Informatics for Clinicians and Clinical Investigators – v6 June 21, 2012

Subtitle: What every clinical researcher needs to know about informatics

Goal: train the next generation of clinical researchers in the basics of clinical information systems (CIS) so they can both use the data that is derived from these systems as well as understand the issues surrounding the design, development, implementation, and evaluation of CIS-based interventions.

Objectives:

1.  Identify the key clinical information system-related challenges facing clinical researchers over the next 3-5 years

2.  Identify the knowledge that a person with an MD degree and training in health services research should know about clinical information systems.

Tentative Course Schedule:

1.  July 11 – Sittig, Introduction to course and Informatics.

2.  July 18 –McCoy - Clinical decision support

3.  July 25 – Herskovic – Data Warehouses

4.  Aug 1 – Sittig – Controlled Clinical Vocabularies

5.  Aug 8 – Sittig – Clinical Information System Return on Investment Calculations - How do we begin moving away from association toward causation?

6.  Aug15- Singh - e-Communication

7.  Aug 22 –Cohen - Natural Language Processing

8.  Aug 29 – Sittig – Future of Clinical Informatics

1.  Sittig – Introduction to Clinical Informatics

  1. Right System – Hardware and software must be capable of supporting the clinical activities. It must be fast, reliable, and appropriately protected to ensure the safety, privacy, and integrity of the clinical and administrative data it contains.
  2. Right Content – EMR vocabulary used to encode the clinical findings, enter orders, and store laboratory results must be standardized and used to encode all data. The clinical knowledge that forms the basis of the clinical decision support must be evidence-based and appropriate for the user’s practice as well as periodically updated.
  3. Right Human-Computer User Interface – The EMR’s user interface must be user-friendly: easy to learn and use. The interface should present all the relevant patient data in a format that allows the clinicians to rapidly perceive the problem, formulate a response, and document his/her actions.
  4. Right People – Users must be appropriately trained and re-trained and interact closely with the informatics experts and clinical application coordinators responsible for designing and maintaining the systems.
  5. Right Workflow / Communication – the EMR must fit into the workflow of the clinic or hospital and enhance situational awareness of its users who often practice in time pressured settings.
  6. Right Organizational Policy & Procedures – the organization must make adjustments to previous policies or new policies that account for the EMR use
  7. Right State and Federal Rules and Regulations – both the State and Federal governments must continue to work to create the appropriate regulatory environment that will enable these systems to continue evolving while maintaining appropriate safety and privacy oversight.
  8. Right Monitoring -- organizations or users must continually evaluate the performance of EMRs through robust, monitoring systems and test if automated processes are working as expected after implementation.

Readings:

·  Read law of medical information': the further information has to be able to circulate (i.e. the more diverse contexts it has to be usable in), the more work is required to disentangle the information from the context of its production.

·  Sittig DF, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care. 2010 Oct;19 Suppl 3:i68-74.

·  section 1.1.4 (page 13) in Shortliffe and Cimino.

·  Marc Berg’s Health Information Management- chapter 4, particularly pages 71 – 78,

2.  Clinical decision support – (McCoy) following the clinical decision making process, there is a tremendous amount of work involved in setting up and maintaining any clinical decision support system. (Sittig – Grand Challenges; Ash – CDS)

a.  CDS means different things to different people

b.  For patient-specific CDS, you need DATA!

c.  Clinical Knowledge Management is necessary for CDS

d.  Knowledge engineers are “special people”

e.  Work to facilitate translation for collaboration

f.  The system, including the hardware, software and user interface must be easy to use and fast

g.  Workflow analysis must be a part of the organizational culture

h.  Communicating new CDS features and functions to clinicians is hard

i.  Training and supporting CDS users is difficult

j.  Nurture and support your clinical champions

Readings:

·  Ash JS, Sittig DF, Guappone KP, Dykstra RH, Richardson J, Wright A, Carpenter J, McMullen C, Shapiro M, Bunce A, Middleton B. Recommended practices for computerized clinical decision support and knowledge management in community settings: a qualitative study. BMC Med Inform Decis Mak. 2012 Feb 14;12:6.

·  Sittig DF, Wright A, Osheroff JA, et al. Grand challenges in clinical decision support. J Biomed Inform. 2008 Apr;41(2):387-92. Epub 2007 Sep 21.

3.  Data warehouses – (Herskovic) In addition to the real-time, transaction oriented face of the EMR, there is also the vast amount of clinical data that is contained in the off-line clinical data warehouses. Over time, use of this data will become even more important for administrative and clinical decision support. (Bernstam)

1. Missing data cannot be assumed to be “normal” or unimportant-

2. Data collected for one purpose is not valid for another purpose

3. It is difficult to understand “why” something was done from billing codes. They are better at telling us “what” happened. Also no indication of the severity of the illness.

4. The freetext portion of the EHR contains at least 50% of the important data.

5. Difficult to track relationships in data from a database since you only have timestamps (which may be inaccurate). The rest is conjecture. Also not everything that is done is tracked.

6. Difficult to get all the data you want even prospectively since other people are not as interested in particular data items as you are. Therefore, large DB-centric trials reduce to the least common denominator.

7. No matter how big your database is, if you apply enough filtering criteria you can run out of sample. (ref: Weiner, M ?)

8. No matter how many study inclusion or exclusion criteria you develop, you will always have a few individuals in the sample that are not appropriate and you will always miss a few who should be included in the sample, but aren’t.

9. There are many patients in your database that have essentially no data (they registered but never came, went to the ED once, came for a test, etc.) and will wreck havoc with your denominators.

10. There’s often more than one storage location, or multiple ways to code the same concept, for any particular data item – make sure you find them all (i.e. HbA1c could be in labs, health maintenance, a flowsheet, a note, etc.)

4.  Sittig - Controlled Clinical Vocabularies – Common, standards-based clinical vocabularies will become more important as time passes. In addition, before we can have wide-spread adoption and sharing of clinical data, much more work will need to be done with the existing clinical vocabulary standards. (based on paper by Alan Rector – Why is clinical terminology so hard?)

a.  The scale and the multiplicity activities tasks and users it is expected to serve is vast.

b.  Conflicts between the needs of users and the requirements for rigorously developed software must be reconciled

c.  The complexity of clinical pragmatics – support for practical use for data entry, browsing, and retrieval – and the need for testing the pragmatics of terminologies implemented in software.

d.  Separating language and concept representation is difficult and has often been inadequate.

e.  Pragmatic clinical conventions often do not conform to general logical or linguistic paradigms.

f.  Both defining formalisms for clinical concept representation and populating them with clinical knowledge or ‘ ontologies’ are hard – and that their difficulty has often been underestimated.

g.  Determining and achieving the appropriate level of clinical consensus is hard and requires that the terminology be open ended and allow local tailoring.

h.  The structure idiosyncrasies of existing conventional coding and classification systems must be addressed

The terminology must be coordinated and coherent with medical record and messaging models and standards

i.  Change must be managed, and it must be managed without corrupting information already recorded in medical records.

Readings:

·  Rector AL. Clinical terminology: why is it so hard? Methods Inf Med. 1999 Dec;38(4-5):239-52.

·  Rosenbloom ST, Brown SH, Froehling D, Bauer BA, Wahner-Roedler DL, Gregg WM, Elkin PL. Using SNOMED CT to represent two interface terminologies. J Am Med Inform Assoc. 2009 Jan-Feb;16(1):81-8

5.  Sittig – Clinical Information System Return on Investment Calculations - How do we begin moving away from association toward causation?

a.  Sir Austin Bradford Hill’s Criteria for Causation

b.  Strength of association - The greater the change observed, the more likely the association is to be causal

  1. Consistency of findings - Has the change been observed by different groups, in different places with different circumstances and systems? The more, the better!
  2. Specificity of association - How many other factors exist which might explain the change? If none, then it is easy…if many does not mean that one of them is still not correct.
  3. Temporality - Did the change occur following the introduction of the computer system?

f.  Dose – response - Does the size of the change increase with increases in features or system capabilities?

  1. Plausibility - Is there any plausible connection between the change we see and the system we have implemented?

h.  Coherence - Does the change seem to be coherent with known aspects of computer systems?

  1. Experimental evidence - If the system is removed, does the change stop or reverse itself?

j.  Analogy - Are there other similar systems that have shown similar effects

Readings:

Chertow, GM et al. “Guided Medication Dosing for Inpatients with Renal Insufficiency.” JAMA. 286(22): 2839-44, 2001.

6.  Communication & Workflow analysis – (Singh) prior to system implementation, careful workflow analysis and documentation can improve the changes of implementation success.

a.  Figuring out who to send a message (whether computer generated or not) to.

b.  Acknowledgement: Making sure that all messages are received.

c.  Attestation is the act of applying an electronic signature to the content, showing authorship and legal responsibility for a particular unit of information.

d.  Authentication is the security process of verifying a user’s identity with the system that authorizes the individual to access the system (e.g., the sign-on process). Authenticating is important because it assigns responsibility for an entry they create, modify, or view.

e.  Non-repudiation—strong and substantial evidence that will make it difficult for the signer to claim that the electronic presentation is not valid.

f.  Asynchronous vs. synchronous

g.  Channels – a wide variety of different communication channels available, from basic face-to-face conversation, through to telecommunication channels like the telephone or e-mail, and computational channels like the medical record. Includes written, spoken, email, message

h.  Coded vs. freetext messages

i.  Fail-safe mechanisms

Readings:

·  Coiera E. Communication systems in healthcare. Clin Biochem Rev. 2006 May;27(2):89-98.

·  AHIMA. "Electronic Signature, Attestation, and Authorship. Appendix B: Laws, Regulations, and Electronic Signature Acts." Journal of AHIMA 80, no.11 (November-December 2009). Available at: http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_045546.hcsp?dDocName=bok1_045546

7.  Natural Language Processing (Cohen) (Friedman papers)

a)  Gold standards

b)  Part-of-record detection (i.e. Family history vs personal history, prescription vs current meds) - History of or Family history of vs. illness patient has

c)  Temporality, especially relative time

d)  Anaphoric referent disambiguation

e)  Cross-document reference disambiguation

f)  Word sense disambiguation - “hand” – clap, help, set of cards in poker, end of your arm, height of horse

g)  Misspellings, abbreviations, acronyms

h)  Relationship detection and extraction

i)  Named entity recognition

j)  Quality and usefulness of the dictionaries.

k)  Negatives in text – need to recognize these

l)  Severity of conditions or illnesses

m)  Identifying quantity or counts.

n)  Optical character recognition vs. ASCII vs. voice recognition – many confuse these

8.  Sittig – Future of Clinical Informatics

a.  The next generation Internet;

b.  Real-time clinical decision support systems;

c.  Off-line, population-based systems;

d.  Large, integrated, individual patient-level phenotypic and genotypic databases with intelligent data mining capabilities;

e.  Wireless, invasive and non-invasive physiologic monitoring devices;

f.  Natural Language Processing (NLP) systems;

  1. Mathematical models of complex biological systems

Reading: Sittig DF. Potential impact of advanced clinical information technology on cancer care in 2015. Cancer Causes Control. 2006 Aug;17(6):813-20.

Grading scheme:

Attendance: Students are required to attend 5/8 classes during the course. For each course session attended, students will receive 2% of their final grade (in other words, class attendance counts for 10% of the final grade. Special exceptions may be made for students who are not physically located at UTHouston.

Quizzes: Following each class there will be a quiz consisting of 5-10 multiple choice or true/false or matching questions that cover key points from that lecture. This quiz will be available online for 1 week after the end of the lecture. The results of all these quizzes will count toward 40% of the final course grade.

Writing project: The final project which counts for 50% of the final course grade will consist of brief reviews of selected scientific articles along with the student’s answers to 1-3 questions. These reviews will be 1-2 pages (300-500 words). Students will be responsible for reviewing and answering questions for 5 of the 7 articles provided. Each review will count for 10% of the final grade. The first 2 reviews will be due following week 4 (August 5, 2012) and the final 3 reviews will be due at the end of final week of class (September 2, 2012). There is no penalty for turning in these assignments early. 10% penalty/week they are late.

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