The document discusses advanced analytics and big data in healthcare. It notes that while there is a large amount of healthcare data being generated, less than 10% of organizations are focusing on analytics. It then covers various types of data in healthcare, challenges with data integration and sharing across different systems, and the value of analytics in improving outcomes. It provides examples of using analytics for quality improvement, care coordination, and other areas. Finally, it discusses recommendations and limitations for various stakeholders in utilizing big data and analytics.
Overview of d-Wise technologies, and their core competencies for building clinical systems and healthcare systems. Technology expertise in SAS, entimo, Oracle solutions. Significant thought leadership on implementation of clinical data standards
You almost need to be a super sleuth to decode the acronyms in clinical metadata. Our confidential (not really) dossier of some of the important acronyms in clinical data standards will debrief you on the case.
d-Wise offers an ability to reduce data integration efforts within Pharmaceutical clinical operations significantly with the implementation of SAS's Clinical Data Integration software. Discussion of the Opportunity, the Challenges, Data Quality Challenges, Integration Features, Benefits SDTM Implementation
The Hive Data Virtualization Introduction - Sanjay Krishnamurti, Chief Archit...The Hive
Talk by Sanjay Krishnamurthi, Chief Architect of Informatica at The Hive Panel Discussion "Data Virtualization: Beyond Traditional Data Integration" on May 28, 2013.
Hadoop and Data Virtualization - A Case Study by VHADenodo
Access to full webinar: http://goo.gl/dQjxRe
This webinar by Hortonworks, VHA and Denodo provides information about the functionalities and benefits of Hadoop in Modern Data Architectures; how Hadoop along with data virtualization simplify data management and enable faster data discovery; and what data virtualization can offer in big data projects. VHA explains how they deployed data virtualization and Hadoop together and presents their lessons learned and best practices for data lake and data virtualization deployment.
Access the webinar: http://goo.gl/p08pTz
These slides were presented in a webinar by Denodo in collaboration with BioStorage Technologies and Indiana Clinical and Translational Sciences Institute and Regenstrief Institute.
BioStorage Technologies, Inc., Indiana Clinical and Translational Sciences Institute, and Regenstrief Institute (CTSI) have joined Denodo to talk about the important role of technological advancements, such as data virtualization, in advancing biospecimen research.
By watching this webinar, you can gain insight into best practices around the integration of biospecimen and research data as well as technology solutions that provide consolidated views and rapid conversions of this data into valuable business insights. You will also learn how data virtualization can assist with the integration of data residing in heterogeneous repositories and can securely deliver aggregated data in real-time.
Overview of d-Wise technologies, and their core competencies for building clinical systems and healthcare systems. Technology expertise in SAS, entimo, Oracle solutions. Significant thought leadership on implementation of clinical data standards
You almost need to be a super sleuth to decode the acronyms in clinical metadata. Our confidential (not really) dossier of some of the important acronyms in clinical data standards will debrief you on the case.
d-Wise offers an ability to reduce data integration efforts within Pharmaceutical clinical operations significantly with the implementation of SAS's Clinical Data Integration software. Discussion of the Opportunity, the Challenges, Data Quality Challenges, Integration Features, Benefits SDTM Implementation
The Hive Data Virtualization Introduction - Sanjay Krishnamurti, Chief Archit...The Hive
Talk by Sanjay Krishnamurthi, Chief Architect of Informatica at The Hive Panel Discussion "Data Virtualization: Beyond Traditional Data Integration" on May 28, 2013.
Hadoop and Data Virtualization - A Case Study by VHADenodo
Access to full webinar: http://goo.gl/dQjxRe
This webinar by Hortonworks, VHA and Denodo provides information about the functionalities and benefits of Hadoop in Modern Data Architectures; how Hadoop along with data virtualization simplify data management and enable faster data discovery; and what data virtualization can offer in big data projects. VHA explains how they deployed data virtualization and Hadoop together and presents their lessons learned and best practices for data lake and data virtualization deployment.
Access the webinar: http://goo.gl/p08pTz
These slides were presented in a webinar by Denodo in collaboration with BioStorage Technologies and Indiana Clinical and Translational Sciences Institute and Regenstrief Institute.
BioStorage Technologies, Inc., Indiana Clinical and Translational Sciences Institute, and Regenstrief Institute (CTSI) have joined Denodo to talk about the important role of technological advancements, such as data virtualization, in advancing biospecimen research.
By watching this webinar, you can gain insight into best practices around the integration of biospecimen and research data as well as technology solutions that provide consolidated views and rapid conversions of this data into valuable business insights. You will also learn how data virtualization can assist with the integration of data residing in heterogeneous repositories and can securely deliver aggregated data in real-time.
HIMSS Analytics, with a goal of helping healthcare organizations understand and advance healthcare analytics, has developed the Adoption Model for Analytics Maturity (AMAM) published here on www.SlideShare.net for healthcare industry reference.
This 8 stage international prescriptive analytics oriented maturity model offers an easy assessment and a detailed industry specific road map to help healthcare providers interested in analytics advance their capabilities.
For further information please see www.HIMSSAnalytics.org
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...Aridhia Informatics Ltd
This webinar with our partner Pivotal aired in July 2016.
The increasing sophistication of modern medicine, a seemingly endless supply of data, and the ability to perform large-scale computation is transforming clinical research. However, utilising data to generate new treatments and therapies has continued to prove complicated. The silo-based information systems built over the last 30 years are simply unable to scale to support today’s use cases.
Aridhia, creators of AnalytiXagility, the ground-breaking research and healthcare data analysis platform, is now enabling its customers to rapidly analyse massive amounts of data in meaningful ways to change how diseases are understood, managed and treated. Powered by Pivotal Greenplum, AnalytiXagility is at the forefront of Advanced Clinical Research Information Systems (ACRIS), one of Gartner’s 10 “Transformational Digital Disruptors in Healthcare by 2025”.
Learn how big data and data science are being applied to clinical research and:
• Why research-oriented healthcare delivery organizations and academic medical centers need an ACRIS
• How improving collaboration and productivity accelerates the discovery of insights and increases competiveness
• Why robust data security is critical to modernizing engagement between academia, industry and healthcare
• How to reduce research costs while improving commercialization opportunities
• Why enabling transparent analysis and reproducibility of research are key to scientific progress
• Best practices to get started on your digital transformation and Big Data journey
This prevention is a reflection of my vision on how Big Data impacts healthcare and the efforts that Oracle and VX Healthcare Analytics put into making Big Data work in the patient profiling space
CBIG Event June 20th, 2013. Presentation by Albert Khair. “Emerging Trends in...Subrata Debnath
Join Albert for his presentation which will focus on key emerging trends in Business Intelligence (BI) and Analytics. He will identify ways in which an enterprise can organize capacities for successfully leveraging continually advancing tools and technologies in the Analytics space with the goal of developing and deploying optimal business value in the most effective and efficient manner. Lexmark International achieved operational excellence and order of magnitude efficiencies in reporting performance and user satisfaction by integrating data from various functional silos with disparate BI standards into SAP HANA (High Performance ANalytic Appliance) and then leveraging BusinessObjects BI 4.0 for meeting complex BI analytics, report development, and end-user requirements.
N. Albert Khair is a Business Intelligence, Enterprise Architecture and Data Warehousing expert and has worked in Information Technology (IT) for more than 25 years and is currently employed by Lexmark International headquartered in Lexington, Kentucky. Albert’s work experience within the continental U.S. and abroad spans both public and private sectors, including government, insurance, consulting, airlines and high-tech electronics industries. Albert's functional areas of focus include: Oracle ERP, SAP ERP, SAP NetWeaver, SAP BusinessObjects BI4.0, Supply Chain, Finance, Sales and Distribution, SAP BW, SAP HANA/RDS. Albert has been published in Information Week, a magazine for business and technology managers, and has presented at SAP Insider and ASUG (Americas SAP Users Group) at their national and regional conferences.
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Perficient, Inc.
The average academic research organization (ARO) and hospital has many systems that house patient-related information, such as patient records and genomic data. Combining data from a variety of sources in an ongoing manner can enable complex and meaningful querying, reporting and analysis for the purposes of improving patient safety and care, boosting operational efficiency, and supporting personalized medicine initiatives.
In this webinar, Perficient’s Mike Grossman, a director of clinical data warehousing and analytics, and Martin Sizemore, a healthcare strategist, discussed:
-How AROs and hospitals can benefit from a systematic approach to combining data from diverse systems and utilizing a suite of data extraction, reporting, and analytical tools, in order to support a wide variety of needs and requests
-Examples of proposed solutions to real-life challenges AROs and hospitals often encounter
How to Load Data More Quickly and Accurately into Oracle's Life Sciences Data...Perficient, Inc.
Sponsors and CROs know the value of having a consolidated and regulatory-compliant data warehouse, such as Oracle’s Life Sciences Data Hub (LSH), as well as the importance of consistently loading data into that warehouse quickly and accurately.
However, as data structures from the source files change over time, it can be very time consuming to modify the data structure in the warehouse itself. Additionally, for the large groups of SAS datasets that are typical for a clinical trial, the out-of-the-box load times can be quite long, as the data is loaded one set at a time.
Perficient has the answer. In this webinar, we discussed and demonstrated an autoloader tool that greatly simplifies the data loading process for LSH. We showed how the autoloader can automatically load files, detect metadata changes, upgrade target structures, and load data, all with no human intervention. In addition, we demonstrated how Perficient’s autoloader tool can load multiple datasets in parallel to minimize load times.
A brief tutorial on Big Data and its applications to healthcare. The discussion is centered around technical aspects related to this method of computing rather than concrete examples of its use in medical practice.
It is indeed boom time for Big Data in Healthcare. According to CBE insights, Big Data startups garnered USD 400M in investors funding in first half 2014 as compared to USD133M in the whole of 2013.
Levi Thatcher, Health Catalyst Director of Data Science and his team provide a live demonstration using healthcare.ai to implement a healthcare-specific machine learning model from data source to patient impact. Levi goes through a hands-on coding example while sharing his insights on the value of predictive analytics, the best path towards implementation, and avoiding common pitfalls. Frequently asked questions are answered during the session.
During the webinar, we will:
Describe and install healthcare.ai
Build and evaluate a machine learning model
Deploy interpretable predictions to SQL Server
Discuss the process of deploying into a live analytics environment.
If you’d like to follow along, you should download and install R and RStudio prior to the event. We look forward to you joining us!
In this webinar presentation, d-Wise draws on its deep and unrivaled core expertise enabling life sciences clients to modernize their SAS infrastructure by highlighting key strategies on how to successfully modernize your SAS implementation.
HIMSS Analytics, with a goal of helping healthcare organizations understand and advance healthcare analytics, has developed the Adoption Model for Analytics Maturity (AMAM) published here on www.SlideShare.net for healthcare industry reference.
This 8 stage international prescriptive analytics oriented maturity model offers an easy assessment and a detailed industry specific road map to help healthcare providers interested in analytics advance their capabilities.
For further information please see www.HIMSSAnalytics.org
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...Aridhia Informatics Ltd
This webinar with our partner Pivotal aired in July 2016.
The increasing sophistication of modern medicine, a seemingly endless supply of data, and the ability to perform large-scale computation is transforming clinical research. However, utilising data to generate new treatments and therapies has continued to prove complicated. The silo-based information systems built over the last 30 years are simply unable to scale to support today’s use cases.
Aridhia, creators of AnalytiXagility, the ground-breaking research and healthcare data analysis platform, is now enabling its customers to rapidly analyse massive amounts of data in meaningful ways to change how diseases are understood, managed and treated. Powered by Pivotal Greenplum, AnalytiXagility is at the forefront of Advanced Clinical Research Information Systems (ACRIS), one of Gartner’s 10 “Transformational Digital Disruptors in Healthcare by 2025”.
Learn how big data and data science are being applied to clinical research and:
• Why research-oriented healthcare delivery organizations and academic medical centers need an ACRIS
• How improving collaboration and productivity accelerates the discovery of insights and increases competiveness
• Why robust data security is critical to modernizing engagement between academia, industry and healthcare
• How to reduce research costs while improving commercialization opportunities
• Why enabling transparent analysis and reproducibility of research are key to scientific progress
• Best practices to get started on your digital transformation and Big Data journey
This prevention is a reflection of my vision on how Big Data impacts healthcare and the efforts that Oracle and VX Healthcare Analytics put into making Big Data work in the patient profiling space
CBIG Event June 20th, 2013. Presentation by Albert Khair. “Emerging Trends in...Subrata Debnath
Join Albert for his presentation which will focus on key emerging trends in Business Intelligence (BI) and Analytics. He will identify ways in which an enterprise can organize capacities for successfully leveraging continually advancing tools and technologies in the Analytics space with the goal of developing and deploying optimal business value in the most effective and efficient manner. Lexmark International achieved operational excellence and order of magnitude efficiencies in reporting performance and user satisfaction by integrating data from various functional silos with disparate BI standards into SAP HANA (High Performance ANalytic Appliance) and then leveraging BusinessObjects BI 4.0 for meeting complex BI analytics, report development, and end-user requirements.
N. Albert Khair is a Business Intelligence, Enterprise Architecture and Data Warehousing expert and has worked in Information Technology (IT) for more than 25 years and is currently employed by Lexmark International headquartered in Lexington, Kentucky. Albert’s work experience within the continental U.S. and abroad spans both public and private sectors, including government, insurance, consulting, airlines and high-tech electronics industries. Albert's functional areas of focus include: Oracle ERP, SAP ERP, SAP NetWeaver, SAP BusinessObjects BI4.0, Supply Chain, Finance, Sales and Distribution, SAP BW, SAP HANA/RDS. Albert has been published in Information Week, a magazine for business and technology managers, and has presented at SAP Insider and ASUG (Americas SAP Users Group) at their national and regional conferences.
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Perficient, Inc.
The average academic research organization (ARO) and hospital has many systems that house patient-related information, such as patient records and genomic data. Combining data from a variety of sources in an ongoing manner can enable complex and meaningful querying, reporting and analysis for the purposes of improving patient safety and care, boosting operational efficiency, and supporting personalized medicine initiatives.
In this webinar, Perficient’s Mike Grossman, a director of clinical data warehousing and analytics, and Martin Sizemore, a healthcare strategist, discussed:
-How AROs and hospitals can benefit from a systematic approach to combining data from diverse systems and utilizing a suite of data extraction, reporting, and analytical tools, in order to support a wide variety of needs and requests
-Examples of proposed solutions to real-life challenges AROs and hospitals often encounter
How to Load Data More Quickly and Accurately into Oracle's Life Sciences Data...Perficient, Inc.
Sponsors and CROs know the value of having a consolidated and regulatory-compliant data warehouse, such as Oracle’s Life Sciences Data Hub (LSH), as well as the importance of consistently loading data into that warehouse quickly and accurately.
However, as data structures from the source files change over time, it can be very time consuming to modify the data structure in the warehouse itself. Additionally, for the large groups of SAS datasets that are typical for a clinical trial, the out-of-the-box load times can be quite long, as the data is loaded one set at a time.
Perficient has the answer. In this webinar, we discussed and demonstrated an autoloader tool that greatly simplifies the data loading process for LSH. We showed how the autoloader can automatically load files, detect metadata changes, upgrade target structures, and load data, all with no human intervention. In addition, we demonstrated how Perficient’s autoloader tool can load multiple datasets in parallel to minimize load times.
A brief tutorial on Big Data and its applications to healthcare. The discussion is centered around technical aspects related to this method of computing rather than concrete examples of its use in medical practice.
It is indeed boom time for Big Data in Healthcare. According to CBE insights, Big Data startups garnered USD 400M in investors funding in first half 2014 as compared to USD133M in the whole of 2013.
Levi Thatcher, Health Catalyst Director of Data Science and his team provide a live demonstration using healthcare.ai to implement a healthcare-specific machine learning model from data source to patient impact. Levi goes through a hands-on coding example while sharing his insights on the value of predictive analytics, the best path towards implementation, and avoiding common pitfalls. Frequently asked questions are answered during the session.
During the webinar, we will:
Describe and install healthcare.ai
Build and evaluate a machine learning model
Deploy interpretable predictions to SQL Server
Discuss the process of deploying into a live analytics environment.
If you’d like to follow along, you should download and install R and RStudio prior to the event. We look forward to you joining us!
In this webinar presentation, d-Wise draws on its deep and unrivaled core expertise enabling life sciences clients to modernize their SAS infrastructure by highlighting key strategies on how to successfully modernize your SAS implementation.
Migrating to SAS Grid into a new operating environment is a complex and challenging task. Especially, when it is across platform (different OS level), different OS Bit sizes and across SAS versions. But with careful planning and use of simple toolset it can be managed with little pain. Some of the key challenges and solution listed in this paper can help towards better understanding of things to consider when working on SAS Grid migration. Finally, do not underestimate the training requirement of your team of programmers on expected changes towards transition on to the SAS Grid.
Risk Management Training Slides.
Slides prepared based on "The Healthcare Quality Handbook" by Janet A Brown. Very useful health care quality reference for CPHQ exam preparation. For more slides, contact ckmujeeb@hotmail.com
A hybrid approach to data management is emerging in healthcare as organizations recognize the value of an enterprise data warehouse in combination with a data lake.
In this SlideShare, we discuss data lakes in healthcare and we:
Provide an overview of a Hadoop-based data lake architecture and integration platform, and its application in machine learning, predictive modeling, and data discovery
Discuss several key use cases driving the adoption of data lakes for both providers and health plans
Discuss available data storage forms and the required tools for a data lake environment
Detail best practices for conducting data lake assessments and review key implementation considerations for healthcare
Microsoft: A Waking Giant In Healthcare Analytics and Big DataHealth Catalyst
In 2005, Northwestern Memorial Healthcare embarked upon a strategic Enterprise Data Warehousing (EDW) initiative with the Microsoft technology platform as the foundation. Dale Sanders was CIO at Northwestern and led the development of Northwestern’s Microsoft-based EDW. At that time, Microsoft as an EDW platform was not en vogue and there were many who doubted the success of the Northwestern project. While other organizations were spending millions of dollars and years developing EDW’s and analytics on other platforms, Northwestern achieved great and rapid value at a fraction of the cost of the more typical technology platforms. Now, there are more healthcare data warehouses built around Microsoft products than any other vendor. The risky bet on Microsoft in 2005 paid off.
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
In this context, Dale will talk about:
His up and down journey with Microsoft as an Air Force and healthcare CIO, and why he is now more bullish on Microsoft like never before
A quick review of the Healthcare Analytics Adoption Model and Closed Loop Analytics in healthcare, and how Microsoft products relate to both
The rise of highly specialized, cloud-based analytic services and their value to healthcare organizations’ analytics strategies
Microsoft’s transformation from a closed-system, desktop PC company to an open-system consumer and business infrastructure company
The current transition period of enterprise data warehouses between the decline of relational databases and the rise of non-relational databases, and the new Microsoft products, notably Azure and the Analytic Platform System (APS), that bridge the transition of skills and technology while still integrating with core products like Office, Active Directory, and System Center
Microsoft’s strategy with its PowerX product line, and geospatial analysis and machine learning visualization tools
Microsoft: A Waking Giant in Healthcare Analytics and Big DataDale Sanders
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
While Healthcare 1.0 was broadly defined by a focus on defensive medicine, billing, and fee-for-service, culminating in the mass adoption of EMRs, Healthcare 2.0 is a new wave focused on improving clinical efficiency, quality of care, affordability, and fee-for-value; culminating in a new age of healthcare analytics. This new age of analytics will require a new set of organizational skills and a foundational set of analytic information systems that many executives have not anticipated.
Join Dale Sanders, a 20-year healthcare CIO veteran and the industry's leading analytics expert, as he discusses his lessons learned, best practices in analytics, and what the C-level suite needs to know about this topic, now. Listen to Dale discuss 1) A step-by-step curriculum for analytic adoption and maturity in healthcare organizations, 2) the basic approach to a late-binding data warehouse, 3) pros and cons of early versus late binding, 4) the volatility in vocabulary and business rules in healthcare, 5) how to engineer your data to accommodate volatility in the future
Reviewing the Healthcare Analytics Adoption Model: A Roadmap and Recipe for A...Health Catalyst
Dale Sanders provides an update on the Healthcare Analytics Adoption Model. Dale published the first version of this model in 2002, calling it the Analytics Capability Maturity Model. The three intentions at that time are the same as they are today: 1) Provide healthcare leaders with a clear roadmap for the progression of analytic maturity in their organization. 2) Provide vendors with a roadmap to meet the analytic needs of clients. 3) Create a common framework to benchmark the progressive adoption of analytics at the industry level.
In 2012, Dale co-published a new version of the Model with Dr. Denis Protti, rebranding it the Healthcare Analytics Adoption Model and purposely borrowing from the widespread adoption of the EMR Adoption Model (EMRAM) published and supported by HIMSS. In 2015, Dale transferred the model under a creative commons copyright to HIMSS to create a vendor-independent industry standard that is now widely applied to support the original three intentions. He continues to collaborate with HIMSS to progress the Model.
During this webinar, Dale:
-Reviews the current state of the Health Catalyst Model, including recent changes that advocate a ninth level—direct-to-patient analytics and AI.
-Shares his observations of maturity in the market.
-Provides an update on the current state of the HIMSS Adoption Model for Analytic Maturity.
In this webinar, Dale Sanders will provide a pragmatic, step-by-step, and measurable roadmap for the adoption of analytics in healthcare-- a roadmap that organizations can use to plot their strategy and evaluate vendors; and that vendors can use to develop their products. Attendees will have a chance to learn about:
1) The details of his eight-level model, 2) A brief introduction to the HIMSS/IIA DELTA Model, 3) The importance of permanent organizational teams to sustain improvements from analytic investments, 4) The process of curating and maturing data governance, and 5) The coordination of a data acquisition strategy with payment and reimbursement strategies
Going Beyond the EMR for Data-driven Insights in HealthcarePerficient, Inc.
Join Dr. Marcie Stoshak-Chavez, MD, FACEP, Director of Healthcare Strategic Advisory Services at Perficient and Mr. J.D. Whitlock, Director of Clinical & Business Intelligence at Catholic Health Partners to learn how analytics is being used to measure and monitor performance and provide service-line directors and financial administrators with reporting and analysis that enhances clinical care processes and business operations.
Learn how clinicians and administrators armed with the data-driven insights from the EMR and beyond can:
Derive meaningful insights for care delivery by analyzing clinical, financial and operational data
Collaborate more effectively and improve quality of care by securely sharing insights among providers
Meaningfully measure and understand performance across key Federally mandated measures and take prescribed action
Stay on top of shifts in regulatory policy that impact reimbursements and quality requirements
Healthcare Analytics Adoption Model -- UpdatedHealth Catalyst
The Healthcare Analytics Adoption Model is the result of a collaboration of healthcare industry veterans over the last 15 years. The model borrows lessons learned from the HIMSS EMR Adoption Model, and describes an analogous approach for assessing the adoption of analytics in healthcare.
The Healthcare Analytics Adoption Model provides:
1) A framework for evaluating the industry’s adoption of analytics
2) A roadmap for organizations to measure their own progress toward analytic adoption
3) A framework for evaluating vendor products
This Analytics Adoption Model will enable healthcare organizations to fully understand and leverage the capabilities of analytics and so achieve the ultimate goal that has eluded most provider organizations – that of improving the quality of care while lowering costs and enhancing clinician and patient satisfaction.
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
Defecation
Normal defecation begins with movement in the left colon, moving stool toward the anus. When stool reaches the rectum, the distention causes relaxation of the internal sphincter and an awareness of the need to defecate. At the time of defecation, the external sphincter relaxes, and abdominal muscles contract, increasing intrarectal pressure and forcing the stool out
The Valsalva maneuver exerts pressure to expel faeces through a voluntary contraction of the abdominal muscles while maintaining forced expiration against a closed airway. Patients with cardiovascular disease, glaucoma, increased intracranial pressure, or a new surgical wound are at greater risk for cardiac dysrhythmias and elevated blood pressure with the Valsalva maneuver and need to avoid straining to pass the stool.
Normal defecation is painless, resulting in passage of soft, formed stool
CONSTIPATION
Constipation is a symptom, not a disease. Improper diet, reduced fluid intake, lack of exercise, and certain medications can cause constipation. For example, patients receiving opiates for pain after surgery often require a stool softener or laxative to prevent constipation. The signs of constipation include infrequent bowel movements (less than every 3 days), difficulty passing stools, excessive straining, inability to defecate at will, and hard feaces
IMPACTION
Fecal impaction results from unrelieved constipation. It is a collection of hardened feces wedged in the rectum that a person cannot expel. In cases of severe impaction the mass extends up into the sigmoid colon.
DIARRHEA
Diarrhea is an increase in the number of stools and the passage of liquid, unformed feces. It is associated with disorders affecting digestion, absorption, and secretion in the GI tract. Intestinal contents pass through the small and large intestine too quickly to allow for the usual absorption of fluid and nutrients. Irritation within the colon results in increased mucus secretion. As a result, feces become watery, and the patient is unable to control the urge to defecate. Normally an anal bag is safe and effective in long-term treatment of patients with fecal incontinence at home, in hospice, or in the hospital. Fecal incontinence is expensive and a potentially dangerous condition in terms of contamination and risk of skin ulceration
HEMORRHOIDS
Hemorrhoids are dilated, engorged veins in the lining of the rectum. They are either external or internal.
FLATULENCE
As gas accumulates in the lumen of the intestines, the bowel wall stretches and distends (flatulence). It is a common cause of abdominal fullness, pain, and cramping. Normally intestinal gas escapes through the mouth (belching) or the anus (passing of flatus)
FECAL INCONTINENCE
Fecal incontinence is the inability to control passage of feces and gas from the anus. Incontinence harms a patient’s body image
PREPARATION AND GIVING OF LAXATIVESACCORDING TO POTTER AND PERRY,
An enema is the instillation of a solution into the rectum and sig
Telehealth Psychology Building Trust with Clients.pptxThe Harvest Clinic
Telehealth psychology is a digital approach that offers psychological services and mental health care to clients remotely, using technologies like video conferencing, phone calls, text messaging, and mobile apps for communication.
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:
1. Direct Patient Care:
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.
Objective: Contribute to improving the quality of care for children by participating in research initiatives.
This includes tasks like:
Attending workshops and conferences on pediatric nursing.
Participating in clinical trials related to child health.
Implementing evidence-based practices into their daily routines.
By fulfilling these objectives, pediatric nurses play a crucial role in ensuring the optimal health and well-being of children throughout all stages of their development.
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We understand the unique challenges pickleball players face and are committed to helping you stay healthy and active. In this presentation, we’ll explore the three most common pickleball injuries and provide strategies for prevention and treatment.
Struggling with intense fears that disrupt your life? At Renew Life Hypnosis, we offer specialized hypnosis to overcome fear. Phobias are exaggerated fears, often stemming from past traumas or learned behaviors. Hypnotherapy addresses these deep-seated fears by accessing the subconscious mind, helping you change your reactions to phobic triggers. Our expert therapists guide you into a state of deep relaxation, allowing you to transform your responses and reduce anxiety. Experience increased confidence and freedom from phobias with our personalized approach. Ready to live a fear-free life? Visit us at Renew Life Hypnosis..
CHAPTER 1 SEMESTER V PREVENTIVE-PEDIATRICS.pdfSachin Sharma
This content provides an overview of preventive pediatrics. It defines preventive pediatrics as preventing disease and promoting children's physical, mental, and social well-being to achieve positive health. It discusses antenatal, postnatal, and social preventive pediatrics. It also covers various child health programs like immunization, breastfeeding, ICDS, and the roles of organizations like WHO, UNICEF, and nurses in preventive pediatrics.
R3 Stem Cells and Kidney Repair A New Horizon in Nephrology.pptxR3 Stem Cell
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JR's Lifetime Advanced Analytics
1. Advanced AnalyticsAdvanced Analytics
in Healthcarein Healthcare
Using data and analytics to improve quality and financial
outcomes across the healthcare continuum
2. 2
We’re drowning inWe’re drowning in
data but starving fordata but starving for
knowledge!knowledge!
- unknown author
3. 3
Data in HealthcareData in Healthcare
• 10 X 24th
• Growing at 40% annually
• Largely unstructured – audio dictation,
clinical narratives, personal monitors and
sensors, images, EMRs, email/text, social
media, applications
• 1000’s of EMRs that don’t talk to one another
• Less than 10% of all healthcare organizations
in the U.S. are focusing on analytics
• 60% haven’t even started!
4. • Source data exists in a format
difficult to get at for end
users. Raw data doesn’t add
value for a company looking
to differentiate from it’s
competitors
• Data and Analytics drive end-to-end process
optimization and improve competitiveness
• Value-added descriptive and predictive methods
are used to better understand customers
and drive strategy
• Data is transformed and easily accessed
to provide basic insight into key financial
and operational business drivers
The Value of Data
4
OPERATIONS
Increasing Value
of Data
AND
Increasing Levels
of Competitiveness
Increasing Value
of Data
AND
Increasing Levels
of Competitiveness
5. 5
The Healthcare Landscape
• Movement from a volume-based
to a value-based business model
• PCP and Nursing shortages require
greater efficiency to achieve
patient-centric goals
• Entrenched inefficiencies in care caused by poor
gathering, sharing and use of information
• Pervasiveness of chronic illnesses with patients living
longer
• Patients not fully engaged in their care plans
• Growing complexity throughout the system
6. 6
The Role of Analytics
• Improve clinical outcomes and care
coordination
• Streamline operations and reduce practice
costs
• Create actionable insights from data
• Improved understanding of at-risk
populations
• Targeted marketing
• Manage patients with chronic illness or poor
adherence individually and innovatively
9. 9
McKinsey 2011 (Big Data Study)
• Healthcare is positioned to benefit greatly
from big data as long as barriers to its use
can be overcome
• Each stakeholder group generates huge
pools of data, but they have historically been
unconnected from each other
• Recent technical advances have made it
easier to collect and analyze information from
multiple sources
• Estimated $450B per year in savings in the
health sectors
11. 11
BIG Data (cont.)
• Large pools of data that can be captured,
communicated, aggregated, stored, and analyzed
• Unstructured data that doesn’t fit well into the
relational database structure that we are used to
with an EDW
• Nuances of small populations (e.g. gluten allergies)
can be included in big data algorithms
• Q=f(L, K, Data)
o Along with human capital and hard assets, data
is becoming an ever-greater part of the
production function
13. Polling Question
How important will implementing a Big Data strategy
be for your organization within the next 5 years?
(Vital/Very/Somewhat/Not At All/Not Sure)
13
14. Polling Question
Is anyone currently involved in a Big Data project
within their organization?
(Yes/No)
Less than 10% of companies say
they are involved in Big Data
at the moment
14
15. 15
The Analytic Possibilities Curve
Business Value
TechnicalComplexity
Routine
Reporting &
Monitoring
Trending
Routine Analytics
Data Mining &
Evaluation
Forecasting,
Predictive
Modeling &
Campaigns
16. Advanced Analytic Shortage
“There will be a shortage of talent necessary for
organizations to take advantage of big data. By 2018,
the United States alone could face a shortage of
140,000 to 190,000 people with deep analytical skills as
well as 1.5 million managers and analysts with the
know-how to use the analysis of big data to make
effective decisions”
(McKinsey)
16
18. Data Analytics Blueprint
18
Develop an Enterprise Data Roadmap (EDR)
Identify Data & Process Gaps
Evaluate the Data Infrastructure
Analyze Business User Needs & Capabilities
Identify Analytics Goals
19. Big Data Analysis Techniques – Data Integration
• Data Warehouses (DW) – Combination of data across
transactional systems and other sources with the primary
purpose being to provide analytics and decision support
• Internal Integration in healthcare involves integrating data
for use by your organization
• System-wide integration pulls together data from
participants across the healthcare continuum
• Analytic Data Marts (ADM) – Targeted data solutions
that are more flexible than a DW focusing on a specific
department (e.g. Marketing) or function (improving the
Quality of Care)
• Data Quality/Master Data Management – Systems to
make data more useable and reliable
19
20. Big Data Analysis Techniques – Data Integration
• Generation 3 of the All Payer Claims Database (ACPD3)
brings in population data in the form of benchmarks
• Costs for diabetic patients with CHF and obesity might be
high in absolute value for your plan or ACO, but lower than
benchmark focus care management in other places
• Costs for ER might be lower than budget but high relative to
other networks target intervention programs in this area
• Incorporation of best practices and therapeutic pathways
into the APCD system
• Combine with demographic and lifestyle data in a way that
allows for individualized medicine
20
21. Big Data Analysis Techniques – Data Mining
• Analysis of large quantities of data to extract previously
unknown, interesting patterns in data
• Retrospective
• Cluster analysis involves determining which records are
closely grouped
• Anomaly detection looks for unusual records in the
database
• Association mining attempts to determine where
dependencies occur in the data
21
22. Big Data Analysis Techniques – Predictive
Models• A model or algorithm is developed that specifies the
relationship between an outcome and a set of independent
variables in order to predict what will happen when new
data becomes available
• Regression models describe the linear relationship
between a target variable and a set of predictor variables
• Logistic regression is used when the independent
variable is binary
• Decision trees models involve multiple variable analysis
capability that enables you to go beyond simple one-
cause, one-effect relationships
22
23. Big Data Analysis Techniques – Predictive
Models
• Neural Networks are a
predictive technique that can
recognize and learn patterns in
data
• Simulation models in
healthcare allow for the replication
of reality and exploration of
possible changes and what-if
scenarios
23
24. Big Data Analysis Techniques – Next Generation
• Text, Web and Sentiment Analytics
• 85% of healthcare data is in unstructured formats
• Uses sophisticated linguistic rules and statistical
methods to evaluate text
• Automatically determines keywords and topics,
categorizes content, manages semantic terms, unearths
sentiment and puts things in context
• Visualization supports easy, perceptual inference of
relationships that are otherwise more difficult to induce
through typical tabular or graphically static formats
• Real-Time analytics in healthcare support active
knowledge systems which use patient data to improve
coordination of care and outcomes
24
26. Value of Analytics
• Advanced analytics provides opportunities for
providers, facilities, insurers, and government entities
to improve in the following areas:
Disease Intervention & Prevention
Care Coordination
Customer Service
Financial Risk Management
Fraud & Abuse
Operations
Health Care Reform
26
27. Healthcare Examples
27
Quality of Care
Monitoring refills for
discharged patients
and developing
intervention protocols
Integration of admin
data and EMRs to
predict preventable
conditions/diseases
Identification of
patients at higher risk
for a fall
Coordination of Care
ER docsprepared for
incoming patients with
high severity
Longitudinal treatment
of returning nursing
home
Identification of
patients most likely to
adhere to a care plan
Customer Service
Understanding the
unique drivers of
patient satisfaction for
your office
Technologies and
processes to involve
caregivers
Coordination of
satisfaction measures
for entire episodes of
care
Risk Managemet &
Financial Performance
Targeted marketing
campaigns to reduce
churn or defection
Predictive analytics
improving the ROI of
care management
programs
Risk Adjustment
Operations
Evaluating alternative
clinical pathways to
individualize treatment
Operational KPI
control charts allowing
for faster recognition
and correction of
problems
Standardization of
processes to collect
and share data
Fraud & Abuse
Identification of ER
frequent flyers
Integration of
consumer databases
to identify fraud for
subsidy eligibility
Social network
analysis to target
members and
providers abusing pain
medications
29. Beyond the Frontier
• Health Monitoring – sensors on pills, integrate scales with
provider systems, swiping ID cards at gyms, etc.
• Matching products on the exchange with other preferences for
health-related products (Amazon-style)
• Working with Health Concierge’s and Medical Advocates to
control costs via personalized medicine
• Optimizing operational performance and adopting technology-
enabled process improvements
• True Master Data Management and data quality across the
enterprise – building a system of systems
• Integrating with databases for consumer products (e.g. person
is flagged with a gym membership gets a gift card from Dick’s)
29
30. Case Study – Patient Satisfaction KPI
• Pediatric dental practice had extremely high web
reviews but no internal patient satisfaction data
• Instituted a survey mechanism for parents at
the conclusion of each visit
• First six months 98% of parents provided 4/5-star overall rating
with a 40% responder rate
• Initial efforts focused only on the dissatisfied 2%
• Analytics showed 95% of those who responded (even if
dissatisfied) kept their 6 month follow-up but only 61% of those
who did not respond kept the appointment
• Processes put in place to ‘touch’ non-responders in the time before
the next visit
• 92% of patients now keep their follow-up appointments
30
31. Case Study – Frequent Flyer Analysis
• Predictive modeling can be used to identify members who are
likely to utilize the ER more than three times per calendar year
• Using demographic, medical claims, pharmacy claims, product
and member-specific data we created models to identify
members who are at a higher risk of becoming a frequent flyer
• Three different advanced analytics models were developed
• Concurrent
• Same-year Intervention
• Prospective
• Prospective model correctly predicts roughly 69% of the
Commercial frequent flyers
• Potential claim cost savings to the health plan (ER only) from
successfully intervening on only 10% of true positives estimated
at $1.5M/year
31
32. Case Study – Predicting Type 2 Diabetics
• Predictive modeling used to identify persons who are at
increased risk for Type 2
• Model correctly predicts those who develop Type 2 in the next
time period around 17% of the time in the Commercial (18-64)
population and 19% of the time in the Medicare population
32
33. Polling Question
Rate your organization’s level of Big Data readiness
in the area of Expertise with Big Data techniques
(High, Medium, Low)
33
34. Polling Question
Rate your organization’s level of readiness in the
area of Software/Hardware requirements for Big Data
(High, Medium, Low)
34
35. Polling Question
Rate the skill level in your organization of employees
who will need to be involved with Big Data projects
(High, Medium, Low)
35
36. Polling Question
Rate the volume and quality of the data that is
available to analytic units in your organization to
implement a Big Data project
(High, Medium, Low)
36
37. Recommended Priorities for Payors
• Improving data operations to leverage
existing ‘basic’ analytics more quickly
• Effective data capture, improved data
quality structures and data
governance
• Partnering with providers,
manufacturers and government to
implement monitoring and
intervention programs supported by
analytic frameworks
37
38. Recommended Priorities for Providers
• Standardized and comprehensive data
capture
• Reinforce the culture of information
sharing
• Improving technology around clinical
data
• Improving technology around operations
• Putting data to use in analytics to
improve patient care and patient risk
38
39. Recommended Priorities for Manufacturers
• Focus on payer and customer value by
clearly establishing the true, total cost of
care for a product
• Incorporate output data as a function in all
new products
• Establishing systems to monitor product
efficacy and safety
• Collaboration throughout the entire
healthcare system and with external
partners to increase the rate of
breakthrough scientific discoveries
39
40. Recommended Priorities for Government
• Continue to support the adoption of EMRs
• Support the integration of de-identified payer and
provider data in cloud-based solutions
• Fund researchers to run retrospective clinical trials that
analyze real-world outcomes of highly touted
technologies
• Simplify processes around data for government
programs to ensure program efficiency can be easily
gauged
40
41. Recommended Priorities for Patients/Members
• Look to better understand data and choices regarding
care
• Demand accurate security and storage of electronic
health data and easier mechanisms to self-report
• Understand that your personal health data can benefit
everyone
• Divulge information to providers regarding behavior
and preferences that are not part of a patient record
• Take part in trials and pilots
41
42. Limitations to Big Data & Analytics
• Policy issues around privacy, security and liability in
integrating the data pools across stakeholders
• Time to implement – Lag between the labor and capital
investments and productivity gains
• Investment in IT is NOT big data
• Industry – Payors may gain at the expense of providers
• Cost for providers to implement EMRs
• Shortage of Talent
42
43. Implementing Big Data & Analytics
• Invest in talent & dedicate people to big data
• Have analysts work collaboratively with IT
• Develop cross-functional teams that understand data
• Recognize that data is an engine for growth instead
of a back-office function
• Develop a process-orientation around data and
analytics
• Educate the public. Develop policies that balance the
interests of insurers with public privacy concerns
• Help providers to develop robust data infrastructures
43
Any discussion about analytics in healthcare needs to start with data
Audience hand-raise poll…
Who has heard about Big Data?
One septillion…had to look it up
Google, Amazon, Apple and Facebook are the pioneers of Big Data and already understand the value of it for their business
All this stuff is interwoven into the concept of Big Data
Requires a strategy
The major pools of big data that exist in healthcare today are currently poorly integrated with little or no overlap
Its going to take a lot of work and it’s not easy, but the benefits of integrating this data are obvious and almost untapped
Reporting & Monitoring – EMRs; identifying patients misusing drugs; readmission rates; patient satisfaction
Trending – healthcare businesses seem to worry about year-over-year and lack of long term trending distorts the picture
Routine Analytics – Time Series analysis – New patients over time; Churn; Patients with certain conditions; Office utilization analysis; Brand/generic utilization
Data Mining & Evaluation – Identifying patients with negative drug/drug interactions; evaluation of clinical pathways to determine a best course of action; Identifying patients with potential diseases that have been misdiagnosed/undiagnosed; performance evaluation of integrated care programs; Understanding the determinants of satisfaction and driving KPIs/visual dashboards
Predictive modeling – predicting patients likely to frequent the ER; predicting patients/members likely to leave; risk adjustment; understanding the determinants of patient satisfaction;
Visualization
Sometimes difficult to justify since you don’t see the rewards right away and you need to make a significant investment in time, capital and human resources
80% is really 95% for initial model development
Axiom, unstructured data
Supply of healthcare data is catching up to the demand
Another interesting point is that many healthcare organizations, typically healthcare providers, don’t have data integration strategies and technology in place. In many instances they are supporting data integration requirements using a hodgepodge of primitive technical approaches that don’t provide the ability to change those solutions around changing needs. Worse, they don’t provide the security and the governance subsystems required to remain compliant with the changing regulations.
Cluster Analysis – identification of subpopulations of complex patients (multiple conditions) who may benefit from targeted care management campaigns
Anomaly detection – Finding patients or providers engaging in fraudulent behavior
Association mining – (Discharged, Same-Day Prescription) PCP follow-up correlated with lower readmission rates; (Onset of T2 Diabetes, Socioeconomic=High, Personal Trainer) rapid improvements
All of these modeling techniques really look to provide insight into variables that cause differences across values of the dependent variable
Regression – predicting cost or utilization for a person or population based on a set of health factors; improving rates of central line infections across hospitals
Logistic Regression – Frequent Flyers; Hospital readmissions for certain diseases; Members likely to leave/shop
Decision trees – determining the appropriate clinical pathway for migraine sufferers; determining a person likely to respond to a marketing campaign
Neural Network – determining those likely to adhere to a care plan
• Clinical Simulation: simulation is mainly used to study certain diseases
• Operational Simulation is mainly used for capturing, analyzing, and studying healthcare operations, service
delivery, scheduling, healthcare business processes, and patient flow.
• Educational Simulation is used for training and educational purposes, where virtual environments and virtual and physical
objects are extensively used to depict reality
Neural Network – determining those likely to adhere to a care plan
Simulation-
• Clinical Simulation: simulation is mainly used to study certain diseases
• Operational Simulation is mainly used for capturing, analyzing, and studying healthcare operations, service
delivery, scheduling, healthcare business processes, and patient flow.
• Educational Simulation is used for training and educational purposes, where virtual environments and virtual and physical
objects are extensively used to depict reality
Text/Web Analytics – unstructured data like clinical notes, survey data, social media
It’s not really next generation. It’s here, but just not being used extensively in healthcare
Text/Web Analytics – unstructured data like clinical notes, survey data, social media
Sentiment Analysis – understanding how different people understand/accept a diagnosis
Visualization – Turning rows and columns of data into pictures and graphs that provide for much better holistic understanding to support faster decisions
Real-time – Currently the most prevalent application for real-time health care analytics is within Clinical Decision Support (CDS) software. These programs analyze clinical information at the point of care and support health providers as they make prescriptive decisions. These real-time systems are active knowledge systems, which use two or more items of patient data to generate case-specific advice. Alerts in a pcp office when a patient has been admitted to the ER; real-time concussion data to sideline doctor’s; out of control claims for the week for a payor
You can interpret customers' opinions, improve products, optimize services, streamline processes and make proactive, fact-based decisions.
Exchanges
Understanding and adapting to the individual marketplace faster than the competition
ACOs
Integrating EMRs and patient registries to cost and utilization data to improve care
Commercial Risk Adjustment
Handling the size and complexity of data requirements and streamlining processes for submitting and receiving data
ICD-10
Adjusting to new requirements and analyzing disease information in a more robust way
1/HIPPA, data security breaches, liability; Access to Data (Internal and External barriers). Liability – who is responsible when an inaccurate piece of data has a negative consequence?
2/Dependent on Initial data infrastructure
3/Must invest in IT (Technology, Storage and Computing Power) but must also recognize the need
for IT to work collaboratively with Analytic functions (both learning/developing new skills and competencies)
to capitalize on Big Data
4/Supporting human decision making with analysis
5/Some of this is already happening and if I can think of it, its possible and likely to happen
6/ Example of Amy and Under Armour mouthpieces
Weighted average of active patients booked was around 74%...now it’s 89%! This equated to $82K in extra revenue for the practice
Improving data operations to leverage existing ‘basic’ analytics more quickly
ACOs, episodes of care, leaver/stayer
Isolating outliers – High cost members/groups, providers or facilities that are higher cost/lower quality
Employer Group Reporting
Effective data capture, improved data quality structures and data governance
Defining critical fields that are value drivers for the plan and members
Building clear analytical methods to evaluate expected member value/satisfaction and system performance
Identification of processes that could be made more efficient through big data such as provider authorization, referrals, evaluation of claims accuracy, auto-adjudication of claims
Partnering with providers, manufacturers and government to implement monitoring and intervention programs supported by analytic frameworks
Identifying positive trends such as providers, groups, health conditions and patient types that have much lower than expected costs
Not only looking to maximize risk adjustment revenue, but also identify people/conditions where costs are more avoidable
Creating incentives for best-in-class providers
Working proactively with poorly trending groups or individuals on health and wellness programs and incentives
Standardized and comprehensive data capture
Drive continued adoption of EMR
Develop a strategy to capture data from ‘Smart’ devices and integrate into a holistic view of a patient
Participation in HIEs and data sharing partnerships with private institutions
Reinforce the culture of information sharing
Explain to patients why their information could not only help themselves but others
Simplifying the technical barriers to sharing information with appropriate parties
Improving technology around clinical data
Designing data architecture and governance models to manage and share key clinical data
Eliminating gaps in patient health histories; Longitudinal patient records
Improving technology around operations
Creating decision bodies with joint clinical and IT representation that are responsible for defining and prioritizing key data needs
Putting data to use in analytics to improve patient care and patient risk
Developing informatics talent and predictive analytic capabilities
Incorporating data into decisions and pilot programs that improve the overall quality of care
Focusing on outcomes based protocols that balance cost and quality
1/HIPPA, data security breaches, liability; Access to Data (Internal and External barriers). Liability – who is responsible when an inaccurate piece of data has a negative consequence?
2/Dependent on Initial data infrastructure
3/Must invest in IT (Technology, Storage and Computing Power) but must also recognize the need
for IT to work collaboratively with Analytic functions (both learning/developing new skills and competencies)
to capitalize on Big Data
4/Supporting human decision making with analysis
Move away from the notion that all data needs to be perfect. Advanced analysis and statistics supports strategy. It’s not used for reporting
Develop cross-functional teams that understand data – how it is structured, where it exists, research into emerging sources, legacy system expertise, data quality, etc.