A Model for Setting Optimal Data-Acquisition Policy and its Application with Clinical Data

Manual data acquisition is often subject to incompleteness – data attributes that are missing due to time and data-availability constraints, which might damage data usability for analyses and decision making. This study introduces a novel optimization model for setting mandatory versus voluntary attributes in a dataset. This model may direct the decision of whether or not to enforce the acquisition of certain attributes, given certain constraints and dependencies. The feasibility and the potential contribution of the proposed model were evaluated with a clinical dataset that reflects Colonoscopy procedures performed in a large hospital over a 4-year period. The evaluation demonstrated that the model can be reasonably estimated within the given context, and that its implementation may contribute important insight toward improving data quality. The current data-acquisition setup was shown to be sub-optimal, and some further evaluation identified factors that influence incompleteness and may require revisions to current data acquisition policies

The Use of Clinical Decision Support to Improve Nursing Practice

Healthcare information technology is solidly entrenched in most acute care hospitals but the need to demonstrate its positive impact on patient outcomes persists. Clinical decision support (CDS) is an informatics tool that is highly customizable to promote patient improvement activities. Despite its high potential, studies have had mixed results regarding the impact of CDS and it has not been widely studied in the realm of nursing practice. One aim of this dissertation was to analyze the concept of CDS in order to inform the examination of the relationships between CDS implementation and nursing interventions. The determining factors of nurses use and acceptance of CDS was also described within the context of the CDS concept schematic developed. Data from 4718 pediatric hospital admissions were analyzed to examine if there was a relationship between the implementation of CDS and the implementation of sequential compression devices (SCD) for the purpose of preventing VTE and the placement of chart notifications of VTE risk. Admissions with patients who were identified as at risk for VTE had SCDs placed almost two and one-half times more often after the CDS was implemented (RR = 2.32; 95% CI (1.9 – 2.83)) and 33 times more likely to have chart notifications placed. In order to describe the determining factors of use, the unified theory of acceptance and use of technology (UTAUT) was adapted to create an electronic survey. Two multivariate regression models were built to describe the UTAUT model from previous literature. Results demonstrated that the model as described explains the majority of the data but also highlighted some weaknesses in the realm of the construct voluntary use. The results of this dissertation contribute to the limited literature regarding CDS use in nursing practice

Reconocimiento de enfermedades en fichas técnicas de medicamentos y su anotación con SNOMED-CT

La interoperabilidad o habilidad para intercambiar información entre sistemas informáticos es una cuestión de gran importancia en la informática médica. La interoperabilidad influye directamente en la calidad de los sistemas médicos existentes en la práctica clínica, ya que permite que la información se trate de manera eficiente y consistente. Para la comunicación entre sistemas informáticos heterogéneos se necesitan terminologías o diccionarios que representen e identifiquen conceptos médicos de forma única, sin importar el idioma o la forma lingüística en la que aparezcan. Estas terminologías permiten a los sistemas informáticos tener la misma visión del mundo y que la información intercambiada sea entendible. Actualmente, los esfuerzos para la adopción de estas terminologías en la práctica clínica recaen en los profesionales del dominio médico. Los profesionales son los encargados de reconocer conceptos médicos manualmente en documentos del área de la medicina y anotarlos con el código del concepto asociado en la terminología. No existe ningún método automático que permita el reconocimiento de conceptos de un determinado dominio, como por ejemplo las enfermedades, y que posteriormente encuentre el concepto asociado dentro de una terminología con un grado de precisión suficientemente elevado para que pueda ser adoptado en la práctica clínica. En esta tesis de máster se propone un nuevo método para el reconocimiento de enfermedades en fichas técnicas de medicamentos y su posterior mapeo con la terminología médica SNOMED-CT en español. El método utiliza dos nuevas técnicas propuestas en la tesis para cada fase. La nueva técnica para el reconocimiento de enfermedades propuesta está basada en reglas y en diccionarios especializados en medicina. La nueva técnica de mapeo está basada en la generación de las posibles combinaciones lingüísticas en las que puede aparecer la enfermedad para realizar comparaciones exactas de palabras, utilizando las funciones sintácticas de las palabras como guía. El método propuesto se centra en la identificación de enfermedades dentro de la sección de indicaciones terapéuticas de las fichas técnicas de medicamentos

Developing a Tool to Support Decisions on Patient Prioritization at Admission to Home Health Care

Background and aims: Millions of Americans are discharged from hospitals to home health every year and about third of them return to hospitals. A significant number of rehospitalizations (up to 60%) happen within the first two weeks of services. Early targeted allocation of services for patients who need them the most, have the potential to decrease readmissions. Unfortunately, there is only fragmented evidence on factors that should be used to identify high-risk patients in home health. This dissertation study aimed to (1) identify factors associated with priority for the first home health nursing visit and (2) to construct and validate a decision support tool for patient prioritization. I recruited a geographically diverse convenience sample of nurses with expertise in care transitions and care coordination to identify factors supporting home health care prioritization. Methods: This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults referred to home health. Variables included sociodemographics, diagnosis, comorbid conditions, adverse events, medications, hospitalization in last 6 months, length of stay, learning ability, self-rated health, depression, functional status, living arrangement, caregiver availability and ability and first home health visit priority decision. A combination of data mining and logistic regression models was used to construct and validate the final model. Results: The final model identified five factors associated with first home health visit priority. A cutpoint for decisions on low/medium versus high priority was derived with a sensitivity of 80% and specificity of 57.9%, area under receiver operator curve (ROC) 75.9%. Nurses were more likely to prioritize patients who had wounds (odds ratio [OR]=1.88), comorbid condition of depression (OR=1.73), limitation in current toileting status (OR= 2.02), higher numbers of medications (increase in OR for each medication =1.04) and comorbid conditions (increase in OR for each condition =1.04). Discussion: This dissertation study developed one of the first clinical decision support tools for home health, the PREVENT - Priority for Home Health Visit Tool. Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes