A Spatial Inquiry of the U.S. Opioid Epidemic and Geodemographic Segmentation Systems

The objective of this dissertation research was to explore the use of geodemographic segmentation as a socioeconomic variable to spatially analyze opioid related mortalities and hospital discharges. Opioid data were investigated by three ICD-10 classifications: heroin, other opioids, and other synthetic narcotics. Demographic and spatial characteristics of opioid mortality were examined using data from the Centers for Disease Controls (CDC) National Vital Statistics System mortality (NVSS-M) multiple causes of death dataset via the WONDER database for the year 2017. This was followed by a literature review of previous research that investigated the use of geodemographic segmentation systems in health research.Spatial rules association data mining was used to explore the relationship between county level ESRI Tapestry segmentation and opioid mortality rates from the CDC NVSS-M for the years 2015-2017. These findings were further examined by comparing the results to the 2017 Tennessee opioid mortality and Tapestry data at the ZIP code level. Additional demographic analysis was conducted using county level socioeconomic variables, unemployment, and opioid prescribing rates.Tennessee opioid related hospital discharge and mortality data from the year 2017 were analyzed using rate mapping, ANOVA, descriptive statistics, and spatial rules based association data mining. The rates were associated with ESRI Tapestry LifeMode groupings. The results of the analysis of Tennessees ZIP code level data were compared to the CDCs county level data from 2017 to examine scale dependency of the analysis and data

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers

Technology Target Studies: Technology Solutions to Make Patient Care Safer and More Efficient

Presents findings on technologies that could enhance care delivery, including patient records and medication processes; features and functionality nurses require, including tracking, interoperability, and hand-held capability; and best practices

Predicting length of stay (LOS) in a hospital post-sugery

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe amount of time a patient stays in the hospital after a surgery has been an issue that hospital management faces, a longer stay in the recovery room involves a high cost to the hospital and consumes a lot of hospital resources, manpower and equipment. The amount of time is difficult to predict precisely since there are many external and internal factors that account for a longer or shorter stay and it is difficult for a team to consider all these factors and make this estimation manually. With the advancement of machine learning methods and models this prediction can be made automatically. The aim of this study was to create a predicting model that look at the patient data and the procedure data and predicts the amount of time the patient will stay after the surgery to make the current prediction of the length of stay by the hospital more accurate and compliment the current surgery scheduling and discharge system. To achieve the objective, a data mining approach was implemented. Python Language was used, with particular emphasis on Scikit-Learn, pandas and Seaborn packages. Tables from a relational database were processed and extracted to build a dataset. Exploratory data analysis was performed, and several model configurations were tested. The main differences that separate the models are outlier treatment, sampling techniques, feature scalers, feature engineering and type of algorithm – Linear Regression, Decision Trees Regressor, Multilayer Perceptron Regressor, Random Forest Regressor, Light Gradient Boosting Machine Regressor and Gradient Boosting Regressor. A total of 32993 hospital episodes were observed on this study. Out of these, 2006 were eliminated due to some data anomalies, namely, values that were wrong or impossible. The data was split in training and test data. Several model configurations were tested. The main differences that separate the models are outlier treatment, feature scalers, feature engineering and the type of algorithm. The best performing model had a score of 0.73 R2 which was obtained by using the Light Gradient Boosting Machine Regressor Algorithm using outlier removal, Robust Scaling and using all the features in the dataset

Safe start at home : what parents of newborns need after early discharge from hospital - a focus group study

The length of postpartum hospital stay is decreasing internationally. Earlier hospital discharge of mothers and newborns decreases postnatal care or transfers it to the outpatient setting. This study aimed to investigate the experiences of new parents and examine their views on care following early hospital discharge.; Six focus group discussions with new parents (n = 24) were conducted. A stratified sampling scheme of German and Turkish-speaking groups was employed. A 'playful design' method was used to facilitate participants communication wherein they used blocks and figurines to visualize their perspectives on care models The visualized constructions of care models were photographed and discussions were audio-recorded and transcribed verbatim. Text and visual data was thematically analyzed by a multi-professional group and findings were validated by the focus group participants.; Following discharge, mothers reported feeling physically strained during recuperating from birth and initiating breastfeeding. The combined requirements of infant and self-care needs resulted in a significant need for practical and medical support. Families reported challenges in accessing postnatal care services and lacking inter-professional coordination. The visualized models of ideal care comprised access to a package of postnatal care including monitoring, treating and caring for the health of the mother and newborn. This included home visits from qualified midwives, access to a 24-h helpline, and domestic support for household tasks. Participants suggested that improving inter-professional networks, implementing supervisors or a centralized coordinating center could help to remedy the current fragmented care.; After hospital discharge, new parents need practical support, monitoring and care. Such support is important for the health and wellbeing of the mother and child. Integrated care services including professional home visits and a 24-hour help line may help meet the needs of new families

Use of statistical analysis, data mining, decision analysis and cost effectiveness analysis to analyze medical data : application to comparative effectiveness of lumpectomy and mastectomy for breast cancer.

Statistical models have been the first choice for comparative effectiveness in clinical research. Though effective, these models are limited when the data to be analyzed do not fit the assumed distributions; which is mostly the case when the study is not a clinical trial. In this project, data mining, decision analysis and cost effectiveness analysis methods were used to supplement statistical models in comparing lumpectomy to mastectomy for surgical treatment of breast cancer. Mastectomy has been the gold standard for breast cancer treatment for since the 1800s. In the 20th century, an equivalence of mastectomy and lumpectomy was established in terms of long-term survival and disease free survival. However, short term comparative effectiveness in post-operative outcomes has not been fully explored. Studies using administrative data are lacking and no study has used new technologies of self-expression, particularly the internet discussion board. In this study, data used were from the Nationwide Inpatient Sample (NIS) 2005, the Thomson Reuter\u27s MarketScan 2000 - 2001, the medical literature on clinical trials and online individuals\u27 posts in discussion boards on breastcancer.org. The NIS was used to compare lumpectomy to mastectomy in terms of hospital length of stay, total charges and in-hospital death at the time of surgery. MarketScan data was used to evaluate the comparative follow-up outcomes in terms of risk of repeat hospitalization, risk of repeat operation, number of outpatient services, number of prescribed medications, length of stay, and total charges per post-operative hospital admission on a period of eight months average. The MarketScan was also used to construct a simple post-operative hospital admission predictive model and to perform short-term cost-effectiveness analysis. The medical literature was used to analyze long term -10 years- mortality and recurrence for both treatments. The web postings were used to evaluate the comparative cost to improve quality of life in terms of patient satisfaction. In NIS and MarketScan data, International Classification of Disease, 9th revision, Clinical Modification (lCD-9-CM) diagnosis codes were used to extract cases of breast cancer; and ICD-9-CM procedure codes and Current Procedural Terminology, 4th edition procedure codes were used to form groups of treatment. Data were pre-processed and prepared for analysis using data mining techniques such as clustering, sampling and text mining. To clean the data for statistical models, some continuous variables were normalized using methods such as logarithmic transformation. Statistical models such as linear regression, generalized linear models, logistic and proportional hazard (Cox) regressions were used to compare post-operative outcomes of lumpectomy versus mastectomy. Neural networks, decision tree and logistic regression predictive modeling techniques were compared to create a simple predictive model predicting 90-day post-operative hospital re-admission. Cost and effectiveness were compared with the Incremental Cost Effectiveness Ratio (ICER). A simple method to process and analyze online po stings was created and used for patients\u27 input in the comparison of lumpectomy to mastectomy. All statistical analyses were performed in SAS 9.2. Data Mining was performed in SAS Enterprise Miner (EM) 6.1 and SAS Text Miner. Decision analysis and Cost Effectiveness Analysis were performed in TreeAge Pro 2011. A simple comparison of the two procedures using the NIS 2005, a discharge-level data, showed that in general, a lumpectomy surgery is associated with a significantly longer stay and more charges on average. From the MarketScan data, a person-level data where a patient can be followed longitudinally, it was found that for the initial hospitalization, patients who underwent mastectomy had a non-significant longer hospital stay and significantly lower charges. The post-operative number of outpatient services, prescribed medications as well as length of stay and charges for post-operative hospital admissions were not statistically significant. Using the MarketScan data, it was also found that the best model to predict 90-day post-operative hospital admission was logistic regression. A logistic regression revealed that the risk of a hospital re-admission within 90 days after surgery was 65% for a patient who underwent lumpectomy and 48% for a patient who underwent mastectomy. A cost effectiveness analysis using Markov models for up to 100 days after surgery showed that having lumpectomy saved hospital related costs every day with a minimum saving of $33 on day 10. In terms of long-term outcomes, the use of decision analysis methods on the literature review data revealed that, 10-years after surgery, 739 recurrences and 84 deaths were prevented among 10,000 women who had mastectomy instead of lumpectomy. Factoring patients\u27 preferences in the comparison of the two procedures, it was found that patients who undergo lumpectomy are non-significantly more satisfied than their peers who undergo mastectomy. In terms of cost, it was found that lumpectomy saves$517 for each satisfied individual in comparison to mastectomy. In conclusion, the current project showed how to use data mining, decision analysis and cost effectiveness methods to supplement statistical analysis when using real world nonclinical trial data for a more complete analysis. The application of this combination of methods on the comparative effectiveness of lumpectomy and mastectomy showed that in terms of cost and patients\u27 quality of life measured as satisfaction, lumpectomy was found to be the better choice

A Novel Method for Assessing Medication-Related Adverse Outcomes in a Community Hospital

The use of medications for hospitalized patients is universal, and unfortunately medication-related adverse outcomes are common. The accurate assessment of medication-related harm in hospitalized patients is foundational to the development of an effective hospital medication safety program. Every hospital has its own unique fingerprint of harm, accurate determination of the nature of medication-related harm specific to each hospital is necessary to facilitate prevention of that harm with specific and effective interventions. This project has provided a community hospital with its first systematic methodology for assessing medication-related harm. The methodology is adapted from that used in a recent national-level study. Several commonly accepted methods of assessment of medication-related adverse events are in use, but no single method is capable of giving a complete picture of harm at the hospital level. Using a method nearly identical to one employed in large national studies the author examined rates and types of medication-related adverse outcomes in a California community hospital. The hospital had about one-third the national rate of adverse events. An incidental finding was a 4-year pattern of increasing incidence of adverse outcomes followed by 2 years of declining incidence of adverse outcomes. The information gained from the novel assessment method provided a clearer picture of patient harm, a basis for a more effective medication safety plan, and promoted interprofessional collaboration

Data mining and analysis of lung cancer data.

Lung cancer is the leading cause of cancer death in the United States and the world, with more than 1.3 million deaths worldwide per year. However, because of a lack of effective tools to diagnose Lung Cancer, more than half of all cases are diagnosed at an advanced stage, when surgical resection is unlikely to be feasible. The main purpose of this study is to examine the relationship between patient outcomes and conditions of the patients undergoing different treatments for lung cancer and to develop models to predict the mortality of lung cancer. This study will identify the demographic, finance, and clinical factors related to the diagnosis or mortality of Lung Cancer to help physicians and patients in their decision-making. We combined Text Miner and Cluster analysis to identify the claim data for Lung Cancer and to determine the category of diagnosis, treatment procedures and medication treatments for those patients. Moreover, the claims data were used to define severity level and treatment categories. Compared with using diagnosis codes directly, the combination of text mining and cluster analysis is more efficient and captures more useful information for further analysis. In order to analyze the mortality of Lung Cancer, we also found that survival analysis is appropriate to preprocess the data for the relationship between a predictor variable of interest and the time of an event. The proportional hazard model examined the effects of different treatment clusters using a hazard ratio and the proportional effect of a treatment cluster (treatment procedure or medication treatment) may vary with time. A decision tree was built to generate rules for identifying high risk lung cancer cases among the regular inpatient population. Two primary data sets have been used in this study, the Nationwide Inpatient Sample (NIS) and the Thomson MedStat MarketScan data. Kernel density estimation was used for NIS to examine the relationship between Age, Length of stay, Diagnosis Categories, Total Cost and Lung Cancer by visualization. The Kaplan-Meier method and Cox proportional hazard model are used for the Medstat data to discover the relationship between the factors and the target variable for more detail. Time series and predictive modeling are used to predict the total cost for hospital decision making, the mortality of Lung cancer based on the historical data and to generate rules to identify the diagnosis of Lung cancer. Older patients are more likely to have lung cancers that would lead to a higher probability of longer stay and higher costs for the treatment. Within 7 defined clusters of diagnosis for Lung Cancer, the malignant neoplasm of lobe, bronchus or lung is under higher risk. Age, length of stay, admit type, clusters of diagnosis, and clusters of treatment procedures and Major Diagnostic Categories (MDC) were identified as significant factors for the mortality of lung cancer

Beyond HCAHPS: Analysis of patients’ comments provides an expanded view of their hospital experiences

An important concern for health care professionals is that standardized patient surveys may not fully capture all the topics that are important to patients. As a result, health care professionals may not have a complete picture of what their patients experience. The purpose of this research is to utilize a state-of-the-art Natural Language Processing technique to make sense of patients’ solicited, unstructured comments to gain a deeper and broader understanding of their experiences in the hospital. We analyzed a large dataset of inpatient survey responses (48,592 patients generating 65,998 comments) by a patient experience survey vendor for an eleven-hospital health care system in a large Midwest US city. Comments were first analyzed by Top2Vec algorithm in Python and more than 650 groupings of comments were then reduced into 20 sub-domains within 4 topic domains to better understand patient feedback on their hospital experience. We find distinct domains in the textual data that are not completely captured by survey domains. Furthermore, these domains match components of a hierarchical model of health service quality: interpersonal, technical, environmental, and administrative quality. Our findings broaden and deepen understanding of domains on standardized surveys. That is, completely new issues that are not measured in structured surveys are found in patient comments, and even when patient comments can be assigned to specific domains (e.g., nurse communication, discharge, etc.) found in standardized surveys, novel sub-topics provide a more nuanced understanding of patients’ hospital experiences. Novel sub-topics found in patient comments include clinicians’ diagnostic skill, compassionate care, team coordination, transfer processes, roommates, and others. Health care organizations should utilize state-of-the-art methods to mine insights from patient comments, and ensure they have processes, resources, and capabilities needed to translate insights into action. Experience Framework This article is associated with the Policy & Measurement lens of The Beryl Institute Experience Framework. (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens