Medication errors are among the most common causes of unintended harm to patients and have led to many deaths. Some categories of medication errors include; medications administered to the wrong person; medications administered at the wrong time, through the wrong route; administration of the wrong medication and/or dose; and the omission of medications. Guided by the logic model, the just culture model, and the Knowles theory of andragogy, the purpose of the project was to determine if providing information related to evidence-based strategies to reduce medication errors would result in safer medication administration practices and improved patient outcomes A survey was administered to 11 medical and nursing staff at an outpatient internal medical clinic to determine their knowledge about medications errors prior to providing evidence-based information on strategies to reduce medication errors. After the educational session, a survey was conducted to determine staff members' retention of knowledge. A significant increase in the percent of correct responses to the survey from 68% to 100% after the educational session (t = -3.9; p = 0.001)) shows that the educational in-service had a positive outcome in increasing staff members' knowledge about reducing medication errors in an out-patient internal medicine clinic. Improving clinic staff knowledge and behaviors regarding medication administration has the potential to bring about social change by decreasing medication errors, improving patient safety, and improving health outcomes.

Indiana University-Purdue University Indianapolis (IUPUI) / Primary users can create a user experience (UX) for others—secondary users— when interacting with a system in public. Common ground occurs when people have certain knowledge in common and each knows that they have this shared understanding. This research investigates how designing for a secondary UX improves common ground during a patient-provider first encounter. During formative work, patients and providers participated in telephonic interviews and answered online questionnaires so that their respective information requirements for clinical encounters could be understood. The outcome of the formative work was a smartphone application prototype to be used as the treatment in an experimental study. In a mixed methods study, with a patient role-player using the prototype during a simulated clinical encounter with 12 providers, the impact of the prototype upon secondary user satisfaction and common ground was assessed. The main finding was that the prototype was capable of positively impacting secondary user satisfaction and facilitating common ground in certain instances. Combining the notions of human-computer interaction design, common ground, and smartphone technology improved the efficiency and effectiveness of providers during the simulated face-to-face first encounter with a patient. The investigation substantiated the notion that properly designed interactive systems have the potential to provide a satisfactory secondary UX and facilitate common ground.

Indiana University-Purdue University Indianapolis (IUPUI) / Phenotyping definitions are essential in cohort identification when conducting clinical research, but they become an obstacle when they are not readily available. Developing new definitions manually requires expert involvement that is labor-intensive, time-consuming, and unscalable. Moreover, automated approaches rely mostly on electronic health records’ data that suffer from bias, confounding, and incompleteness. Limited efforts established in utilizing text-mining and data-driven approaches to automate extraction and literature-based knowledge discovery of phenotyping definitions and to support their scalability. In this dissertation, we proposed a text-mining pipeline combining rule-based and machine-learning methods to automate retrieval, classification, and extraction of phenotyping definitions’ information from literature. To achieve this, we first developed an annotation guideline with ten dimensions to annotate sentences with evidence of phenotyping definitions' modalities, such as phenotypes and laboratories. Two annotators manually annotated a corpus of sentences (n=3,971) extracted from full-text observational studies’ methods sections (n=86). Percent and Kappa statistics showed high inter-annotator agreement on sentence-level annotations. Second, we constructed two validated text classifiers using our annotated corpora: abstract-level and full-text sentence-level. We applied the abstract-level classifier on a large-scale biomedical literature of over 20 million abstracts published between 1975 and 2018 to classify positive abstracts (n=459,406). After retrieving their full-texts (n=120,868), we extracted sentences from their methods sections and used the full-text sentence-level classifier to extract positive sentences (n=2,745,416). Third, we performed a literature-based discovery utilizing the positively classified sentences. Lexica-based methods were used to recognize medical concepts in these sentences (n=19,423). Co-occurrence and association methods were used to identify and rank phenotype candidates that are associated with a phenotype of interest. We derived 12,616,465 associations from our large-scale corpus. Our literature-based associations and large-scale corpus contribute in building new data-driven phenotyping definitions and expanding existing definitions with minimal expert involvement.

Indiana University-Purdue University Indianapolis (IUPUI) / Periodontal disease (PD) affects 42% of US population resulting in compromised quality of life, the potential for tooth loss and influence on overall health. Despite significant understanding of PD etiology, limited longitudinal studies have investigated PD change in response to various treatments. A major barrier is the difficulty of conducting randomized controlled trials with adequate numbers of patients over a longer time. Electronic dental record (EDR) data offer the opportunity to study outcomes following various periodontal treatments. However, using EDR data for research has challenges including quality and missing data. In this dissertation, I studied a cohort of patients with PD from EDR to monitor their disease status over time. I studied retrospectively 28,908 patients who received comprehensive oral evaluation at the Indiana University School of Dentistry between January 1st-2009 and December 31st-2014. Using natural language processing and automated approaches, we 1) determined PD diagnoses from periodontal charting based on case definitions for surveillance studies, 2) extracted clinician-recorded diagnoses from clinical notes, 3) determined the number of patients with disease improvement or progression over time from EDR data. We found 100% completeness for age, sex; 72% for race; 80% for periodontal charting findings; and 47% for clinician-recorded diagnoses. The number of visits ranged from 1-14 with an average of two visits. From diagnoses obtained from findings, 37% of patients had gingivitis, 55% had moderate periodontitis, and 28% had severe periodontitis. In clinician-recorded diagnoses, 50% patients had gingivitis, 18% had mild, 14% had moderate, and 4% had severe periodontitis. The concordance between periodontal charting-generated and clinician-recorded diagnoses was 47%. The results indicate that case definitions for PD are underestimating gingivitis and overestimating the prevalence of periodontitis. Expert review of findings identified clinicians relying on visual assessment and radiographic findings in addition to the case definition criteria to document PD diagnosis. / 2021-08-10

Blockchain is an interesting technology, with different projects developing every day since it first gained its light back in 2008. More and more research finds blockchain useful in several different sectors. One of the sectors being healthcare, specifically for electronic health records (EHR). EHR contains highly sensitive data which is critical to protect and, just in the year 2019, 41,232,527 records were deemed stolen. Blockchain can provide several benefits when it comes to EHR, such as increased security, availability, and privacy, however, it needs to be done correctly. Due to blockchain being a rather novel technology, there is room for improvement when it comes to integrating blockchain with EHR. In this thesis a framework for EHR in the healthcare sector is proposed, using Ethereum based smart contracts together with decentralized off-chain storage using InterPlanetary File System (IPFS) and strong symmetric encryption. The framework secures the records and provides a scalable solution. Furthermore, a discussion and evaluation regarding several security aspects that the framework excels on as well as what the framework could improve on.

The present study takes legibility research and extends it to the medical setting. Internal Medicine Physicians from UCF developed six passages of medical text detailing a History of Present Illness (HPI) Report from an emergency department as well as comprehension questions for the purpose of our study. In our study, we first presented non-medical passages and comprehension questions in six common fonts to identify participants' individual fastest and slowest fonts. We then gave participants medical passages in both their best and worst fonts while measuring reading speed and comprehension. This study was delivered to a population of Amazon Mechanical Turk crowd workers to help us better understand how legibility improvements can be made within specific fields. We hope that with this study we can begin the process of restructuring Electronic Health Records to be more usable and efficient for physicians.

Recent progress in treatment interventions has resulted in increased survival rates and longevity for diagnosed heart failure patients. However, heart failure still remains one of the leading causes of rehospitalization worldwide, where emergency readmissions continue to be a common occurrence. The multifactorial complexity of heart failure makes clinical judgment difficult and may lead to erroneous discharge prognoses and estimates in recovery trajectories. Recognizing emergency readmissions among heart failure patients who have been discharged is crucial within the critical six-month post-discharge period to proactively address additional support needs. To address the research question, “To what extent can machine learning models predict emergency readmissions in Chinese heart failure patients within six months post-discharge?”, this paper uses electronic health records obtained from a single healthcare center in China, containing 2,008 validated heart failure patients. This study adopts an experimental research methodology, where four machine learning models are developed to explore the research question. To ensure robustness, 10-fold cross-validation with stratified sampling and a two-step feature selection process is performed in addition to evaluation through metrics such as the area under the receiving operating curve and F1 Score. The findings indicate only modest predictive capability among the classifiers in the validation cohort. The best-achieved area under the receiving operating curve and F1 Score are obtained from separate classifiers with scores of 0.682 and 0.577, respectively. The findings provide valuable insights into future research on the effectiveness of ML-based prediction models for emergency readmission in Chinese heart failure patients.

Medical record systems are used whenever caregiving is practiced. The medical records serve an important role in establishing patient safety. It is not possible to prevent honest-but-curious doctors from accessing records since it is legally required to allow doctors to access health records for emergency cases. However, it is possible to log accesses to records and mitigate malicious behaviour through rate limiting. Nevertheless, many of the records systems today are lacking good authentication, logging and auditing and existing proposals for securing medical records systems focus on the context of multiple different healthcare providers. In this thesis, an architecture for an electronic health records system for a telemedicine provider is designed. The architecture is based on several requirements from both the legal perspective and general security conventions, but also from a doctor’s perspective. Unlike the legal and general security conventions perspective, doctor requirements are more functionality and usability concerns rather than security concerns. The architecture is evaluated based on two main threat models and one secondary threat model, i.e. insider adversaries. Almost all requirements are satisfied by the solution design, but the two main threat models can not be entirely mitigated. It is found that confidentiality can be violated by the two main threat models, but the impact is heavily limited through audit logging and rate limiting. / Journalsystem är en central del inom vården och patientjournaler har en stor roll i att uppnå bra patientsäkerhet. Det är inte möjligt att förhindra läkare från att läsa särskilda journaler eftersom läkare behöver tillgång till journaler vid nödsituationer. Däremot går det att logga läkarnas handlingar och begränsa ondsint beteende. Trots det saknar många av dagens journalsystem bra metoder för autentisering, loggning och granskning. Befintliga förslag på att säkra journalsystemen fokuserar på sammanhang där flera olika vårdgivare är involverade. I den här rapporten presenteras en arkitektur för ett patientjournalsystem till en telemedicinsk leverantör. Arkitekturen utgår från flertalet krav baserade på både ett legalt perspektiv och generella säkerhetskonventioner, men även läkares perspektiv. Arkitekturen är evaluerad baserat på två huvudsakliga hotmodeller och en sekundär hotmodell. Arkitekturen uppfyller så gott som alla krav, men de två huvudsakliga hotmodellerna kan inte mitigeras helt och hållet. De två huvudsakliga hotmodellerna kan bryta sekretessen, men genom flödesbegränsning och granskning av loggar begränsas påverkan.

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<p>Early trials of Electronic Personal Health Records (ePHRs) show they provide two strong benefits: better healthcare outcomes and lower taxpayer costs. However, consumers are concerned about the possible loss or misuse of personal health data. For people to adopt ePHRs, they must trust both the system and the operating organization. The model presented here studies consumers’ likelihood of adopting ePHRs, combining trust, distrust, risk, motivation, and ease of use; as well as their perceptions of government, software vendors, and physicians as providers of ePHRs. Based on the Technology Acceptance Model, and incorporating elements of trust-distrust dualism and perceived risk, the model was tested empirically using survey data from 366 Canadian adults. The model explains 52 percent of the variance in the intention to use an ePHR, with strong negative effects from perceived risk and distrust, and strong positive effects from trust and perceived usefulness. Other findings include further evidence that trust and distrust are different constructs, not ends of a spectrum; that Canadians’ relationship with their healthcare system is complex; and that the risks in using an online system can be overcome by the perceived benefits. Open-ended responses show that people generally trust their doctors, but are sceptical that a doctor could provide a secure ePHR. Responses indicated that participants liked the consolidation of data and ease of access, but feared loss of privacy.</p> / Doctor of Philosophy (PhD)