Sepsis outcomes in solid organ transplant recipients.

BACKGROUND: We present data on a cohort of patients diagnosed with sepsis over a 10-year period comparing outcomes in solid organ transplant (SOT) and non-solid organ transplant (non-SOT) recipients. METHODS: This is a retrospective single-center study of patients with diagnosis of sepsis from 1/1/06 to 6/30/16. Cases and controls were matched by year of sepsis diagnosis with propensity score matching. Conditional logistic regression and repeated measurement models were performed for binary outcomes. Trends over time for in-hospital mortality were determined using the Cochran-Armitage test. A gamma-distributed model was performed on the continuous variables. RESULTS: Overall, there were 18 632 admission encounters with a discharge diagnosis of sepsis in 14 780 unique patients. Of those admissions, 1689 were SOT recipients. After 1:1 matching by year, there were three thousand three hundred and forty patients (1670 cases; 1670 controls) diagnosed with sepsis. There was a decreasing trend for in-hospital mortality for sepsis over time in SOT patients and non-SOT patients (P < .05) due to early sepsis recognition and improved standard of care. Despite higher comorbidities in the SOT group, conditional logistic regression showed that in-hospital mortality for sepsis in SOT patients was similar compared with non-SOT patients (odds ratio [OR] =1.14 [95% confidence interval {CI}, 0.95-1.37], P = .161). However, heart and lung SOT subgroups had higher odds of dying compared with the non-SOT group (OR = 1.83 [95% CI, 1.30-2.57], P < .001 and OR = 1.77 [95% CI, 1.34-2.34], P < .001). On average, SOT patients had 2 days longer hospital length of stay compared with non-SOT admissions (17.00 ± 19.54 vs 15.23 ± 17.07, P < .05). Additionally, SOT patients had higher odds of hospital readmission within 30 days (OR = 1.25 [95% CI, 1.06-1.51], P = .020), and higher odds for DIC compared with non-SOT patients (OR = 1.76 [95% CI, 1.10-2.86], P = .021). CONCLUSION: Sepsis in solid organ transplants and non-solid organ transplant patients have similar mortality; however, the subset of heart and lung transplant recipients with sepsis has a higher rate of mortality compared with the non-solid organ transplant recipients. SOT with sepsis as a group has a higher hospital readmission rate compared with non-transplant sepsis patients.

Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods.

BACKGROUND: A key challenge to mining electronic health records for mammography research is the preponderance of unstructured narrative text, which strikingly limits usable output. The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining. METHODS: The authors searched the enterprise-wide data warehouse at the Houston Methodist Hospital, the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR), for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammogram readings performed between January 2006 and May 2015 and an available pathology report. The authors developed natural language processing (NLP) software algorithms to automatically extract mammographic and pathologic findings from free text mammogram and pathology reports. The correlation between mammographic imaging features and breast cancer subtype was analyzed using one-way analysis of variance and the Fisher exact test. RESULTS: The NLP algorithm was able to obtain key characteristics for 543 patients who met the inclusion criteria. Patients with estrogen receptor-positive tumors were more likely to have spiculated margins (P = .0008), and those with tumors that overexpressed human epidermal growth factor receptor 2 (HER2) were more likely to have heterogeneous and pleomorphic calcifications (P = .0078 and P = .0002, respectively). CONCLUSIONS: Mammographic imaging characteristics, obtained from an automated text search and the extraction of mammogram reports using NLP techniques, correlated with pathologic breast cancer subtype. The results of the current study validate previously reported trends assessed by manual data collection. Furthermore, NLP provides an automated means with which to scale up data extraction and analysis for clinical decision support. Cancer 2017;114-121. © 2016 American Cancer Society.

Early clearance vs persistence of de novo donor-specific antibodies following lung transplantation.

BACKGROUND: The natural history of de novo donor-specific antibodies (dnDSA) after lung transplantation is not well-described. We sought to determine the incidence and risk factors associated with dnDSA and compare outcomes between recipients with transient (or isolated) vs persistent dnDSA after transplantation. METHODS: A single-center review of all lung transplants from 1/2009-7/2013. DSAs were tested eight times in the first year and every 4 months thereafter. Outcomes examined included acute rejection and graft failure. RESULTS: Median follow-up was 18 months (range: 1-61 months), and 24.6% of 333 first-time lung-only transplant recipients developed a dnDSA. Ethnicity, HLA-DQ mismatches, post-transplantation platelet transfusion and Lung Allocation Score >60 were associated with dnDSA (P<.05). Overall graft survival was worse for dnDSA-positive vs negative recipients (P=.025). Of 323 recipients with 1-year follow-up, 72 (22.2%) developed dnDSA, and in 25 (34.7%), the dnDSA was transient and cleared. Recipients with transient dnDSA were less likely to develop acute rejection than those with persistent dnDSA (P=.007). CONCLUSIONS: Early post-lung transplantation, dnDSA occurred in 1/4 of recipients, was associated with peri-transplant risk factors and resulted in decreased survival. Spontaneous clearance of dnDSA, seen in one-third of recipients, was associated with a lower risk of acute rejection.

TP-0184 inhibits FLT3/ACVR1 to overcome FLT3 inhibitor resistance and hinder AML growth synergistically with venetoclax.

We identified activin A receptor type I (ACVR1), a member of the TGF-ß superfamily, as a factor favoring acute myeloid leukemia (AML) growth and a new potential therapeutic target. ACVR1 is overexpressed in FLT3-mutated AML and inhibition of ACVR1 expression sensitized AML cells to FLT3 inhibitors. We developed a novel ACVR1 inhibitor, TP-0184, which selectively caused growth arrest in FLT3-mutated AML cell lines. Molecular docking and in vitro kinase assays revealed that TP-0184 binds to both ACVR1 and FLT3 with high affinity and inhibits FLT3/ACVR1 downstream signaling. Treatment with TP-0184 or in combination with BCL2 inhibitor, venetoclax dramatically inhibited leukemia growth in FLT3-mutated AML cell lines and patient-derived xenograft models in a dose-dependent manner. These findings suggest that ACVR1 is a novel biomarker and plays a role in AML resistance to FLT3 inhibitors and that FLT3/ACVR1 dual inhibitor TP-0184 is a novel potential therapeutic tool for AML with FLT3 mutations.

A comparative efficacy study of diagnostic digital breast tomosynthesis and digital mammography in BI-RADS 4 breast cancer diagnosis.

PURPOSE: Probability of malignancy for BI-RADS 4-designated breast lesions ranges from 2% to 95%, contributing to high false-positive biopsy rates. We compare clinical performance of digital breast tomosynthesis (DBT) versus digital mammography (2D) among our BI-RADS 4 population without prior history of breast cancer. METHODS: We extracted retrospective data i.e., clinical, mammogram reports, and biopsy data, from electronic medical records across Houston Methodist's nine hospitals for patients who underwent diagnostic examinations using both modalities (02/01/2015 - 09/30/2020). 2D and DBT cohorts were not intra-individual matched, and there was no direct mammogram evaluation. Using Student's t test, Fisher's exact test, and Chi-squared test, we evaluated the data to determine statistical significance of differences between modalities in BI-RADS 4 cases. We calculated adjusted odds-ratio between modalities for cancer detection rate (CDR) and biopsy-derived positive predictive value (PPV3). RESULTS: There were 6,356 encounters (6,020 patients) in 2D and 5,896 encounters (5,637 patients) in DBT assessed as BI-RADS 4. Using Fisher's exact test, DBT mammography cases were significantly assessed as BI-RADS 4 5.66% more often than those undergoing 2D mammography, P = 0.0046 (1.0566 95% CI: 1.0169-1.0977). The CDRs were 112.65 (2D) and 120.76 (DBT), adjusted odds-ratio: 1.04 (0.93, 1.16)), P = 0.5029, while PPV3 were 14.41% (2D) and 15.99% (DBT), adjusted odds-ratio: 1.09 (0.97, 1.22), P = 0.1483; both logistic regression-adjusted for all other factors. CONCLUSION: DBT did not achieve better performance and sensitivity in assigning BI-RADS 4 cases compared with 2D, showed no significant advantage in CDR and PPV3, and does not reduce false-positive biopsies among BI-RADS 4-assessed patients.

A Time-Series Feature-Based Recursive Classification Model to Optimize Treatment Strategies for Improving Outcomes and Resource Allocations of COVID-19 Patients.

This paper presents a novel Lasso Logistic Regression model based on feature-based time series data to determine disease severity and when to administer drugs or escalate intervention procedures in patients with coronavirus disease 2019 (COVID-19). Advanced features were extracted from highly enriched and time series vital sign data of hospitalized COVID-19 patients, including oxygen saturation readings, and with a combination of patient demographic and comorbidity information, as inputs into the dynamic feature-based classification model. Such dynamic combinations brought deep insights to guide clinical decision-making of complex COVID-19 cases, including prognosis prediction, timing of drug administration, admission to intensive care units, and application of intervention procedures like ventilation and intubation. The COVID-19 patient classification model was developed utilizing 900 hospitalized COVID-19 patients in a leading multi-hospital system in Texas, United States. By providing mortality prediction based on time-series physiologic data, demographics, and clinical records of individual COVID-19 patients, the dynamic feature-based classification model can be used to improve efficacy of the COVID-19 patient treatment, prioritize medical resources, and reduce casualties. The uniqueness of our model is that it is based on just the first 24 hours of vital sign data such that clinical interventions can be decided early and applied effectively. Such a strategy could be extended to prioritize resource allocations and drug treatment for futurepandemic events.

Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia.

Introduction: Geriatric patients with dementia incur higher healthcare costs and longer hospital stays than other geriatric patients. We aimed to identify risk factors for hospitalization outcomes that could be mitigated early to improve outcomes and impact overall quality of life. Methods: We identified risk factors, that is, demographics, hospital complications, pre-admission, and post-admission risk factors including medical history and comorbidities, affecting hospitalization outcomes determined by hospital stays and discharge dispositions. Over 150 clinical and demographic factors of 15,678 encounters (8407 patients) were retrieved from our institution's data warehouse. We further narrowed them down to twenty factors through feature selection engineering by using analysis of variance (ANOVA) and Glmnet. We developed an explainable machine-learning model to predict hospitalization outcomes among geriatric patients with dementia. Results: Our model is based on stacking ensemble learning and achieved accuracy of 95.6% and area under the curve (AUC) of 0.757. It outperformed prevalent methods of risk assessment for encounters of patients with Alzheimer's disease dementia (ADD) (4993), vascular dementia (VD) (4173), Parkinson's disease with dementia (PDD) (3735), and other unspecified dementias (OUD) (2777). Top identified hospitalization outcome risk factors, mostly from medical history, include encephalopathy, number of medical problems at admission, pressure ulcers, urinary tract infections, falls, admission source, age, race, anemia, etc., with several overlaps in multi-dementia groups. Discussion: Our model identified several predictive factors that can be modified or intervened so that efforts can be made to prevent recurrence or mitigate their adverse effects. Knowledge of the modifiable risk factors would help guide early interventions for patients at high risk for poor hospitalization outcome as defined by hospital stays longer than seven days, undesirable discharge disposition, or both. The interventions include starting specific protocols on modifiable risk factors like encephalopathy, falls, and infections, where non-existent or not routine, to improve hospitalization outcomes of geriatric patients with dementia. Highlights: A total 15,678 encounters of Geriatrics with dementia with a final 20 risk factors.Developed a predictive model for hospitalization outcomes for multi-dementia types.Risk factors for each type were identified including those amenable to interventions.Top factors are encephalopathy, pressure ulcers, urinary tract infection (UTI), falls, and admission source.With accuracy of 95.6%, our ensemble predictive model outperforms other models.

An AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients.

Healthcare regulatory agencies have mandated a reduction in 30-day hospital readmission rates and have targeted COPD as a major contributor to 30-day readmissions. We aimed to develop and validate a simple tool deploying an artificial neural network (ANN) for early identification of COPD patients with high readmission risk. Using COPD patient data from eight hospitals within a large urban hospital system, four variables were identified, weighted and validated. These included the number of in-patient admissions in the previous 6 months, the number of medications administered on the first day, insurance status, and the Rothman Index on hospital day one. An ANN model was trained to provide a predictive algorithm and validated on an additional dataset from a separate time period. The model was implemented in a smartphone app (Re-Admit) incorporating four input risk factors, and a clinical care plan focused on high-risk readmission candidates was then implemented. Subsequent readmission data was analyzed to assess impact. The areas under the curve of receiver operating characteristics predicting readmission with ANN is 0.77, with sensitivity 0.75 and specificity 0.67 on the separate validation data. Readmission rates in the COPD high-risk subgroup after app and clinical intervention implementation saw a significant 48% decline. Our studies show the efficacy of ANN model on predicting readmission risks for COPD patients. The AI enabled Re-Admit smartphone app predicts readmission risk on day one of the patient's admission, allowing for early implementation of medical, hospital, and community resources to optimize and improve clinical care pathways.

Risk Factors for and Frequency of CT Scans, Steroid Use, and Repeat Visits in Inflammatory Bowel Disease Patients Seen at a Single-Center Emergency Department: A Retrospective Cohort Study.

Patients with inflammatory bowel disease often present to the emergency department due to the chronic relapsing nature of the disease. Previous studies have shown younger patients to have an increased frequency of emergency department visits, resulting in repeated exposure to imaging studies and steroids, both of which are associated with risks. We performed a retrospective cohort analysis of inflammatory bowel disease patients seen at Houston Methodist Hospital's emergency department from January 2014 to December 2017 using ICD codes to identify patients with Crohn's disease, ulcerative colitis, or indeterminate colitis from the electronic medical record. Data were collected on demographics, medications, and imaging. Five hundred and fifty-nine patients were randomly selected for inclusion. Older age was associated with decreased risk of CT scan or steroid use. Patients with ulcerative colitis compared to Crohn's had decreased risk of CT scan, while there was an increased risk of CT in patients on a biologic, immunomodulator, or when steroids were given. Steroid use was also more common in those with inflammatory bowel disease as the primary reason for the visit. Patients in our study frequently received steroids and had CT scans performed. The increased risk of CT in those on a biologic, immunomodulator, or steroids suggests more severe disease may contribute. Guidelines are needed to reduce any unnecessary corticosteroid use and limit repeat CT scans in young inflammatory bowel disease patients to decrease the risk of radiation-associated malignancy over their lifetime.

Analysis of the Implementation of Telehealth Visits for Care of Patients With Cancer in Houston During the COVID-19 Pandemic.

PURPOSE: The purpose of this study was to evaluate the use of telemedicine amid the SARS-CoV-2 pandemic in patients with cancer and assess barriers to its implementation. PATIENTS AND METHODS: Telehealth video visits, using the Houston Methodist MyChart platform, were offered to patients with cancer as an alternative to in-person visits. Reasons given by patients who declined to use video visits were documented, and demographic information was collected from all patients. Surveys were used to assess the levels of satisfaction of treating physicians and patients who agreed to video visits. RESULTS: Of 1,762 patients with cancer who were offered telehealth video visits, 1,477 (83.8%) participated. The patients who declined participation were older (67.7 v 60.2 years; P < .0001), lived in significantly lower-income areas (P = .0021), and were less likely to have commercial insurance (P < .0001) than patients who participated. Most participating patients (92.6%) were satisfied with telehealth video visits. A majority of physicians (65.2%) were also satisfied with its use, and 74% indicated that they would likely use telemedicine in the future. Primary concerns that physicians had in using this technology were inadequate patient interactions and acquisition of medical data, increased potential for missing significant clinical findings, decreased quality of care, and potential medical liability. CONCLUSION: Oncology/hematology patients and their physicians expressed high levels of satisfaction with the use of telehealth video visits. Despite recent advances in technology, there are still opportunities to improve the equal implementation of telemedicine for the medical care of vulnerable older, low-income, and underinsured patient populations.

SCOR: A secure international informatics infrastructure to investigate COVID-19.

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.

Preventing inpatient falls with injuries using integrative machine learning prediction: a cohort study.

Patient falls during hospitalization can lead to severe injuries and remain one of the most vexing patient-safety problems facing hospitals. They lead to increased medical care costs, lengthened hospital stays, more litigation, and even death. Existing methods and technology to address this problem mostly focus on stratifying inpatients at risk, without predicting fall severity or injuries. Here, a retrospective cohort study was designed and performed to predict the severity of inpatient falls, based on a machine learning classifier integrating multi-view ensemble learning and model-based missing data imputation method. As input, over two thousand inpatient fall patients' demographic characteristics, diagnoses, procedural data, and bone density measurements were retrieved from the HMH clinical data warehouse from two separate time periods. The predictive classifier developed based on multi-view ensemble learning with missing values (MELMV) outperformed other three baseline models; achieved a cross-validated AUC of 0.713 (95% CI, 0.701-0.725), an AUC of 0.808 (95% CI, 0.740-0.876) on the separate testing set. Our studies show the efficacy of integrative machine-learning based classifier model in dealing with multi-source patient data, which in this case delivers robust predictive performance on the severity of patient falls. The severe fall index provided by the MELMV classifier is calculated to identify inpatients who are at risk of having severe injuries if they fall, thus triggering additional steps of intervention to prevent a harmful fall, beyond the standard-of-care procedure for all high-risk fall patients.

A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer.

PURPOSE: The Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize mammographic reporting to assess cancer risk and facilitate the decision to biopsy. Because of substantial interobserver variability in the application of the BI-RADS lexicon, the decision to biopsy varies greatly and results in overdiagnosis and excessive biopsies. The false-positive rate from mammograms is estimated to be 7% to approximately 10% overall, but within the BI-RADS 4 category, it is greater than 70%. Therefore, we developed the Breast Cancer Risk Calculator (BRISK) to target a well-characterized and specific patient subgroup (BI-RADS 4) rather than a broad heterogeneous group in assessing breast cancer risk. METHODS: BRISK provides a novel precise risk assessment model to reduce overdiagnosis and unnecessary biopsies. It was developed by applying natural language processing and deep learning methods on 5,147 patient records archived in the Houston Methodist systemwide data warehouse from 2006 to May 2015, including imaging and pathology reports, mammographic images, and patient demographics. Key characteristics for BI-RADS 4 patients were collected and computed to output an index measure for biopsy recommendation that is clinically relevant and informative and improves upon the traditional BI-RADS 4 scores. RESULTS: For the validation set, we assessed data from 1,247 BI-RADS 4 patients, including mammographic images and medical reports. The BRISK model sensitivity to predict malignancy was 100%, whereas the specificity was 74%. The total accuracy of our implemented model in BRISK was 81%. Overall area under the curve was 0.93. CONCLUSION: BRISK for abnormal mammogram uses integrative artificial intelligence technology and has demonstrated high sensitivity in the prediction of malignancy. Prospective evaluation is under way and can lead to improvement in patient-physician engagement in making informed decisions with regard to biopsy.

Comparison of Direct Patient Care Costs and Quality Outcomes of the Teaching and Nonteaching Hospitalist Services at a Large Academic Medical Center.

PURPOSE: To compare costs of care and quality outcomes between teaching and nonteaching hospitalist services, while testing the assumption that resident-driven care is more expensive. METHOD: Records of inpatients with the top 20 Medicare Severity Diagnosis-Related Groups admitted to the University Teaching Service (UTS) and nonteaching hospitalist service (NTHS) at Houston Methodist Hospital from 2014-2015 were analyzed retrospectively. Direct costs of care, length of stay (LOS), in-hospital mortality (IHM), 30-day readmission rate (30DRR), and consultant utilization were compared between the UTS and NTHS. Propensity score matching and case mix index (CMI) were used to mitigate differences in baseline characteristics. To compare outcomes between matched groups, the Wilcoxon rank sum test and chi-square test were used. A sensitivity analysis was conducted using multivariable regression analysis. RESULTS: From the overall study population of 8,457 patients, 1,041 UTS and 3,123 NTHS patients were matched. CMI was 1.07 for each group. The UTS had lower direct costs of care per case ($5,028 vs. $5,502, P = .006), lower LOS (4.7 vs. 5.2 days, P = .0002), and lower consultant utilization (1.0 vs. 1.6, P ≤ .0001) versus the NTHS. The UTS and NTHS 30DRR (17.2% vs. 19.3%, P = .110) and IHM (2.9% vs. 3.7%, P = .206) were comparable. The multivariable regression analysis validated the matched data and identified an incremental cost savings of $333/UTS patient. CONCLUSIONS: Patients of an academic hospitalist service had significantly shorter LOS, fewer consultants, and lower direct care costs than comparable patients of a nonteaching service.

A Behavior-Modification, Clinical-Grade Mobile Application to Improve Breast Cancer Survivors' Accountability and Health Outcomes.

PURPOSE: Only 34% of breast cancer survivors engage in the recommended level of physical activity because of a lack of accountability and motivation. Methodist Hospital Cancer Health Application (MOCHA) is a smartphone tool created specifically for self-reinforcement for patients with cancer through the daily accounting of activity and nutrition and direct interaction with clinical dietitians. We hypothesize that use of MOCHA will improve the accountability of breast cancer survivors and help them reach their personalized goals. PATIENTS AND METHODS: Women with stages I to III breast cancer who were at least 6 months post-active treatment with a body mass index (BMI) greater than 25 kg/m2 were enrolled in a 4-week feasibility trial. The primary objective was to demonstrate adherence during weeks 2 and 3 of the 4-week study period (14 days total). The secondary objective was to determine the usability of MOCHA according to the system usability scale. The exploratory objective was to determine weight loss and dietitian-participant interaction. RESULTS: We enrolled 33 breast cancer survivors who had an average BMI of 31.6 kg/m2. Twenty-five survivors completed the study, and the average number of daily uses was approximately 3.5 (range, 0 to 12) times/day; participants lost an average of 2 lbs (+4 lbs to -10.6 lbs). The average score of usability (the second objective) was 77.4, which was greater than the acceptable level. More than 90% of patients found MOCHA easy to navigate, and 84% were motivated to use MOCHA daily. CONCLUSION: This study emphasizes the importance of technology use to improve goal adherence for patients by providing real-time feedback and accountability with the health care team. MOCHA focuses on the engagement of the health care team and is integrated into clinical workflow. Future directions will use MOCHA in a long-term behavior modification study.

Prevalence of in-hospital nonsteroidal antiinflammatory drug exposure in patients with a primary diagnosis of heart failure.

AIM: To determine the prevalence of in-hospital nonsteroidal antiinflammatory drug (NSAID) exposure and associated outcomes in patients admitted with a primary diagnosis of heart failure. METHODS: We performed a propensity-matched cohort analysis of patients admitted to Houston Methodist Hospital System with a primary diagnosis of heart failure according to the International Classification of Diseases-9-Clinical Modification (ICD-9-CM) from January 1, 2011 to December 31, 2014. RESULTS: Of the 9742 patients admitted with a primary diagnosis of heart failure, 384 patients (3.9%) were exposed to NSAID. After applying propensity scores we matched 305 NSAID exposed with 915 unexposed patients. Patients with in-hospital NSAID exposure had a longer length of stay (7.0±8.8 days vs 6.1±8.5; P=.003) and increased prevalence of worsening renal function (34.4% vs 27.9%; P=.030). There were not statically significant differences in in-hospital mortality rate or 30-day all-cause readmission rate. CONCLUSION: Exposure to NSAID in patients admitted with a primary diagnosis of heart failure was low but was associated with adverse outcomes including longer length of stay and higher prevalence or worsening renal function.

METEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine.

GOAL: The aim of this paper is to propose the design and implementation of next-generation enterprise analytics platform developed at the Houston Methodist Hospital (HMH) system to meet the market and regulatory needs of the healthcare industry. METHODS: For this goal, we developed an integrated clinical informatics environment, i.e., Methodist environment for translational enhancement and outcomes research (METEOR). The framework of METEOR consists of two components: the enterprise data warehouse (EDW) and a software intelligence and analytics (SIA) layer for enabling a wide range of clinical decision support systems that can be used directly by outcomes researchers and clinical investigators to facilitate data access for the purposes of hypothesis testing, cohort identification, data mining, risk prediction, and clinical research training. RESULTS: Data and usability analysis were performed on METEOR components as a preliminary evaluation, which successfully demonstrated that METEOR addresses significant niches in the clinical informatics area, and provides a powerful means for data integration and efficient access in supporting clinical and translational research. CONCLUSION: METEOR EDW and informatics applications improved outcomes, enabled coordinated care, and support health analytics and clinical research at HMH. SIGNIFICANCE: The twin pressures of cost containment in the healthcare market and new federal regulations and policies have led to the prioritization of the meaningful use of electronic health records in the United States. EDW and SIA layers on top of EDW are becoming an essential strategic tool to healthcare institutions and integrated delivery networks in order to support evidence-based medicine at the enterprise level.