1. Evidence-Based Medicine; Data Analytics
LIS 4785 Introduction to Health Informatics
Fall 2015 Week 8-1
Instructor: Dr. Sanghee Oh

2. Evidence-Based Medicine

3. Evidence-Based Medicine

4. Evidence-based Medicine
Evidence-based medicine (EBM) is the process of systematically _________, _________ and ___________________ to aid the delivery of optimum clinical care to patients.
“The conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients” (Sackett et al., 1996).
The practice of EBM is the integration of individual clinical expertise with the best available external clinical evidence from systematic research (Sackett et al., 1996).

5. A shift in the culture of health provision away from decisions based on opinion, past practices and precedent, toward a system that better utilize science, research and evidence to guide decision making

6. Benefits
EBM offers the surest and most ______________ to determine and maintain consistently high quality and safety standards in medical practice;
EBM can help _________________________________________;
EBM has the potential to _____________________ significantly.
EBM allows providing more _______ , _________ medical interventions resulting in better health outcomes for patients.
Greater _____________ in the quality of care

7. Clinical Decision Support System (CDSS)
Computer software that presents users with a knowledge base patient-specific data, and related information at the point of care to enhance healthcare provision and management
Clinical guidelin e
Clinical protocol
Organizations’ structure, culture, and reward systems

8. Challenges
___________________of EBM research into everyday practice is a major challenge.
There are substantial gaps between _________________ and __________________________________.
Clinical practice is _______________ than trials.
No time, to competence
__________________ of what works
________________________ are important.

9. EBP Process Steps
ASSESS the patient: Start with the patient -- a clinical problem or question arises from the care of the patient
_________ the question
Construct a well built clinical question derived from the case
_________ the evidence
Select the appropriate resource(s) and conduct a search
Appraise that evidence for its validity (closeness to the truth) and applicability (usefulness in clinical practice)
_________ the evidence by talking with the patient
Return to the patient -- integrate that evidence with clinical expertise, patient preferences and apply it to practice
_________
Evaluate your performance with this patient

10. Types of Clinical Questions
Therapy questions
Diagnosis questions
Prognosis questions
Harm questions

11. Therapy
Questions of treatment in order to achieve some outcome.  May include drugs, surgical intervention, change in diet, counseling, etc.
Diagnosis
Questions of identification of a disorder in a patient presenting with specific symptoms.
Prognosis
Questions of progression of a disease or likelihood of a disease occurring.
Etiology/Harm
Questions of negative impact from an intervention or other exposure.

12. Therapy
“In a population at risk for H1N1 influenza, does a new H1N1 vaccine (versus no vaccine) decrease disease rates?”
Diagnosis
“In patients with a history of recent chest pains, does stress echocardiography versus cardiac catheterization diagnose coronary artery disease?”
“In patients with whom acute appendicitis is suspected, does CT scan, as compared with exploratory laparotomy, diagnose appendicitis?
Prognosis
In patients with coronary heart disease, does an episode of CHF lead to reduced overall quality of life?
Etiology/Harm
“In previously healthy children, does vaccination with MMR vaccine increase the risk of autism?”
“In adults, does cell phone use (versus no cell phone use) increase the risk of brain cancer?”

13. How to ask clinical questions (PICO Model)
PICO clarifies the question and, determines search concepts and type of study that is most appropriate to answer the question type.

14. EBM and HIT
The concept of EBM is a strong commitment to the establishment of robust clinical information systems to help realized EBM commitments.
___________, ___________, and ___________ research-based knowledge in decision making
Developing HIT systems for managing medical knowledge for ___________, ___________, and ___________ the use of evidence
Promoting and facilitating _______________________.

15. Data Analytics

16. Data Analytics _______________ of EHR data
Use (re-use) EHR data for other purposes, such as quality improvement and clinical research.
Analytics
“The ___________ of data, statistical and quantitative analysis, explanatory and predictive models and fact-based management to drive decisions and actions”
“The ____________ of data and applied analytical disciplines (e.g., statistical, contextual, quantitative, predictive, cognitive, other models) to drive fact-based decision making for planning management, measurement and learning.”

17. Terminology ____________________
The area of computer science that aims to build systems and algorithms that learn from data
The processing and modeling of large amounts of data to discover previously unknown patterns or relationships
An applied data mining technique to mostly unstructured textual data

18. What is Big Data and how does it work?

19. 4 Characteristics of Big Data
Big data is described as high in;
Volume: ever-increasing amounts
Velocity: quickly generated
Variety: many different types
Veracity: from trustable sources

24. Clinical Data
Clinical data is a staple resource for most health and medical research. Clinical data is either collected during the course of ongoing patient care or as part of a formal clinical trial program. Clinical data falls into six major types:
Electronic health records
Administrative data
Claims data
Disease registries
Health surveys
Clinical trials data
A number of structured (images, lab results, etc.) and unstructured (e.g., textual notes, clinical narratives, reports, and other types of documents) are generated from a variety of info systems in HIT.

25. The Analytics Pipeline (Process)
Data Sources
_________
_________
_________
Clinical data
Extract
Organize
Normalize
Match
Machine learning
Statistical inference
Descriptive
Predictive
Prescriptive

26. 3 Levels of Data Analytics
________________
Standard types of report that describe current situations and problems
Simulation and modeling techniques that identify trends and portend outcomes of action taken
Optimizing clinical, financial, and other outcomes

27. Predictive Analytics for Healthcare

28. Predictive Analytics
A branch of the advanced analytics which is used to make predictions about unknown future events.
Use many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to ___________________________________________.
Predictive analytics models capture relationships among many factors to ____________________________________.

29. Predictive Data Analysis Process
Predictive analysis applications in healthcare can ___________________________________________ such as diabetes, asthma and other lifetime illnesses.

30. Predictive Analytics for Healthcare
______________________
Diagnostic assistance
Clinical treatment effectiveness
Clinical care intervention
Research for improved disease management
_______________________
Claims management
Fraud detection and prevention
Voice of the patient
Patient discharge and follow-up care
Readmission prevention

31. Challenges to Data Analytics
___________________
Provenance: where the data is originated and how trustworthy it is for large-scale processing and analysis.

32. Healthcare Data Visualization
Healthmap
Contagious Disease Surveillance Tool
Utilize online informal sources for disease outbreak monitoring and real-time surveillance of emerging public health threats
Brings together disparate data sources, including online news aggregators, eyewitness reports, expert-curated discussions and validated official reports, to achieve a unified and comprehensive view of the current global state of infectious diseases and their effect on human and animal health.

33. Institute for Health Metrics and Evaluation
Data Visualization
Policy reports
Research articles
GBD (Global Burden of Diseases) country Profiles
Infographics
Presentations
US County Profiles
Topics
Tools