159 research outputs found
IMPLEMENTATION OF AMERICAN COLLEGE OF CARDIOLOGY / AMERICAN HEART ASSOCIATION CARDIOVASCULAR EVALUATION GUIDELINES FOR PATIENTS HAVING NON-CARDIAC SURGERY
Leeper, Robert S., Implementation of American College of Cardiology/American Hospital Association Cardiovascular Evaluation Guidelines for Patients having Non-cardiac Surgery. Unpublished Doctor of Nursing Practice Scholarly Project, University of Northern Colorado, (2020).
Anesthesia outcomes in non-cardiac surgery are dependent upon recognition of cardiovascular disease, estimating functional capacity, the status of existing co-morbidities, and degree of end-organ disease. Anesthesia providers in a rural surgery center identified an increase in the number of patients coming to the surgery center with unstable cardiovascular conditions resulting in delayed start-times, postponements, and cancellations. The broader objective for this anesthesia quality improvement project was greater patient access, improved quality of life, and safer delivery of anesthesia service. Anesthesia provider’s cardiovascular evaluation methodology was updated by providing education for anesthesia staff including implementation of recommendations and step-wise algorithm in the current American College of Cardiology/American Heart (Fleisher et al., 2014) guidelines. According to the guidelines anesthesia providers can greatly reduce the number of surgical start-time delays or cancellations due to unstable cardiovascular conditions on the day of surgery. Following evidence-based guideline recommendations and cardiac assessment tools anesthesia providers are able to minimize the probability of major adverse cardiac events. Quality anesthesia care is enhanced by pre-operative identification of active cardiac disease, estimation of functional capacity using the Duke Activity Scale Index, and a cardiac risk calculator the Revised Cardiac Risk Index (Lee et al. 1999). The primary objective for this anesthesia quality improvement project was greater patient access, safer anesthesia delivery, and improved quality of life. Donabedian’s (1990) structure-process-outcome model provided the framework for this clinical practice improvement project
From Data to Decision: An Implementation Model for the Use of Evidence-based Medicine, Data Analytics, and Education in Transfusion Medicine Practice
Healthcare in the United States is underperforming despite record increases in spending. The causes are as myriad and complex as the suggested solutions. It is increasingly important to carefully assess the appropriateness and cost-effectiveness of treatments especially the most resource-consuming clinical interventions. Healthcare reimbursement models are evolving from fee-for-service to outcome-based payment. The Patient Protection and Affordable Care Act has added new incentives to address some of the cost, quality, and access issues related to healthcare, making the use of healthcare data and evidence-based decision-making essential strategies. However, despite the great promise of these strategies, the transition to data-driven, evidence-based medical practice is complex and faces many challenges.
This study aims to bridge the gaps that exist between data, knowledge, and practice in a healthcare setting through the use of a comprehensive framework to address the administrative, cultural, clinical, and technical issues that make the implementation and sustainability of an evidence-based program and utilization of healthcare data so challenging. The study focuses on promoting evidence-based medical practice by leveraging a performance management system, targeted education, and data analytics to improve outcomes and control costs.
The framework was implemented and validated in transfusion medicine practice. Transfusion is one of the top ten coded hospital procedures in the United States. Unfortunately, the costs of transfusion are underestimated and the benefits to patients are overestimated. The particular aim of this study was to reduce practice inconsistencies in red blood cell transfusion among hospitalists in a large urban hospital using evidence-based guidelines, a performance management system, recurrent reporting of practice-specific information, focused education, and data analytics in a continuous feedback mechanism to drive appropriate decision-making prior to the decision to transfuse and prior to issuing the blood component.
The research in this dissertation provides the foundation for implementation of an integrated framework that proved to be effective in encouraging evidence-based best practices among hospitalists to improve quality and lower costs of care. What follows is a discussion of the essential components of the framework, the results that were achieved and observations relative to next steps a learning healthcare organization would consider
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Developing Predictive Models for Risk of Postoperative Complications and Hemodynamic Instability in Patients Undergoing Surgery
Patients undergoing high-risk surgeries are often at higher risk of developing hemodynamic instability during surgery resulting in poor postoperative outcomes. This is usually associated with significantly increased postoperative morbidity and mortality, which therefore makes the early identification of these critical events and those patients at risk of postoperative complications crucial. With these motivations in mind, we first created a large deidentified research dataset of surgical case medical records from University of California, Irvine Medical Center (UCIMC) matched with physiological waveforms as well as intermittent vital sign values, lab values, and ventilator settings. To our knowledge, such a dataset does not currently exist for the intraoperative environment. We hope that creating a such a dataset will allow for advances in machine learning for intraoperative care. Using medical data from UCLA, we have developed deep neural network models to classify the risks of postoperative mortality, acute kidney injury, and reintubation utilizing readily available intraoperative information. Our risk scores were compared to currently commonly used risk indices ASA and Surgical Apgar as well as logistic regression. While the deep neural network models performed better than the risk scores and logistic regression, clinicians require additional information to assess what led to increased risk of complications. To address this, we also assessed the use of generalized additive neural networks (GANNs) to create a graphical look at how different features contributed to the risk of in hospital mortality. Finally, we were also interested in predicting critical intraoperative events to allow for time for the clinician to avoid such events. We focused on intraoperative hypotension as it is easier to define and has been shown to lead to increased risk of acute kidney injury, stroke, and myocardial injury. For the hypotension prediction models, we looked at the arterial pressure waveform and EMR data as inputs. Overall, these aims address a gap in current clinical decision guidance and support to reduce adverse events during surgery as well complications after
The Impact of Preoperative Frailty on Surgical Morbidity in Elective Surgery Patients: Opportunity for Intervention?
Minimizing complications after surgery is important for patients and systemically
to minimize cost and health care utilization. Frailty represents physiologic reserve in patients. Designed to assess mortality and resource utilization in elderly populations, a correlation to post-operative complications in surgical patients is now known. Thus, frailty represents a more complete approach for prospective risk assessment. The number of metrics for assessment and varying definitions limit the widespread application of frailty assessment in patients, as does the paucity of data regarding how to intervene. We successfully implemented pre-operative frailty assessment across a healthcare system. We describe the presence of frailty across surgical populations, including all age groups and demonstrate an increase in post-operative morbidity and healthcare utilization for inpatient and outpatient elective surgery populations. A novel approach to improve pre-operative ambulation according to Health Promotions ideology is presented. Finally, future efforts to address frailty pre-operatively are presented
Optimising cardiac services using routinely collected data and discrete event simulation
Background: The current practice of managing hospital resources, including beds, is very much driven by measuring past or expected utilisation of resources. This practice, however, doesn’t reflect variability among patients. Consequently, managers and clinicians cannot make fully informed decisions based upon these measures which are considered inadequate in planning and managing complex systems.
Aim: to analyse how variation related to patient conditions and adverse events affect resource utilisation and operational performance.
Methods: Data pertaining to cardiac patients (cardiothoracic and cardiology, n=2241) were collected from two major hospitals in Oman. Factors influential to resource utilisation were assessed using logistic regressions. Other analysis related to classifying patients based on their resource utilisation was carried out using decision tree to assist in predicting hospital stay. Finally, discrete event simulation modelling was used to evaluate how patient factors and postoperative complications are affecting operational performance.
Results: 26.5% of the patients experienced prolonged Length of Stay (LOS) in intensive care units and 30% in the ward. Patients with prolonged postoperative LOS had 60% of the total patient days. Some of the factors that explained the largest amount of variance in resource use following cardiac procedure included body mass index, type of surgery, Cardiopulmonary Bypass (CPB) use, non-elective surgery, number of complications, blood transfusion, chronic heart failure, and previous angioplasty. Allocating resources based on patient expected LOS has resulted in a reduction of surgery cancellations and waiting times while overall throughput has increased. Complications had a significant effect on perioperative operational performance such as surgery cancellations. The effect was profound when complications occurred in the intensive care unit where a limited capacity was observed. Based on the simulation model, eliminating some complications can enlarge patient population.
Conclusion: Integrating influential factors into resource planning through simulation modelling is an effective way to estimate and manage hospital capacity.Open Acces
The Second International Conference on Health Information Technology Advancement
TABLE OF CONTENTS
I. Message from the Conference Co-Chairs
B. Han and S. Falan …………………………....….……………. 5
II. Message from the Transactions Editor
H. Lee …...………..………….......………….……….………….... 7
III. Referred Papers
A. Emerging Health Information Technology and Applications
The Role of Mobile Technology in Enhancing the Use of Personal Health Records
Mohamed Abouzahra and Joseph Tan………………….……………. 9
Mobile Health Information Technology and Patient Care: Methods, Themes, and Research Gaps
Bahae Samhan, Majid Dadgar, and K. D. Joshi…………..…. 18
A Balanced Perspective to Perioperative Process Management
Jim Ryan, Barbara Doster, Sandra Daily, and Carmen Lewis…..….…………… 30
The Impact of Big Data on the Healthcare Information Systems
Kuo Lane Chen and Huei Lee………….…………… 43
B. Health Care Communication, Literacy, and Patient Care Quality
Digital Illness Narratives: A New Form of Health Communication
Jofen Han and Jo Wiley…..….……..…. 47
Relationships, Caring, and Near Misses: Michael’s Story
Sharie Falan and Bernard Han……………….…..…. 53
What is Your Informatics Skills Level? -- The Reliability of an Informatics Competency Measurement Tool
Xiaomeng Sun and Sharie Falan.….….….….….….…. 61
C. Health Information Standardization and Interoperability
Standardization Needs for Effective Interoperability
Marilyn Skrocki…………………….…….………….… 76
Data Interoperability and Information Security in Healthcare
Reid Berryman, Nathan Yost, Nicholas Dunn, and Christopher Edwards.…. 84
Michigan Health Information Network (MiHIN) Shared Services vs. the HIE Shared Services in Other States
Devon O’Toole, Sean O’Toole, and Logan Steely…..……….…… 94
D. Health information Security and Regulation
A Threat Table Based Approach to Telemedicine Security
John C. Pendergrass, Karen Heart, C. Ranganathan, and V.N. Venkatakrishnan
…. 104
Managing Government Regulatory Requirements for Security and Privacy Using Existing Standard Models
Gregory Schymik and Dan Shoemaker…….…….….….… 112
Challenges of Mobile Healthcare Application Security
Alan Rea………………………….……………. 118
E. Healthcare Management and Administration
Analytical Methods for Planning and Scheduling Daily Work in Inpatient Care Settings:
Opportunities for Research and Practice
Laila Cure….….……………..….….….….… 121
Predictive Modeling in Post-reform Marketplace
Wu-Chyuan Gau, Andrew France, Maria E. Moutinho, Carl D. Smith, and Morgan C. Wang…………...…. 131
A Study on Generic Prescription Substitution Policy as a Cost Containment Approach for Michigan’s Medicaid System
Khandaker Nayeemul Islam…….…...……...………………….… 140
F. Health Information Technology Quality Assessment and Medical Service Delivery
Theoretical, Methodological and Practical Challenges in Designing Formative Evaluations of Personal eHealth Tools
Michael S. Dohan and Joseph Tan……………….……. 150
The Principles of Good Health Care in the U.S. in the 2010s
Andrew Targowski…………………….……. 161
Health Information Technology in American Medicine: A Historical Perspective
Kenneth A. Fisher………………….……. 171
G. Health Information Technology and Medical Practice
Monitoring and Assisting Maternity-Infant Care in Rural Areas (MAMICare)
Juan C. Lavariega, Gustavo Córdova, Lorena G Gómez, Alfonso Avila….… 175
An Empirical Study of Home Healthcare Robots Adoption Using the UTUAT Model
Ahmad Alaiad, Lina Zhou, and Gunes Koru.…………………….….………. 185
HDQM2: Healthcare Data Quality Maturity Model
Javier Mauricio Pinto-Valverde, Miguel Ángel Pérez-Guardado, Lorena Gomez-Martinez, Martha Corrales-Estrada, and Juan Carlos Lavariega-Jarquín.… 199
IV. A List of Reviewers …………………………..…….………………………208
V. WMU – IT Forum 2014 Call for Papers …..…….…………………20
A model not a prophet:Operationalising patient-level prediction using observational data networks
Improving prediction model developement and evaluation processes using observational health data
A model not a prophet:Operationalising patient-level prediction using observational data networks
Improving prediction model developement and evaluation processes using observational health data
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