Using Comparative Clinical and Economic Outcome Information to Profile Physician Performance

1995 ◽  
Vol 8 (4) ◽  
pp. 213-220 ◽  
Author(s):  
Michael S. Hendryx ◽  
Douglas S. Wakefield ◽  
James F. Murray ◽  
Tanya Uden-Holman ◽  
Charles M. Helms ◽  
...  
Keyword(s):  
Clinical Performance ◽  
Clinical Information ◽  
Information Management System ◽  
Outcome Data ◽  
Third Party ◽  
Continuous Quality ◽  
Physician Performance ◽  
Outcome Information ◽  
Using Data ◽  
Cost Outcomes

This paper presents strategies and empirical examples of comparative physician profiling under conditions of limited patient sample sizes and varying patient severity. A method by which clinical and cost outcomes may be evaluated simultaneously is also presented. Physician economic and clinical performance are compared using data abstracted from nine hospitals into the MedisGroups clinical information management system for inpatients treated from July, 1990 through June, 1992. The main outcome measures are comparative total and ancillary adjusted charges, and morbidity status. Results suggest that objective comparative outcome data provide useful information to assist in evaluating physician performance. A simultaneous comparison of clinical outcomes and adjusted charges identifies physicians who experience favorable outcomes at lower charges, as well as those who have higher charges and/or poorer outcomes. Strategies outlined in this paper may be of value to clinicians, governing boards, and third party payors. These strategies may be used to assist with privileging and other peer review activities when pursued proactively within a Continuous Quality Improvement framework to improve care.

scholarly journals A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

2021 ◽  
Vol 22 (6) ◽  
pp. 2822
Author(s):  
Efstathios Iason Vlachavas ◽  
Jonas Bohn ◽  
Frank Ückert ◽  
Sylvia Nürnberg
Keyword(s):  
Cancer Research ◽  
Clinical Information ◽  
Disease Diagnosis ◽  
Molecular Data ◽  
Molecular Networks ◽  
Biological Knowledge ◽  
Omics Data ◽  
Translational Cancer Research ◽  
Using Data ◽  
Biological Entities

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

scholarly journals Introduction to the human disease resource search and distribution platform through the Korea Biobank Network portal

2021 ◽  
Vol 64 (1) ◽  
pp. 57-65
Author(s):  
Young Hwan Kim ◽  
Hong Rim Cha ◽  
Ji Eun Lee ◽  
Se Eun Cha ◽  
Yeong Jin Choi
Keyword(s):  
Integrated Management ◽  
Open Data ◽  
Clinical Information ◽  
Information Management System ◽  
Current Status ◽  
Common Data Model ◽  
Open Platform ◽  
Resource Search ◽  
Innovative Capabilities ◽  
Standard Terminology

Human-derived materials are a crucial element of research in the life sciences. The Korea Biobank Network (KBN) portal is a shared open platform that provides the nationʼs most extensive disease resources, possessed by Human Bioresource Unit Banks of the KBN, to the public, including those in the fields of industry, academia, and research. This platform was developed to increase the efficient use of national disease resources. In the KBN portal, the current status of disease resources collected in Korea can be checked online. Human bioresources and clinical information are provided to consumers through systematic search and efficient distribution programs. Additionally, by simultaneously operating the KBN Distribution Support Center, we are working to support the rapid and convenient distribution of human resources in response to the needs of consumers. To effectively utilize the open human bioresource sharing platform, it is necessary to introduce an integrated clinical information management system. Currently, the KBN is in the process of establishing standard terminology for data and applying a common data model for the integrated management of various clinical information held by the KBN. We provide communications through the KBN portal, which is interconnected with the distribution support center, regional biobanks, and consumers. In conclusion, the KBN portal will provide open innovation by creating a business or service model by delivering shared open data and internalizing external innovative capabilities.

scholarly journals The Pan-Cancer Landscape of Prognostic Germline Variants in 10,582 Patients

2019 ◽  
Author(s):  
Ajay Chatrath ◽  
Roza Przanowska ◽  
Shashi Kiran ◽  
Zhangli Su ◽  
Shekhar Saha ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Cox Regression ◽  
Clinical Information ◽  
Tumor Aggressiveness ◽  
Sequencing Data ◽  
Germline Variants ◽  
Outcome Information ◽  
Germline Variation ◽  
Patient Prognosis ◽  
Pan Cancer

AbstractWhile clinical data provides physicians with information about patient prognosis, genomic data can further improve these predictions. We analyzed sequencing data from over 10,000 cancer patients and identified hundreds of prognostic germline variants using multivariate Cox regression models. These variants provide information about patient outcomes beyond clinical information currently in use and may augment clinical decisions based on expected tumor aggressiveness. Molecularly, at least twelve of the germline variants are likely associated with patient outcome through perturbation of protein structure and at least five through association with gene expression differences. About half of these germline variants are in previously reported tumor suppressors or oncogenes, with the other half pointing to loci of previously unstudied genes in the literature that should be further investigated for roles in cancers. Our results suggest that germline variation contributes to tumor progression across most cancers and contains patient outcome information not captured by clinical factors.

scholarly journals Clinical performance of intrauterine device TCu-380 A in Benghazi, Libyan Arab Jamahiriya

2000 ◽  
Vol 6 (5-6) ◽  
pp. 1073-1082
Author(s):  
R. Singh ◽  
M. Al Amari
Keyword(s):  
Clinical Performance ◽  
Intrauterine Device ◽  
Health Profile ◽  
Normal Delivery ◽  
Contraceptive Devices ◽  
Effectiveness Rate ◽  
Intrauterine Contraceptive ◽  
Using Data ◽  
Intrauterine Contraceptive Devices

The study examined the reproductive and health profile of Benghazi women using intrauterine contraceptive devices [IUDs]and evaluated one such device, the TCu-380 A. An historical longitudinal study was carried out using data from the Fertility Regulation Clinic, Keish Polyclinic, Benghazi. The subjects were 457 women registered for TCu-380 A insertion between 1995 and 1998, who had been under follow-up for at least 6 months. The majority were Libyan [87.8%], aged 20-29 years [63.4%], of parity 1-6 [67.1%], non-lactating [64.3%]and with normal delivery at last conception [95.1%]. Half had a chronic disease. The cumulative 36-month follow-up of those using TCu-380 A revealed an effectiveness rate of 99.8%, a continuation rate of 96.1% and complications in 3.5%. TCu-380 A appears to be an effective, durable and safe IUD.

scholarly journals Planning for Hospital IT Implementation: A New Look at the Business Case

2008 ◽  
Vol 1 ◽  
pp. BII.S1035
Author(s):  
Nir Menachemi ◽  
Ebrahim Randeree ◽  
Darrell E. Burke ◽  
Eric W. Ford
Keyword(s):  
Information Technology ◽  
Information Systems ◽  
Clinical Performance ◽  
Clinical Information ◽  
Business Case ◽  
Clinical Information Systems ◽  
It Adoption ◽  
Health It ◽  
Maintenance Costs ◽  
It Implementation

Compared to organizations in other industries, hospitals are slow to adopt information technology (IT). Those planning for system implementation must understand the barriers to IT adoption which, in healthcare, include the relatively high acquisition and maintenance costs of sophisticated administrative and clinical information systems. Understanding the overall business case is particularly important for hospital IT planners. This paper describes the literature that examines benefits from using health IT. In addition, we focus on a series of studies conducted in Florida that provide generalizable evidence regarding the overall business case associated with hospital adoption for information systems. These studies focus broadly on the improved financial, operational, and clinical performance associated with IT.

Control Charts in Healthcare Quality Improvement

2012 ◽  
Vol 51 (03) ◽  
pp. 189-198 ◽  
Author(s):  
S. N. van der Veer ◽  
K. J. Jager ◽  
N. Peek ◽  
N. F. de Keizer ◽  
A. Koetsier
Keyword(s):  
Quality Improvement ◽  
Control Charts ◽  
Clinical Information ◽  
Outcome Data ◽  
Skewed Distribution ◽  
Direct Patient Care ◽  
Shewhart Control Charts ◽  
Pdsa Cycle ◽  
Shewhart Control ◽  
Two Phases

SummaryObjectives: Use of Shewhart control charts in quality improvement (QI) initiatives is increasing. These charts are typically used in one or more phases of the Plan Do Study Act (PDSA) cycle to monitor summaries of process and outcome data, abstracted from clinical information systems, over time. We summarize methodological criteria of Shewhart control charts and investigate adherence of published QI studies to these criteria.Methods: We searched Medline, Embase and CINAHL for studies using Shewhart control charts in QI processes in direct patient care. We extracted methodological criteria for Shewhart control charts, and for the use of these charts in PDSA cycles, from textbooks and methodological literature.Results: We included 34 studies, presenting 64 control charts of which 40 control charts plotted two phases of the PDSA cycle. The criterion to use 10–35 data points in a control chart was least adhered to (48.4% non-adherence). Other criteria were: transformation of the data in case of a skewed distribution (43.7% non adherence), when comparing data from two phases of the PDSA cycle the Plan phase (the first phase) needs to be stable (40.0% non-adherence), using a maximum of four different rules to detect special cause variation (14.1% non-adherence), and setting control limits at three standard deviations from the mean (all control charts adhered).Conclusion: There is room for improvement with regard to the methodological construction of Shewhart control charts used in QI processes. Higher adherence to all methodological criteria will decrease the risk of incorrect conclusions about the process being monitored.

scholarly journals P52 A baseline review of the activity of the PICU pharmacists using electronically captured data

2020 ◽  
Vol 105 (9) ◽  
pp. e34.1-e34
Author(s):  
Diarmaid Semple ◽  
Erika Brereton ◽  
Ian Dawkins
Keyword(s):  
Information Quality ◽  
Good Practice ◽  
Clinical Information ◽  
Information Management System ◽  
List Type ◽  
Care Providers ◽  
Medication Error Reporting ◽  
Medicines Reconciliation ◽  
Health And Social Care ◽  
American Medical Informatics Association

AimTo date there are no metrics for the clinical pharmacist service to PICU. It is accepted that use of a Clinical Information Management System (CIMS) has a role in medication safety,1 however there are few studies that review the information potential of a CIMS for collecting pharmacist activity.2MethodAdditional fields and custom reports were configured in the CIMS to enable PICU pharmacists to record their activity in the following areas:Medicines reconciliation within 72 hours of admission to PICUDischarge kardex reviewAnalgesia and sedation (A&S) reviewClinical pharmacy reviewOther interventions & medication error reporting continued as per normal practice. Data was analysed using Microsoft Excel®.ResultsComplete data was available from July 2017 to end of 2018.There were 1274 medicines reconciliations by a pharmacist within 72 hours of admission (78% admissions). 14% of discharge kardexes had been reviewed prior to discharge to the ward. There was an average of 190 pharmacy reviews per 100 bed days. A total of 780 Pharmacist A&S Plans were documented by the clinical pharmacists – an average of 2 per working day, and 48% of admissions.Comparisons between each six month period showed a significant increase in the number of pharmacists medicines reconciliations (p<0.001). No other differences were found.ConclusionThis study has shown that electronic tracking of pharmacist ward activity is possible. It has the potential to demonstrate compliance with external or internal standards and audits. This data continues to be collected, and therefore these results will be used as a baseline to compare future activity. The findings of this study may encourage other units to replicate, providing data that can be used for comparison. Further configuration of the CIMS to capture other metrics such as TDM, and document discrepancies in medicines reconciliation is planned.ReferencesForni A, Chu H, Fanikos J. Technology Utilization to Prevent Medication Errors. Current Drug Safety. 2010;5:13–18.Nelson S, Poikonen J, Reese T, El Halta D, Weir C. The pharmacist and the EHR. Journal of the American Medical Informatics Association. 2016;24:193–197.Health and Information Quality Authority. Guidance for health and social care providers; Principles of good practice in medication reconciliation. Dublin: HIQA; 2014

scholarly journals Design and Analysis of the Third-Party Logisitics Management System Based on J2EE

2012 ◽  
Vol 6-7 ◽  
pp. 491-495
Author(s):  
Li Xia Hao ◽  
Feng Qing Zhao ◽  
Shu Hong Jia ◽  
Chao Hua Ma
Keyword(s):  
Resource Sharing ◽  
Management System ◽  
Information Management System ◽  
Third Party ◽  
Third Party Logistics ◽  
The Third Party Logistics ◽  
Analysis And Design ◽  
The Third ◽  
System Data ◽  
The Third Party

The analysis and design ideas of third party logistics management information systems were introduced based on the research for a company in Beijing, China. With distribution requirements planning ( DRP ) system, data sharing can be realized effectively and are transparent to the existing system with scalability. The third-party logistics information management system developed based on this proposal has been put into operation, and solved the resource sharing problem inside a logistics company.

scholarly journals Medical Informatics Training at Columbia University and the Columbia-Presbyterian Medical Center

1995 ◽  
Vol 04 (01) ◽  
pp. 125-129
Author(s):  
B. A. Allen ◽  
P. D. Clayton ◽  
J. J. Cimino
Keyword(s):  
Medical Informatics ◽  
Medical Center ◽  
Clinical Information ◽  
Information Management System ◽  
Advanced Technology ◽  
Columbia University ◽  
Postdoctoral Training ◽  
Unified Medical Language System ◽  
Part Time Faculty ◽  
Part Time

Abstract:The Department of Medical Informatics at Columbia University College of Physicians and Surgeons consists of a faculty of 17 full-and part-time faculty. The Department faculty collaborate with the Department of Computer Science and several clinical departments of the medical center. We offer courses in medical informatics, formal degrees (M.A., M.Phil. and Ph.D.) and a postdoctoral training program. In addition to academic offerings, the close affiliation with the Columbia-Presbyterian Medical Center and the primary responsibilities for clinical information systems offers trainees unique opportunities to work with and develop real-world applications. Faculty research programs include work on the Integrated Advanced Information Management System (IAIMS), Unified Medical Language System (UMLS), High-Perfor-mance Computing and Communications (HPCC), Electronic Medical Records, automated decision support and technology transfer through the Center for Advanced Technology.

Pygenprop: a Python library for programmatic exploration and comparison of organism genome properties

2019 ◽  
Vol 35 (23) ◽  
pp. 5063-5065
Author(s):  
Lee H Bergstrand ◽  
Josh D Neufeld ◽  
Andrew C Doxey
Keyword(s):  
Data Science ◽  
Source Code ◽  
Open Reading Frames ◽  
Third Party ◽  
Partial Support ◽  
Biochemical Pathways ◽  
Bioinformatics Software ◽  
Using Data ◽  
Genome Analyses ◽  
Reading Frames

Abstract Summary A critical step in comparative genomics is the identification of differences in the presence/absence of encoded biochemical pathways among organisms. Our library, Pygenprop, facilitates these comparisons using data from the Genome Properties database. Pygenprop is written in Python and, unlike existing libraries, it is compatible with a variety of tools in the Python data science ecosystem, such as Jupyter Notebooks for interactive analyses and scikit-learn for machine learning. Pygenprop assigns YES, NO, or PARTIAL support for each property based on InterProScan annotations of open reading frames from an organism’s genome. The library contains classes for representing the Genome Properties database as a whole and methods for detecting differences in property assignments between organisms. As the Genome Properties database grows, we anticipate widespread adoption of Pygenprop for routine genome analyses and integration within third-party bioinformatics software. Availability and implementation Pygenprop is written in Python and is compatible with versions 3.6 or higher. Source code is available under Apache Licence Version 2 at https://github.com/Micromeda/pygenprop. The package can be installed from both PyPi (https://pypi.org/project/pygenprop) and Anaconda (https://anaconda.org/lbergstrand/pygenprop). Documentation is available on Read the Docs (http://pygenprop.rtfd.io/).

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