Implementation of EMR and effects on health care delivery.

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 250-250
Author(s):  
Leonardo Viana Nicacio ◽  
Nat Turner ◽  
Zach Weinberg ◽  
Dorothy Dulko ◽  
Geoffrey Calkins ◽  
...  
Keyword(s):  
Health Care ◽  
Real Time ◽  
Care Delivery ◽  
Healthcare Delivery ◽  
Quality Measures ◽  
Clinical Decision ◽  
Free Text ◽  
Data Systems ◽  
Billing Data ◽  
Colorectal Cancer Patients

250 Background: Electronic health records have potential to enhance health care safety and quality by enabling effective communication, Clinical Decision Support technology, and generation of structured data for analysis. Despite the fact that EHRs have been shown to improve health care processes, the rapid growth of technology used to support medical care has resulted in the creation of enormous volumes of data used to assess and manage healthcare delivery. Valuable data is often siloed in multiple IT systems or captured in free-text clinical notes and non-searchable documents (unstructured data) making it impossible to quickly develop and act on real-time longitudinal data. Methods: Flatiron (FI) operates a "big data" software platform that aggregates, cleans and structures deep clinical oncology data thus facilitating understanding of operational, clinical and financial analytics related to cancer care. The platform proposes to solve these fundamental data challenges, enabling a range of real-time quality measures. It is best understood as three related components: the first is the technical integration between FI and providers' IT and data systems. That integration serves as the plumbing through which data flows securely from the provider to FI. The second is FI’s structured database, which transforms raw clinical, operational, and billing data into clean, structured, longitudinal, real-time patient data. The third is the FI user interface portal, which is a web-based analytics tool that facilitates access to the continually updated structured database. Results: Over 10 institutions have partnered with FI to deploy the platform. The full integration of an institution can be achieved in 12 to 16 weeks with daily updates to the analytics after the initial phase. Over 15,000 patients are expected to be fully integrated in the next 3-6 months. Initial QOPI and other quality measures have been included and customizable dashboards linking charge databases with clinical outcomes have been created. Dashboards with quality metrics will be presented in the conference. Conclusions: Implementation of EMRs alone does not improve healthcare delivery. Data on colorectal cancer patients will be presented at the meeting.

scholarly journals Utility of Natural Language Processing for Clinical Quality Measures Reporting

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Dino P. Rumoro ◽  
Shital C. Shah ◽  
Gillian S. Gibbs ◽  
Marilyn M. Hallock ◽  
Gordon M. Trenholme ◽  
...  
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Medical Center ◽  
Academic Medical Center ◽  
Quality Measures ◽  
Surveillance Program ◽  
Free Text ◽  
Influenza Immunization

ObjectiveTo explain the utility of using an automated syndromic surveillanceprogram with advanced natural language processing (NLP) to improveclinical quality measures reporting for influenza immunization.IntroductionClinical quality measures (CQMs) are tools that help measure andtrack the quality of health care services. Measuring and reportingCQMs helps to ensure that our health care system is deliveringeffective, safe, efficient, patient-centered, equitable, and timely care.The CQM for influenza immunization measures the percentage ofpatients aged 6 months and older seen for a visit between October1 and March 31 who received (or reports previous receipt of) aninfluenza immunization. Centers for Disease Control and Preventionrecommends that everyone 6 months of age and older receive aninfluenza immunization every season, which can reduce influenza-related morbidity and mortality and hospitalizations.MethodsPatients at a large academic medical center who had a visit toan affiliated outpatient clinic during June 1 - 8, 2016 were initiallyidentified using their electronic medical record (EMR). The 2,543patients who were selected did not have documentation of influenzaimmunization in a discrete field of the EMR. All free text notes forthese patients between August 1, 2015 and March 31, 2016 wereretrieved and analyzed using the sophisticated NLP built withinGeographic Utilization of Artificial Intelligence in Real-Timefor Disease Identification and Alert Notification (GUARDIAN)– a syndromic surveillance program – to identify any mention ofinfluenza immunization. The goal was to identify additional cases thatmet the CQM measure for influenza immunization and to distinguishdocumented exceptions. The patients with influenza immunizationmentioned were further categorized by GUARDIAN NLP intoReceived, Recommended, Refused, Allergic, and Unavailable.If more than one category was applicable for a patient, they wereindependently counted in their respective categories. A descriptiveanalysis was conducted, along with manual review of a sample ofcases per each category.ResultsFor the 2,543 patients who did not have influenza immunizationdocumentation in a discrete field of the EMR, a total of 78,642 freetext notes were processed using GUARDIAN. Four hundred fiftythree (17.8%) patients had some mention of influenza immunizationwithin the notes, which could potentially be utilized to meet the CQMinfluenza immunization requirement. Twenty two percent (n=101)of patients mentioned already having received the immunizationwhile 34.7% (n=157) patients refused it during the study time frame.There were 27 patients with the mention of influenza immunization,who could not be differentiated into a specific category. The numberof patients placed into a single category of influenza immunizationwas 351 (77.5%), while 75 (16.6%) were classified into more thanone category. See Table 1.ConclusionsUsing GUARDIAN’s NLP can identify additional patients whomay meet the CQM measure for influenza immunization or whomay be exempt. This tool can be used to improve CQM reportingand improve overall influenza immunization coverage by using it toalert providers. Next steps involve further refinement of influenzaimmunization categories, automating the process of using the NLPto identify and report additional cases, as well as using the NLP forother CQMs.Table 1. Categorization of influenza immunization documentation within freetext notes of 453 patients using NLP

scholarly journals A COMPARATIVE STUDY OF THE PERCEPTIONS OF RURAL DWELLERS TOWARDS HEALTH CARE DELIVERY

2021 ◽  
Vol 9 (04) ◽  
pp. 451-454
Author(s):  
Felix a ◽  
◽  
J. Ugwu ◽  
Clara Okenyi ◽  
◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Delivery ◽  
Significant Relationship ◽  
Survey Design ◽  
Care Delivery ◽  
Healthcare Delivery ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Test Analysis ◽  
Rural Dwellers

The present study aimed to comparatively analyze healthcare delivery perception among rural dwellers based on education (formal/informal) and gender. The study adopted a cross-sectional survey design. A total of two hundred rural dwellers comprising males and females participated in the study. Perception towards health care delivery was measured with a self-developed instrument with demographic information. An independent t-test analysis found no statistically significant relationship between education and perception towards health care delivery. However, the result revealed a significant relationship between gender and perception towards health care delivery. Females were found to show a more positive attitude towards health care delivery than their male counterparts. The findings and conclusions are discussed.

Intensive Care Unit Operational Modeling and Analysis

2011 ◽  
pp. 132-147
Author(s):  
Yue Dong ◽  
Huitian Lu ◽  
Ognjen Gajic ◽  
Brian Pickering
Keyword(s):  
Health Care ◽  
Critical Care ◽  
Health Care Providers ◽  
Systems Engineering ◽  
Care Delivery ◽  
Healthcare Delivery ◽  
Organizational Factors ◽  
Modeling And Analysis ◽  
Novel Approach ◽  
Life Threatening

The outcome of critical illness depends not only on life threatening pathophysiologic disturbances, but also on several complex “system” dimensions: health care providers’ performance, organizational factors, environmental factors, family preferences and the interactions between each component. Systems engineering tools offer a novel approach which can facilitate a “systems understanding” of patient-environment interactions enabling advances in the science of healthcare delivery. Due to the complexity of operations in critical care medicine, certain assumptions are needed in order to understand system behavior. Patient variation and uncertainties underlying these assumptions present a challenge to investigators wishing to model and improve health care delivery processes. In this chapter we present a systems engineering approach to modeling critical care delivery using sepsis resuscitation as an example condition.

Discrete Event Simulation and Real Time Locating Systems

2012 ◽  
Vol 4 (4) ◽  
pp. 16-28
Author(s):  
T. Eugene Day ◽  
Ajit N. Babu ◽  
Steven M. Kymes ◽  
Nathan Ravi
Keyword(s):  
Health Care ◽  
Real Time ◽  
Discrete Event Simulation ◽  
Medical Center ◽  
Discrete Event ◽  
Healthcare Delivery ◽  
The United States ◽  
Careful Consideration ◽  
Health Administration ◽  
Event Simulation

The Veteran’s Health Administration (VHA) is the largest integrated health care system in the United States, forming the arm of the Department of Veterans Affairs (VA) that delivers medical services. From a troubled past, the VHA today is regarded as a model for healthcare transformation. The VA has evaluated and adopted a variety of cutting-edge approaches to foster greater efficiency and effectiveness in healthcare delivery as part of their systems redesign initiative. This paper discusses the integration of two health care analysis platforms: Discrete Event Simulation (DES), and Real Time Locating systems (RTLS) presenting examples of work done at the St. Louis VA Medical Center. Use of RTLS data for generation and validation of DES models is detailed, with prescriptive discussion of methodologies. The authors recommend the careful consideration of these relatively new approaches which show promise in assisting systems redesign initiatives across the health care spectrum.

scholarly journals Contributions on Clinical Decision Support from the 2018 Literature

2019 ◽  
Vol 28 (01) ◽  
pp. 135-137 ◽  
Author(s):  
Vassilis Koutkias ◽  
Jacques Bouaud ◽  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Metastatic Cancer ◽  
Clinical Decision ◽  
Data Driven ◽  
Editorial Team ◽  
Cancer Center ◽  
Bibliographic Databases ◽  
Free Text

Objectives: To summarize recent research and select the best papers published in 2018 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation. Results: Among 1,148 retrieved articles, 15 best paper candidates were selected, the review of which resulted in the selection of four best papers. The first paper introduces a deep learning model for estimating short-term life expectancy (>3 months) of metastatic cancer patients by analyzing free-text clinical notes in electronic medical records, while maintaining the temporal visit sequence. The second paper takes note that CDSSs become routinely integrated in health information systems and compares statistical anomaly detection models to identify CDSS malfunctions which, if remain unnoticed, may have a negative impact on care delivery. The third paper fairly reports on lessons learnt from the development of an oncology CDSS using artificial intelligence techniques and from its assessment in a large US cancer center. The fourth paper implements a preference learning methodology for detecting inconsistencies in clinical practice guidelines and illustrates the applicability of the proposed methodology to antibiotherapy. Conclusions: Three of the four best papers rely on data-driven methods, and one builds on a knowledge-based approach. While there is currently a trend for data-driven decision support, the promising results of such approaches still need to be confirmed by the adoption of these systems and their routine use.

scholarly journals CDS in a Learning Health Care System: Identifying Physicians' Reasons for Rejection of Best-Practice Recommendations in Pneumonia through Computerized Clinical Decision Support

2019 ◽  
Vol 10 (01) ◽  
pp. 001-009 ◽  
Author(s):  
Barbara Jones ◽  
Dave Collingridge ◽  
Caroline Vines ◽  
Herman Post ◽  
John Holmen ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Health Care System ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Clinical Decision ◽  
Additional Patient ◽  
Free Text ◽  
Learning Health Care System ◽  
Care System

Background Local implementation of guidelines for pneumonia care is strongly recommended, but the context of care that affects implementation is poorly understood. In a learning health care system, computerized clinical decision support (CDS) provides an opportunity to both improve and track practice, providing insights into the implementation process. Objectives This article examines physician interactions with a CDS to identify reasons for rejection of guideline recommendations. Methods We implemented a multicenter bedside CDS for the emergency department management of pneumonia that integrated patient data with guideline-based recommendations. We examined the frequency of adoption versus rejection of recommendations for site-of-care and antibiotic selection. We analyzed free-text responses provided by physicians explaining their clinical reasoning for rejection, using concept mapping and thematic analysis. Results Among 1,722 patient episodes, physicians rejected recommendations to send a patient home in 24%, leaving text in 53%; reasons for rejection of the recommendations included additional or alternative diagnoses beyond pneumonia, and comorbidities or signs of physiologic derangement contributing to risk of outpatient failure that were not processed by the CDS. Physicians rejected broad-spectrum antibiotic recommendations in 10%, leaving text in 76%; differences in pathogen risk assessment, additional patient information, concern about antibiotic properties, and admitting physician preferences were given as reasons for rejection. Conclusion While adoption of CDS recommendations for pneumonia was high, physicians rejecting recommendations frequently provided feedback, reporting alternative diagnoses, additional individual patient characteristics, and provider preferences as major reasons for rejection. CDS that collects user feedback is feasible and can contribute to a learning health system.

scholarly journals Management effectiveness and efficiency of healthcare delivery system at ophthalmology outpatient department (OPD) in the Sadar Hospital, Chuadanga, Bangladesh

KYAMC Journal ◽  
2018 ◽  
Vol 9 (2) ◽  
pp. 76-80
Author(s):  
M Idris Ali ◽  
Monira Khatun ◽  
Abdullah Al Mamun ◽  
Md Mofazzal Sharif ◽  
AKM Enamul Haque
Keyword(s):  
Health Care ◽  
Delivery System ◽  
Care Delivery ◽  
Health Care Service ◽  
Healthcare Delivery ◽  
Outpatient Department ◽  
Management Effectiveness ◽  
Hospital District ◽  
Department Of Ophthalmology ◽  
Effectiveness And Efficiency

Background: This present study was carried out in the Outpatient Department of Ophthalmology in Chuadanga Sadar hospital, Bangladesh with the general objective to observe the management effectiveness and efficiency of health service provided in Outpatient Department at Sadar hospital (District level) in Bangladesh and ultimately reveal the need of a managerial personnel in health management other than doctor.Objectives: The objective of the study was to find out management effectiveness, efficiency of health care service at OPD and ultimately reveal need for a managerial personnel for management other than doctor.Materials & Methods: This cross-sectional study was carried out among 450 respondents by using a pre-tested questionnaire over a period of six months.Results: Regarding health care delivery system at outpatient in Bangladesh it was found that before appointment, majority of the respondent (94.44%) were referred to the OPD by local village doctor. After arrival at OPD, majority (29.56%) respondents experienced poor courtesy of the attending personnel. Consultation started between 16-30 minutes after appointment. During consultation with the doctor 66.22% respondents had enough time to consult to a doctor but to some extent. In most (50%) of the cases, consultation time was less than 5 minutes. Most of the respondents (48.88%) were not satisfied with the existing health care. Management effectiveness and efficiency of the existing healthcare service rated as fair (28.44%), poor (24.22%), good (21.57%), very good (13.77%) according to the opinion of the respondents. Ultimately 65.12% respondents sought for a need managerial personnel other than doctor.Conclusion: This study finding concluded need for managerial personnel for hospital management other than doctor himself.KYAMC Journal Vol. 9, No.-2, July 2018, Page 76-80

scholarly journals The Effectiveness of Health Informatics

2010 ◽  
pp. 13-37
Author(s):  
Francesco Paolucci ◽  
Henry Ergas ◽  
Terry Hannan ◽  
Jos Aarts
Keyword(s):  
Health Care ◽  
Health Information ◽  
Information Management ◽  
Health Informatics ◽  
Clinical Decision Making ◽  
Care Delivery ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Long Run ◽  
Information Management Systems

Health care is complex and there are few sectors that can compare to it in complexity and in the need for almost instantaneous information management and access to knowledge resources during clinical decision-making. There is substantial evidence available of the actual, and potential, benefits of e-health tools that use computerized clinical decision support systems (CDSS) as a means for improving health care delivery. CDSS and associated technologies will not only lead to an improvement in health care but will also change the nature of what we call electronic health records (EHR) The technologies that “define” the EHR will change the nature of how we deliver care in the future. Significant challenges relating to the evaluation of these health information management systems relate to demonstrating their ongoing cost-benefit, cost-effectiveness, and effects on the quality of care and patient outcomes. However, health information technology is still mainly about the effectiveness of processes and process outcomes, and the technology is still not mature, which may lead to unintended consequences, but it remains promising and unavoidable in the long run.

Sign in / Sign up

Export Citation Format

Share Document