This document discusses the use of predictive analytics in healthcare. It describes how predictive analytics uses data and statistics to analyze massive amounts of patient information to predict outcomes. This can help with readmissions, triage, emergency care, detecting patient decompensation, and adverse events. Challenges to implementing predictive analytics in healthcare electronically include testing models, oversight, data quality, and ensuring interoperability between systems. When done correctly, predictive analytics has the potential to improve patient health and lower healthcare costs.
Understand what healthcare analytics is.
Identify the 5-stage Analytics Program Lifecycle (APL).
Understand how data analytics can be used in healthcare.
Check it on Experfy: https://www.experfy.com/training/courses/introduction-to-healthcare-analytics.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
This webinar will focus on the technical and practical aspects of creating and deploying predictive analytics. We have seen an emerging need for predictive analytics across clinical, operational, and financial domains. One pitfall we’ve seen with predictive analytics is that while many people with access to free tools can develop predictive models, many organizations fail to provide a sufficient infrastructure in which the models are deployed in a consistent, reliable way and truly embedded into the analytics environment. We will survey techniques that are used to get better predictions at scale. This webinar won’t be an intense mathematical treatment of the latest predictive algorithms, but will rather be a guide for organizations that want to embed predictive analytics into their technical and operational workflows.
Topics will include:
Reducing the time it takes to develop a model
Automating model training and retraining
Feature engineering
Deploying the model in the analytics environment
Deploying the model in the clinical environment
By leveraging Big Data, the healthcare industry has an incredible potential to improve lives. This session will give examples of how data volume, velocity and variety is transforming the “art” of a doctor to the science of care. It will describe how the use of machine learning and massive amount of data will drive the new Consumer Drive healthcare movement.
Understand what healthcare analytics is.
Identify the 5-stage Analytics Program Lifecycle (APL).
Understand how data analytics can be used in healthcare.
Check it on Experfy: https://www.experfy.com/training/courses/introduction-to-healthcare-analytics.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
This webinar will focus on the technical and practical aspects of creating and deploying predictive analytics. We have seen an emerging need for predictive analytics across clinical, operational, and financial domains. One pitfall we’ve seen with predictive analytics is that while many people with access to free tools can develop predictive models, many organizations fail to provide a sufficient infrastructure in which the models are deployed in a consistent, reliable way and truly embedded into the analytics environment. We will survey techniques that are used to get better predictions at scale. This webinar won’t be an intense mathematical treatment of the latest predictive algorithms, but will rather be a guide for organizations that want to embed predictive analytics into their technical and operational workflows.
Topics will include:
Reducing the time it takes to develop a model
Automating model training and retraining
Feature engineering
Deploying the model in the analytics environment
Deploying the model in the clinical environment
By leveraging Big Data, the healthcare industry has an incredible potential to improve lives. This session will give examples of how data volume, velocity and variety is transforming the “art” of a doctor to the science of care. It will describe how the use of machine learning and massive amount of data will drive the new Consumer Drive healthcare movement.
Presentation of Vishal Gulati (Draper Esprit, Venture Partner; Horizon Discovery Group PLC, Board Director) at the Forum of the BioRegion of Catalonia, organized by Biocat.
5 Reasons Why Healthcare Data is Unique and Difficult to MeasureHealth Catalyst
Healthcare data is not linear. It is a complex, diverse beast unlike the data of any other industry. There are five ways in particular that make healthcare data unique:
1. Much of the data is in multiple places.
2. The data is structured and unstructured.
3. It has inconsistent and variable definitions; evidence-based practice and new research is coming out every day. 4. The data is complex.
5. Changing regulatory requirements.
The answer for this unpredictability and complexity is the agility of a late-binding Data Warehouse.
Big Data Analytics for Smart Health CareEshan Bhuiyan
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers.
As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions.
Digital Healthcare Trends: Transformation Towards Better Care RelationshipKumaraguru Veerasamy
Digital health encompasses digital care programs, technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and to make medicine more personalized and precise. With the increasing adoption of telemedicine, wearable devices, mobile health apps (especially during the recent COVID-19 pandemic) and VR/AR; digital health is poised to take healthcare forward.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
My talk in the technical meeting "Global Burden of Diseases and Scientific Computation in Health". 25-26 September 2015. FIOCRUZ, Rio de Janeiro, Brazil
Presentation on Predictive modeling in Health-care at San Jose, Ca 2015. This presentation talks about healthcare industry in US, provides stats and forecasts. It then discusses a few use cases in health care and goes into detail on a kaggle example.
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
Presentation of Vishal Gulati (Draper Esprit, Venture Partner; Horizon Discovery Group PLC, Board Director) at the Forum of the BioRegion of Catalonia, organized by Biocat.
5 Reasons Why Healthcare Data is Unique and Difficult to MeasureHealth Catalyst
Healthcare data is not linear. It is a complex, diverse beast unlike the data of any other industry. There are five ways in particular that make healthcare data unique:
1. Much of the data is in multiple places.
2. The data is structured and unstructured.
3. It has inconsistent and variable definitions; evidence-based practice and new research is coming out every day. 4. The data is complex.
5. Changing regulatory requirements.
The answer for this unpredictability and complexity is the agility of a late-binding Data Warehouse.
Big Data Analytics for Smart Health CareEshan Bhuiyan
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers.
As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions.
Digital Healthcare Trends: Transformation Towards Better Care RelationshipKumaraguru Veerasamy
Digital health encompasses digital care programs, technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and to make medicine more personalized and precise. With the increasing adoption of telemedicine, wearable devices, mobile health apps (especially during the recent COVID-19 pandemic) and VR/AR; digital health is poised to take healthcare forward.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
My talk in the technical meeting "Global Burden of Diseases and Scientific Computation in Health". 25-26 September 2015. FIOCRUZ, Rio de Janeiro, Brazil
Presentation on Predictive modeling in Health-care at San Jose, Ca 2015. This presentation talks about healthcare industry in US, provides stats and forecasts. It then discusses a few use cases in health care and goes into detail on a kaggle example.
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
Real world Evidence and Precision medicine bridging the gapClinosolIndia
Real-world evidence and precision medicine represent complementary forces reshaping the healthcare landscape. The synergy between these realms offers a pathway to more personalized, effective, and patient-centered care. As technology, data analytics, and collaborative initiatives advance, the integration of real-world evidence into precision medicine practices holds the promise of revolutionizing how healthcare is delivered, ensuring that treatments are not only scientifically sound but also tailored to the unique characteristics and experiences of individual patients.
Navigating Healthcare's Seas: Unraveling the Power of Data Mining in HealthcareThe Lifesciences Magazine
Here are 5 Applications of Data Mining in Healthcare: 1. Clinical Decision Support Systems (CDSS) 2. Disease Surveillance and Outbreak Prediction 3. Fraud Detection and Prevention 4. Personalized Medicine 5. Predictive Analytics for Patient Outcomes
Emerging Technologies Shaping the Future of Precision MedicineClinosolIndia
Precision medicine, with its focus on tailoring healthcare interventions to individual characteristics, is undergoing a transformative evolution propelled by emerging technologies. From advanced genomic tools to artificial intelligence, these technologies are revolutionizing our ability to understand, diagnose, and treat diseases at an unprecedented level of specificity. This article explores the key emerging technologies shaping the future of precision medicine.
The Power of Data Analytics in Smart HealthcareWerkDone
Data analytics involves the use of various techniques to analyze and interpret large amounts of data to uncover patterns and insights. In healthcare, data analytics can be used improve the delivery of patient care, predict disease outbreaks, and develop personalized treatment plans.
How to Use Data to Improve Patient Safety: A Two-Part DiscussionHealth Catalyst
As healthcare organizations continue to experience expenses growing faster than revenues, value based care, and consumer transparency of costs and quality, patient safety will be an important determinant of success. This session will describe the sociotechnical attributes of a safe system, the challenges, the barriers and opportunities, and how to use data and your culture of safety as a powerful tool to drive down adverse events.
Attendees will learn:
Why patient safety and quality are important.
How data can help improve patient safety.
The history of patient safety and where we are today.
What components make up a safety analytics culture.
How the internal safety culture directly impacts patient safety metrics.
To describe basic guidelines for improving a safety culture with analytics.
APPLICATION OF DATA SCIENCE IN HEALTHCAREAnnaAntony16
About the application of data science in healthcare. Healthcare is an essential field that touches on people's lives in many ways, and it has been revolutionized by data science over the years. Data science has enabled healthcare providers to better understand patients' needs, identify the root causes of diseases, and design effective treatment plans.
Leveraging Data Analysis for Advancements in Healthcare and Medical Research.pdfSoumodeep Nanee Kundu
Data analysis in healthcare encompasses a wide range of applications, all geared toward improving patient care and well-being. It begins with the collection of diverse healthcare data, which includes electronic health records, medical imaging, genomic data, wearable device data, and more. These data sources provide a rich tapestry of information that can be analysed to unlock valuable insights and drive healthcare advancements.
One of the primary areas where data analysis is a game-changer is in clinical decision-making. Through the utilization of data-driven algorithms, healthcare professionals are empowered to make informed decisions regarding patient diagnosis, treatment plans, and prognosis. Clinical Decision Support Systems (CDSS), powered by data analysis, provide real-time guidance based on evidence-based medical knowledge, assisting physicians in choosing the most appropriate treatments and interventions. This not only enhances patient care but also reduces medical errors and ensures that treatment decisions are aligned with the most current medical research.
Data analysis is also instrumental in early disease identification and monitoring. Machine learning models, for example, can predict the onset of diseases like diabetes, Alzheimer's, and cardiovascular conditions by analysing patient data. This early detection capability enables healthcare providers to intervene proactively, potentially preventing or mitigating the severity of these conditions. This aspect of data analysis significantly contributes to the shift from reactive to proactive healthcare, improving patient outcomes and reducing healthcare costs.
Epidemiology and public health are areas where data analysis plays a vital role. The analysis of healthcare data is essential for tracking and predicting disease outbreaks, which is especially critical in the context of infectious diseases and bioterrorism preparedness. Real-time analysis of health data can offer early warning signs of emerging epidemics, allowing authorities to take timely preventive measures and allocate resources efficiently.
Invited presentation at Presenting Data: How to Convey Information Most Effectively Seminar, Centre of Research Excellence in Patient Safety, School of Public Health and Preventive Medicine, Monash University, February 2015.
CORD Rare Drug Conference, June 8 - 9, 2022
Opportunities and Challenges for Data Management Real-World Data and Real-World Evidence
• Patient support programs: Sandra Anderson, Innomar Strategies
• AI for Data Management and Enhancement: Aaron Leibtag, Pentavere
• Patient Support and RWE: Laurie Lambert, CADTH
In the realm of healthcare, data is a critical asset that holds the potential to revolutionise patient care, enhance treatment outcomes, and streamline healthcare operations. One of the most valuable resources in this data-driven landscape is healthcare datasets. These datasets encompass a wide range of information, from patient medical records and clinical trial data to health insurance claims and public health statistics.
Healthcare datasets serve as the foundation for evidence-based medicine, enabling researchers and healthcare professionals to analyse trends, identify patterns, and make informed decisions. By delving into these datasets, medical researchers can uncover new insights into disease progression, treatment efficacy, and patient outcomes. This knowledge is crucial for developing more effective therapies, improving diagnostic accuracy, and tailoring treatment plans to individual patients' needs.
Moreover, healthcare datasets play a pivotal role in public health initiatives. By examining data on disease incidence, vaccination rates, and health behaviours, public health officials can design targeted interventions, allocate resources more efficiently, and monitor the impact of public health policies. This data-driven approach helps in controlling the spread of infectious diseases, promoting healthy lifestyles, and ultimately reducing the burden of illness on society.
The integration of healthcare datasets with advanced analytics and machine learning technologies opens up even more possibilities. Predictive models built on these datasets can forecast disease outbreaks, identify high-risk patient populations, and optimise resource allocation in healthcare facilities. These predictive insights are invaluable for proactive healthcare management and ensuring that patients receive timely and appropriate care.
However, the effective use of healthcare datasets is not without challenges. Issues related to data privacy, security, and interoperability need to be addressed to ensure that sensitive patient information is protected and that data from different sources can be integrated seamlessly. Additionally, the quality and completeness of data are crucial for drawing accurate conclusions, necessitating rigorous data management and validation practices.
In conclusion, healthcare datasets are a vital resource that holds immense potential for advancing medical research, improving patient care, and enhancing public health outcomes. As technology continues to evolve, the ability to harness the power of these datasets will become increasingly important in shaping the future of healthcare.
Intentional re-challenge and the clinical data management of Drug Related pro...ClinosolIndia
Intentional re-challenge" refers to a deliberate decision to re-administer a drug to a patient who has previously experienced an adverse drug reaction (ADR) or drug-related problem. This is done under controlled circumstances to confirm whether the suspected adverse event was indeed caused by the drug and to assess the reproducibility of the reaction.
In clinical data management, intentional re-challenge involves collecting and analyzing data related to the re-administration of the drug to the patient. This process is often carried out in a clinical trial or controlled clinical setting, and the resulting data play a crucial role in understanding the causality of the adverse event and making informed decisions about the drug's use.
Data Analytics for Population Health Management Strategiesijtsrd
Data analytics plays a pivotal role in population health management, offering strategies to enhance healthcare delivery and outcomes. This review article delves into the multifaceted world of data analytics in the context of population health management. It explores the utilization of health data for risk stratification, predictive modeling, and interventions tailored to the needs of distinct population groups. The article discusses the integration of electronic health records, wearables, and IoT devices to gather comprehensive patient data. Analytical methods, including machine learning and data mining, are examined for their capacity to extract insights from large datasets. The importance of data privacy, security, and ethical considerations in population health management is also addressed. In conclusion, this article underscores the significance of data analytics in optimizing population health management strategies and improving healthcare outcomes. Ravula Sruthi Yadav | Dipiksha Solanki "Data Analytics for Population Health Management: Strategies" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd60104.pdf Paper Url: https://www.ijtsrd.com/pharmacy/pharmacology-/60104/data-analytics-for-population-health-management-strategies/ravula-sruthi-yadav
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CHAPTER 1 SEMESTER V - ROLE OF PEADIATRIC NURSE.pdfSachin Sharma
Pediatric nurses play a vital role in the health and well-being of children. Their responsibilities are wide-ranging, and their objectives can be categorized into several key areas:
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Objective: Provide comprehensive and compassionate care to infants, children, and adolescents in various healthcare settings (hospitals, clinics, etc.).
This includes tasks like:
Monitoring vital signs and physical condition.
Administering medications and treatments.
Performing procedures as directed by doctors.
Assisting with daily living activities (bathing, feeding).
Providing emotional support and pain management.
2. Health Promotion and Education:
Objective: Promote healthy behaviors and educate children, families, and communities about preventive healthcare.
This includes tasks like:
Administering vaccinations.
Providing education on nutrition, hygiene, and development.
Offering breastfeeding and childbirth support.
Counseling families on safety and injury prevention.
3. Collaboration and Advocacy:
Objective: Collaborate effectively with doctors, social workers, therapists, and other healthcare professionals to ensure coordinated care for children.
Objective: Advocate for the rights and best interests of their patients, especially when children cannot speak for themselves.
This includes tasks like:
Communicating effectively with healthcare teams.
Identifying and addressing potential risks to child welfare.
Educating families about their child's condition and treatment options.
4. Professional Development and Research:
Objective: Stay up-to-date on the latest advancements in pediatric healthcare through continuing education and research.
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Attending workshops and conferences on pediatric nursing.
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Antibiotic Stewardship by Anushri Srivastava.pptxAnushriSrivastav
Stewardship is the act of taking good care of something.
Antimicrobial stewardship is a coordinated program that promotes the appropriate use of antimicrobials (including antibiotics), improves patient outcomes, reduces microbial resistance, and decreases the spread of infections caused by multidrug-resistant organisms.
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ACCORDING TO apic.org,
Antimicrobial stewardship is a coordinated program that promotes the appropriate use of antimicrobials (including antibiotics), improves patient outcomes, reduces microbial resistance, and decreases the spread of infections caused by multidrug-resistant organisms.
ACCORDING TO pewtrusts.org,
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VISION
Being proactive
Supporting optimal animal and human health
Exploring ways to reduce overall use of antimicrobials
Using the drugs that prevent and treat disease by killing microscopic organisms in a responsible way
GOAL
to prevent the generation and spread of antimicrobial resistance (AMR). Doing so will preserve the effectiveness of these drugs in animals and humans for years to come.
being to preserve human and animal health and the effectiveness of antimicrobial medications.
to implement a multidisciplinary approach in assembling a stewardship team to include an infectious disease physician, a clinical pharmacist with infectious diseases training, infection preventionist, and a close collaboration with the staff in the clinical microbiology laboratory
to prevent antimicrobial overuse, misuse and abuse.
to minimize the developme
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M Capital Group (“MCG”) predicts that with, against, despite, and even without the global pandemic, the medical technology (MedTech) industry shows signs of continuous healthy growth, driven by smaller, faster, and cheaper devices, growing demand for home-based applications, technological innovation, strategic acquisitions, investments, and SPAC listings. MCG predicts that this should reflects itself in annual growth of over 6%, well beyond 2028.
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There is a movement towards home-based care for the elderly, next generation scanning and MRI devices, wearable technology, artificial intelligence incorporation, and online connectivity. Experts also see a focus on predictive, preventive, personalized, participatory, and precision medicine, with rising levels of integration of home care and technological innovation.
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2. 1
UNCHEALTHCARESYSTEM
The Use of Predictive Analytics in Health Care
• Data Analytics in Health Care
• Types of Data Analytics in Health Care
• Predictive Analytics
• Uses of Predictive Analytics in Health Care
• Implementing Electronic Health Care
Predictive Analytics
Agenda
3. 2
UNCHEALTHCARESYSTEM
Data Analytics in Health Care
In today’s data-driven age, healthcare is transitioning
from opinion-based decisions to informed decisions
based on data and analytics.
Analyzing the data reveals trends and knowledge
that may run contrary to our assumptions causing
a shift in ultimate decisions that in turn will better
serve both patients and healthcare enterprises.
Kolker K. Strata Big Data. 2013: https://www.google.com/?gws_rd=ssl#q=predictive+analytics+in+healthcare+ppt&start=0
5. 4
UNCHEALTHCARESYSTEM
Predictive Analytics
• Predictive analytics uses technology and statistical methods to
search through massive amounts of information, analyzing it to
predict outcomes for individual patients
https://www.healthcatalyst.com/predictive-analytics-healthcare-lessons/
6. 5
UNCHEALTHCARESYSTEM
Uses of Predictive Analytics in Health Care
• Readmissions
• Use an algorithm to predict which patients are likely to be
readmitted to the hospital
• Tailor intervention to individual patient
• Ensure patients actually receive the precise interventions intended
for them
• Monitor specific patients after discharge to find out if having
problems before they decompensate
• Ensure low ratio of patients flagged for intervention to patients who
experience a readmission
Bates D. Health Affairs.2014;33(7):1123-1131
7. 6
UNCHEALTHCARESYSTEM
Uses of Predictive Analytics in Health Care
• Triage
• Estimating risk of complication when patient first presents
to hospital
• Assists in managing staffing and bed resources
• Anticipates need for a transfer to appropriate unit
• Requires detailed guideline that clarifies how algorithm
will inform care
Bates D. Health Affairs.2014;33(7):1123-1131
9. 8
UNCHEALTHCARESYSTEM
Uses of Predictive Analytics in Health Care
• Decompensation
• Often before decompensation there is a period in which
physiological data can be used to determine whether
patient at risk for decompensation
• Host of technologies available to monitor patients who are in ICU,
general care, nursing homes, or at home
Bates D. Health Affairs.2014;33(7):1123-1131
10. 9
UNCHEALTHCARESYSTEM
Uses of Predictive Analytics in Health Care
• Adverse Events
• Predict which patients are at risk of adverse events
• Renal Failure
• Combine analytics with data about exposures to specific medications
and measures of kidney function
• Infection
• Example: Monitoring and interpreting changes in heart rate variability
for detection of major decompensation in infants with low birthweights
before emergence of infection
• Adverse Drug Events
• Predict which patients may suffer an adverse drug event and detect
patients who are in early stages of event
Bates D. Health Affairs.2014;33(7):1123-1131
Suresh S. Pediatr Clin N Amer. 2016;63:357-366
11. 10
UNCHEALTHCARESYSTEM
Implementing Electronic Health Care
Predictive Analytics
• Implementation of electronic health care predictive analytics is
needed to aid in real-time, point-of-care decision making
• Challenges include:
• Testing model in real-world setting under appropriate supervision
• Appropriate oversight of implementation
• Stakeholder engagement
• Appropriate patient privacy and consent policies
• Data quality assurance
• Broad implementation of the model in a health care setting
• Interoperability of health care technology platforms
• Transparency within health care systems
• Long-term challenges
• Medical education and training
• Sustainability
Amarasingham R. Health Affairs.2014;33(7):1148-1154
Cohen I. Health Affairs.2014;33(7):1139-1147
12. 11
UNCHEALTHCARESYSTEM
Conclusions
• Predictive analytics has the potential to use the
power of big data to improve health of patients and
lower cost of health care
• Multiple models exist that will benefit from use of
predictive analytics
• Several challenges need to be overcome to take
advantage of full use of predictive analytics
Bates D. Health Affairs.2014;33(7):1123-1131
