. The predominant message that this dissertation delivered to the health informatics field can be summarized as data fragmentation of EMR has a remarkable influence on HTCP. This risk should be carefully considered and mitigated by clinical researchers for the secondary and meaningful use of EMR, especially when developing or executing an HTCP algorithm for subject selection.
Impact of task, structure, and environment on electronic health record adoption, use, and interoperability in hospitals.
(2010-06) Park, Young-Taek, M.P.H.
A paradigm in the field of Heath Informatics which has been taken for granted up until this point may be disappearing and a new paradigm may begin to take shape as paper-based medical record (PMR) systems are changing to the electronic health record (EHR) systems. Although the PMR has played a critical role in recording patient's clinical information, now many studies report that EHR systems improve quality of care beyond PMRs. For this reason, the governments across the world have initiated various approaches accelerating EHR adoption, use, and interoperability. However, there has been a paucity of studies explaining which factors affect EHR adoption, use, and interoperability in hospitals. The objective of this study is to predict and investigate those factors. This study used a non-experimental, retrospective, cross-sectional study design to measure hospitals' internal features. Specifically, this study conducted a nationwide EHR survey with the IT departments in South Korean hospitals by using online surveys from April 10 to August 3, 2009. It used Generalized Estimating Equations, an extension of the Generalized Linear Model, to interpret EHR system adoption and interoperability, and General Linear Mixed Model for the use of EHR systems. With respect to EHR system adoption, this study found that 1) the likelihood of EHR adoption increases as a hospital's task complexity - measured by the number of medical specialties - IT infrastructure, and organic structural characteristics, and environmental complexity - measured by the number of hospitals within the local area - increases and 2) there were significant interaction effects between task complexity and structural features. Assuming that a hospital adds additional medical specialties, the likelihood of adopting an EHR system of the hospital increases under the decentralized decision-making system, but decreases under the centralized decision-making system. The likelihood decreases under a high level of IT infrastructure, but increases under a lower level of IT infrastructure. For the hospitals' EHR use, there was not any relationship between EHR use and proposed hospital's internal features. Thus, alternative measures of EHR use and internal features were suggested. For EHR interoperability, this study found that 1) the likelihood of having EHR interoperability increases as task complexity and organic managerial features increases, and 2) two interaction effects were reported. Assuming that a hospital adds additional medical specialties, the likelihood of having EHR interoperability of the hospital increases at a high level of IT staff specialization, but decreases at a lower level of IT staff specialization. At a high level of environmental complexity with more than average number of hospitals within the local area, the likelihood of having EHR interoperability of the hospitals located in the area increases as IT staff specialization increases. However, the likelihood decreases as IT staff specialization increases at a lower level of environmental complexity with less than average number of hospitals within the local area. In conclusion, this study verified that hospitals' task, structure, and environmental features were critical factors affecting the EHR system adoption and interoperability. However, these factors did not affect EHR use. Different approaches measuring EHR use and hospitals' various internal features were recommended. This study's results can provide health informaticians, hospital IT managers, and health politicians with new information about EHR system adoption, use, and interoperability for their innovative decision-making.
Improved prediction of biodegradation pathways: visualization and performance.
(2011-02) Gao, Junfeng
The University of Minnesota Pathway Prediction System (UM-PPS) (http://umbbd.msi.umn.edu/predict/) is a rule-based system that predicts plausible pathways for microbial degradation of organic compounds. Its biotransformation rules are based on reactions found in the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.msi.umn.edu/) or in the scientific literature. Since the UM-PPS was created in 2002, its rule base has grown to 275 entries. The original system predicted one level of prediction at a time. It provided a limited view of prediction results and heavily relied on manual interventions. It matched the query compound with all biotransformation rules one by one, which was a time-consuming process. In 2008, the two-level visualization was first implemented to allow users to view two levels of predictions at a time. However, this visualization approach was usually not able to show the complete metabolism of a query compound, and users still needed expert knowledge to make educated choices to continue the prediction. In 2009, we started to develop a multi-level visualization and, simultaneously, work on increasing prediction speed. In 2010, the multi-level visualization was implemented to predict up to six levels of predictions at a time. Not only more products, but also common intermediates and cleavage products are displayed. Users can view prediction alternatives much more easily in a tree-like interactive graph. A multi-level prediction can be computationally intensive and requires users to wait longer than desired for the prediction results. Therefore, we used a multi-thread computing strategy that decreased the prediction run-time by half. We balanced the computing threads and pre-loaded all UM-PPS database tables to permit quick access to its data. Both of these improvements resulted in an additional 30% decrease in prediction run-time. We conducted a simulation study and used another web server to reduce the queuing interference by over 85%. Beta testers were satisfied with its visualization and performance. The above improvements lead to a smarter and faster UM-PPS that has continued its growth in the past 4 years. It now displays better graphical results and predicts biodegradation pathway in a timely manner.
Interview with Bruce Blazar
(2015-06-30) Blazar, Bruce; Tobbell, Dominique
Dr. Blazar begins by discussing the establishment of the University of Minnesota’s Center for Translational Medicine and the Clinical and Translational Science Institute, and the relationship between the two centers. He next goes on to describe the application process for the National Institutes of Health Clinical and Translation Science Award and the major achievements that have resulted from the CTSA grant. Blazar goes on to discuss the importance of biomedical informatics within the CTSA program; the significance of the appointment of Constantin Aliferis, MD, Ph.D., FACMI as director of the Institute for Health Informatics, director of the CTSI Biomedical Informatics program, and the University of Minnesota chief research informatics officer; and the ongoing support and investment of the Academic Health Center leadership in biomedical and health informatics. Next, Blazar reflects on the distinctiveness of the University of Minnesota’s program among the sixty institutions within the CTSA consortium, and discusses the training initiatives that are part of the CTSA program.
Interview with Constantin Aliferis
(2015-06-08) Aliferis, Constantin F.; Tobbell, Dominique
Constantin Aliferis begins by discussing his educational background, including his early interest in biomedical and health informatics. He describes the main focus of his research since graduate school, which has included machine learning and the analysis of complex and high-dimensional data sets; scientometrics and informatics retrieval; and model building, analysis, and knowledge discovery across a variety of disease domains. Aliferis goes on to briefly discuss his tenure at Vanderbilt University, followed by a more detailed discussion of his tenure at New York University. Next, Aliferis offers his definition of precision medicine. The remainder of the interview focuses on health informatics at the University of Minnesota. Aliferis describes his vision for the Institute for Health Informatics, reflects on the strong backing provided by the leadership of the University and the University’s Academic Health Center to support this vision, and offers his perspective on the future of the field of biomedical and health informatics.
Interview with Frank Cerra
(University of Minnesota, 2014-07-31) Cerra, Frank B.; Tobbell, Dominique
Dr. Frank Cerra begins part one of his interview by describing his undergraduate education at SUNY Binghamton, his medical education at Northwestern University Medical School, and his residency at SUNY Buffalo. He then describes his recruitment to the University of Minnesota, his early goals, and his growing administrative roles. He describes the leadership implications of investigations into Antilymphocyte Globulin (ALG) on the Medical School and the merging of University Hospital with Fairview Health Services. He then discusses the following topics: his interest in surgery; the culture of the University of Minnesota’s Department of Surgery; his work with the pharmaceutical industry and the College of Pharmacy; his work developing a critical care program at the University; and his relationships with the hospital directors, hospital nursing, and the School of Nursing. In part of two his interview, Dr. Cerra intersperses reflections on finances and relations among different levels of administration in the University, the AHC, and University Hospital. He also discusses the following topics: his relationship with Neal Gault; strategic and long-range planning; the goals of the AHC; the formation of University of Minnesota Physicians; the establishment of the Biomedical Ethics Center (later the Center for Bioethics) and the Masonic Cancer Center; the investigations into ALG and Dr. John Najarian; the establishment of the Center for Drug Design; William Brody as Provost of the AHC and issues surrounding faculty tenure; and the establishment of the Institute for Health Informatics. In part three of his interview, Dr. Cerra expands on the decision to merge University Hospital with Fairview Health Services, particularly focusing on logistics, culture, and reception. He also discusses failed attempts to create a unified children’s hospital in the Twin Cities. He then reflects on the following topics: the major challenges and achievements of his tenure as senior vice president; the merging of the positions of Senior Vice President of Health Sciences and Dean of the Medical School; the creation of the Clinical and Translational Science Institute and the Biomedical Discovery District; and the medical device industry in Minnesota. He concludes by describing the University of Minnesota and Mayo Clinic partnership in research.
Thai hospitals' adoption of information technology: a theory development and nationwide survey
(2011-12) Theera-Ampornpunt, Nawanan
Background: With documented benefits and recent public policies, health information technology (IT) has received increasing attention in recent years. However, knowledge about Thailand's state of hospital IT adoption is lacking. The literature also identifies organizational management practices that are important to health IT implementation, but these factors are rarely included in quantitative analysis. Paucity of theoretical developments in the area also prevents a systematic approach to IT implementation.Objective: To describe the current state of IT adoption in Thai hospitals and test a proposed model of organizational IT adoption that includes facilitating management practices and important hospital characteristics, motivated in part by Paré and Sicotte (2001)'s IT sophistication framework with modifications.Materials and Methods: A nationwide mail survey was conducted using a developed instrument with established face and content validity in 1,302 hospitals in Thailand after a pilot study using five hospitals for pre-test purposes. Each hospital's IT chief or executive was asked to assess the degrees of specific technologies' adoption, IT-supported hospital functions, within- and outside-hospital information sharing, and presence of specific management practices, each in a 5-point Likert-type scale. Confirmatory and exploratory factor analyses were done, resulting in the rejection of the proposed model and a new set of IT adoption factors that fit the data better. Average scores for each of these new IT adoption aspects were analyzed descriptively to provide Thailand's baseline adoption levels. Construct and criterion validity was also assessed. Path analysis was used to test the proposed model of hospital IT adoption and identify associated organizational factors. Estimates for adoption of basic electronic health records (EHRs), comprehensive EHRs, and computerized physician order entry (CPOE) were also computed from relevant IT-supported functions for cross-study comparisons.Results: The nationwide survey received a 70% response rate, but responding hospitals tended to be somewhat larger and public. Thai hospitals overall had acceptable levels of IT adoption, but information sharing outside the hospitals was very limited. When both outpatient and inpatient settings were considered, about 50% of responding hospitals had complete or partial basic EHR adoption and only 5% had comprehensive EHR adoption, but 90% had CPOE for medication orders. Adoption estimates for the outpatient setting alone were somewhat larger than the inpatient setting. Significant correlations among the different aspects of IT adoption and between these constructs and other criterion variables provide evidence for construct and criterion validity. In path analysis, after respecifying the model based on the factor patterns discovered from the data, the final model indicated significant effects of public status on adoption of infrastructural technologies such as networking and master patient index, as well as on internal information sharing. Bed size was positively associated with infrastructural technologies adoption but negatively associated with the levels of IT-supported clinical functions. Teaching status was not associated with any aspects of IT adoption in the path model. As hypothesized, the extent of facilitating operational IT management was associated with the levels of technology adoption and use of IT to support clinical EHR workflows (order entry and results viewing) as well as inpatient clinical documentation. These latter three constructs were also associated with the extent of internal information sharing, while the extent of external information sharing was associated with the levels of internal information sharing and IT support for inpatient clinical documentation.Discussion: Thailand's adoption picture is very encouraging with many hospitals having some IT infrastructure in place, though adoption gaps still exist. The discovered IT adoption factors and the developed survey instrument had supporting evidence for its validity, and the final model resulting from path analysis provides a useful framework for health IT adoption in future IT adoption studies. The positive association between public status and IT adoption and lack of significant hypothesized association between IT adoption and bed size or teaching status were surprising but may reflect the unique health IT market and dynamics in Thailand. Conclusion: Basic IT adoption in Thai hospitals appears to have passed the tipping point. Focus should be on adoption of more advanced technologies (such as comprehensive EHRs and clinical decision support systems) and ensuring that adoption translates into better processes and outcomes, as well as addressing barriers to health information exchange. The utility of the proposed framework is demonstrated, as is the importance of identified facilitating IT management practices. The final model from this study, named the Theory of Hospital Adoption of Information Systems (THAIS) here, should be cross-validated and refined in future studies.Funding: This study was supported by a research grant from the Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand under Grant No. RD53065/year 2010.
Understanding clinician information demands and synthesis of clinical documents in electronic health record systems.
(2012-06) Farri, Oladimeji Feyisetan
Large quantities of redundant clinical data are usually transferred from one clinical document to another, making the review of such documents cognitively burdensome and potentially error-prone. Inadequate designs of electronic health record (EHR) clinical document user interfaces probably contribute to the difficulties clinicians experience while processing patient-specific information during time-constrained patient encounters. Furthermore, the continuous need for clinicians to review multiple EHR clinical documents during the typical out-patient visit increases the likelihood of overloading their working memory in the short duration available for complex cognitive activities related to patient care. In a collection of three studies incorporating fundamental principles in clinical informatics, cognitive psychology and human-computer interaction, the think-aloud protocol, combined with other qualitative and quantitative methodologies, was utilized to investigate cognitive processes associated with clinicians' synthesis of EHR clinical documents, the impact of time restrictions on these processes, and implementing a novel visualization tool to enhance processing of these documents during patient care. These studies serve to fill fundamental knowledge gaps in our understanding of how clinicians interact with EHR systems when using clinical documents and can help future EHR system user interface design for clinical documentation with the ultimate goal of improving patient care and clinician satisfaction with these systems.

University Digital Conservancy

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