Clinical and Translational
Science Institute / CTSI
at the University of California, San Francisco
Informatics Technologie...
• Where is the wisdom we have lost in
knowledge?
• Where is the knowledge we have lost in
information?
• Where is the info...
What is Data, Information, Knowledge?
MRN: 000-00-00-0
9.8
Data-Information-Knowledge
MRN: 000-00-00-0
HgbA1c 9.8%
Data-Information-Knowledge
MRN: 000-00-00-0
HgbA1c 9.8%
(normal 4.3% to 5.6%)
Data + Context = Information
• Data -- discrete, atomic, raw observations
with no inherent structure
• Information – data ...
Knowledge
• Data -- discrete, atomistic, raw observations
with no inherent structure
• Information – data related to other...
Knowledge Through Hypothesis Testing
• Data supports or refutes hypotheses
• Hypotheses in translational medicine
– T1: ab...
Hypotheses-Centric View of EHR Data
• Observational studies
– more prone to confounding and other biases
– informatics nee...
Observational vs. Interventional
• In 2013, 84% of UCSF
studies were non-
interventional
• What's the right
balance?
– are...
Informatics for Interventional Research
• Traditional trials, parallel to clinical care
– OnCore interoperation with APEX
...
Functional View of POC E-Research Requests
• APEX:
– programmatically sending patient-specific messages to targeted
clinic...
DGIM Survey Choices
(n=16/28 researchers)
APEX Requests Votes for
"Top 5"
Votes for
"Top 1"
Integrate existing risk calcul...
Next Steps
• Build on data management foundation to
increase hypothesis-centric use of informatics
at UCSF
– make point-of...
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UCSF Informatics Day 2014 - Ida Sim, "Informatics Technologies: From a Data-Centric View to a Hypothesis-Centric View"

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  • Don't have to go to too many informatics conferences for this quote to show up from TS Eliot's The Rock.
    Eliot, of course, died before the era of Big Data. If he were alive today, this might have been his next line.
    Because, after all, we're not interested in Data for Data's sake. We're interested in generating information, and knowledge to advance health worldwide.
  • What's the difference between Data Information and Knowledge?
    Here's some data for you. Not too useful is it.
  • Here's some metadata, or data about data. For many of you, this is much more meaningful. If you aren't a clinician, this isn't necessarily any more informative is it.
  • Now let me tell you that A1c reflects your average level of blood sugar control and that the normal range is 4.3 to 5.6. This means this patient has poorly controlled diabetes. Is this still just data? or is this now information? How did data become information?
  • It's data with context that becomes information. Data is discrete, atomic, raw observations with no inherent structure – 9.8.
    Information is data related to other data (A1c and a normal range) in an interpretive context that is meaningful for a particular task, e.g., normal range is meaningful for clinical care charge is meaningful for payment capture
    This means that the same data and metadata may be information to some users and not to others.
  • Knowledge is quite different. Knowledge is generalizable statements about the world that allow you to make predictions about individuals. Knowledge is the basis for action.
  • We advance knowledge by testing hypotheses, using data to support or refute them.
    Very broadly speaking, there are 2 classes of hypotheses in translational medicine: T1 questions about mechanisms of disease and Keith spoke about that with the Precision Medicine initiative.
    I'd like to focus on clinical research, both within traditional health care settings, and beyond the hospital and clinic
  • First thing to note is the difference between observational and interventional approaches. Observational studies, of course, are more prone to confounding and other biases.
    In a Big Data world, many data scientists from finance, insurance, consumer goods, are flocking to health data. We can expect a flood of observational findings from them, as even as they are just learning epi/biostats, and just learning how to work with domain experts in health to deal with confounding and other biases.
    This means that interventional trials will be at a premium to verify observational findings. Almost paradoxically, then, even though Big Data makes it easier for us to do observational research, that isn't our greatest added value. For places like UCSF that have access not just to data but also to patients that we provide care for, our value will increasingly be to tell the wheat from the chaff through interventional studies,
  • But in 2013, 84% of clinical research studies at UCSF were observational. Only 16% were trials.
    Is this the right balance? How many of you do observational research? If it were feasible to do an interventional design, how many of you would rather do that?
  • We need to make interventional trials easier, cheaper, and faster. You heard Sorena talk about interoperating OnCore with APEX for the traditional clinical trial.
    UCSF partnered with Pfizer on an innovative all virtual incontinence trial, where patients were entirely .
    And the newest frontier will be to run interventional trials embedded into APEX, with POC randomization, …
    We should note that T2/T3 research seeks to guide good care, such that care and QI and research are on a spectrum. The informatics we need for good care and good research are often the same.
  • So here are some functionalities that we might want for facilitating care or research in APEX. Which of these is the one you want most? I'll give you a little time to pick your Top 1, and then we'll have a show of hands.
    Let's have a show of hands:
  • Here are results from a survey that Alka Kanaya ran among 28 researchers in General Internal Medicine, with a 57% response rate. You can see that integrating risk calculators was the most wanted. Recruiting study subjects was the second most popular choice for "Most Wanted."
    The other top choice of 2 people was launching external websites from within APEX, but that enthusiasm was not shared by other DGIM colleagues.
    I'm sure different divisions/departments will have different needs and we need to assess them.
  • So Next Steps.
    Over the last several years, UCSF has invested heavily in a data-centric view, the great work you're hearing about today. We need to build now on this data management foundation to increase hypothesis-centric use of informatics at UCSF. We need to make point-of-care and other interventional research easier, faster, cheaper.
    Our next CTSI proposal, due next year, will emphasize e-research infrastructure, for interventional, observational and community-based research.
    We need your input! This Informatics Day is a start and we look forward to continuing the conversation with you. Thank you.
  • UCSF Informatics Day 2014 - Ida Sim, "Informatics Technologies: From a Data-Centric View to a Hypothesis-Centric View"

    1. 1. Clinical and Translational Science Institute / CTSI at the University of California, San Francisco Informatics Technologies: From a Data- Centric View to a Hypothesis-Centric View Ida Sim, MD, PhD Prof Medicine, Co-Director of Biomedical Informatics, CTSI
    2. 2. • Where is the wisdom we have lost in knowledge? • Where is the knowledge we have lost in information? • Where is the information we have lost in Big Data? The Rock, T.S. Eliot. 1934
    3. 3. What is Data, Information, Knowledge? MRN: 000-00-00-0 9.8
    4. 4. Data-Information-Knowledge MRN: 000-00-00-0 HgbA1c 9.8%
    5. 5. Data-Information-Knowledge MRN: 000-00-00-0 HgbA1c 9.8% (normal 4.3% to 5.6%)
    6. 6. Data + Context = Information • Data -- discrete, atomic, raw observations with no inherent structure • Information – data related to other data in an interpretive context that is meaningful for a particular task, e.g., - normal range is meaningful for clinical care - charge is meaningful for payment capture
    7. 7. Knowledge • Data -- discrete, atomistic, raw observations with no inherent structure • Information – data related to other data in an interpretive context that is meaningful for a particular task • Knowledge – generalizable statements that allow you to make predictions about individuals - e.g., people with diabetes are at higher risk of cardiovascular disease
    8. 8. Knowledge Through Hypothesis Testing • Data supports or refutes hypotheses • Hypotheses in translational medicine – T1: about mechanisms of disease – T2/T3 clinical research: treatment, diagnosis, quality of care • within traditional health care settings • beyond hospital and clinic
    9. 9. Hypotheses-Centric View of EHR Data • Observational studies – more prone to confounding and other biases – informatics needs include:1 • clean data and metadata • accurate cohort selection and comparison (see Research Browser) • Expect a Big Data flood of observational findings – many non-health data scientists flocking to health data – need to learn epi/biostats and domain experts to deal with confounding, etc. • Interventional trials will be at a premium to verify observational findings
    10. 10. Observational vs. Interventional • In 2013, 84% of UCSF studies were non- interventional • What's the right balance? – are PIs doing observational research for the right reasons? UCSF 2013 Clinical Research Studies Total = 1,380
    11. 11. Informatics for Interventional Research • Traditional trials, parallel to clinical care – OnCore interoperation with APEX • Virtual, community-based trials – e.g., recruited & screened via web, lab testing in their community, run-in phase with e-diaries, informed consent via web, study medication shipped directly to participants1 • Point-of-care (POC) research – e.g., POC randomization2 , alerts, patient support and care coordination, capturing self-reports via MyChart/sensors, etc – care, QI, and research are on a spectrum3 1 Orri, et al. Contemp Clin Trials 38 (2014) 190–197; 2 Fiore, L. et al. Clin Trials, 2011 8:183-95; 3 Pletcher, et al. JAMA Int Med 2014 174:668-670.
    12. 12. Functional View of POC E-Research Requests • APEX: – programmatically sending patient-specific messages to targeted clinicians via their Inbox – integration of existing risk calculators into APEX (with automated uploading of clinical/lab data) – launching external websites from within APEX – offering structured note templates within APEX (e.g., PHQ-9) – introducing reminders (pop-ups) in APEX – adding documents to Notes or Scanned Outside Documents – insertion of new patient education materials into the After Visit Summary • MyChart: – inserting patient-facing materials into MyChart – recruiting study subjects via MyChart and securing informed consent – mass messaging patients through MyChart – deploying surveys through MyChart
    13. 13. DGIM Survey Choices (n=16/28 researchers) APEX Requests Votes for "Top 5" Votes for "Top 1" Integrate existing risk calculators into APEX 13 4 New structured note templates within APEX 8 2 MyChart Requests Votes for "Top 5" Votes for "Top 1" Deploying surveys through MyChart 9 2 Recruit study subjects via MyChart 8 3 Insert patient-facing materials into MyChart 8 1 Other Top 1 choice with 2 votes: launching external website from within APEX
    14. 14. Next Steps • Build on data management foundation to increase hypothesis-centric use of informatics at UCSF – make point-of-care and other interventional research easier, faster, cheaper • Next CTSI proposal will emphasize e- research infrastructure – interventional, observational, community-based • Need input from you!

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