Docstoc

Informatics

Document Sample
Informatics Powered By Docstoc
					Informatics for Clinicians and Clinical Investigators – v4                5/28/2010
Subtitle: Why is Clinical Informatics So Hard?

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:

June 30 – Sittig, Introduction to course and Informatics.
     Read Sittig, Singh JAMA 2009 – 8 Rights of Safe and Effective EHR Use (QSH article).
     section 1.1.4 (page 13) in Shortliffe and Cimino.
     opportunity for HS researchers to study the effectiveness of information systems
     Marc Berg‘s Health Information Management- chapter 4, particularly pages 71 – 78,

July 7 - Sittig - Controlled Clinical Vocabularies

July 14 Sittig - Clinical decision support

July 21 – Herscovich – Natural Language Processing

July 28 – Johnson – User Interfaces

Aug 4 – Data warehouses – Bernstam or sittig

Aug 11- Singh, e-Communication

Aug 18 – Sittig, Final course – Future of Clinical Informatics


1.   Sittig – Introduction to Clinical Informatics
          a. 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.
          b. 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.
          c. 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.
          d. 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.
          e. 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.
          f. Right Organizational Policy & Procedures – the organization must make adjustments to previous
               policies or new policies that account for the EMR use




                                                                                                                          1
         g.   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.
         h.   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.

2.   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.

References:
     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


3.   Clinical decision support – (Sittig) 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. CDS Themes paper
             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.

4.   Natural Language Processing (Herskovic) (Friedman papers)
     a) Gold standards




                                                                                                                         2
     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


5.   User Interface Design – (Johnson) The user interface is the ―face‖ of the clinical information system. This is the
     only aspect of the system that most clinicians know about. Using the screen design tools that vendors provide, to
     customize various screens for local use, is one of the keys to a successful implementation. (Sanderson – Australia)
     a. screen customization
     b. paper form design
     c. for Health services researchers, interface design is really all about reliable data capture. Interfaces must be
         designed to fit the workflow of clinicians and reliably capture the required data. An interface that may be
         acceptable to a clinician and captures data adequately for the care of individual patients may not meet the
         needs of researchers (or for quality measurement). Typically an interface that meets the needs of researchers
         will require ‗buy-in‘ from the humans using the interface – i.e., must agree that it‘s worth the effort to capture
         the data reliably n a standard format. Still, the researcher must understand the workflow in order to create an
         acceptable interface.


6.   Data warehouses – (Bernstam) 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.)
     At the same time, make sure you aren‘t double counting (e.g. the same HbA1c result in two places, or a pending
     and final result). (Ref: Safran BP article)
     11. Often even so-called, standardized data such as ICD-9 and CPT codes, or even Admit time are used differently
     in different clinics, even in the same location but especially across locations. These clinics can have different
     billing practices, standard operating procedures, levels of aggressiveness with billing, etc that makes "standard"



                                                                                                                              3
     codes assigned to patients, non-standard.


7.   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_04
              5546

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;
          g. 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.

     Preliminary Grading scheme:
     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.
     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.

     Final project:
     The final project which counts for 50% of the final course grade will consist of 10, 1-2 page (200-500word)
     explanations of one of the key points from each week of the course. Students must choose at least 1 and not more
     than 2 topics from any single lecture. Each explanation should include at a minimum:



                                                                                                                         4
1.   A definition or explanation of the key point
2.   An explanation of why this point is important
3.   An explanation of what has been done so far to address this issue
4.   Three references that relate to the topic you are explaining

The first 3 explanations will be due following the end of week 3 (July 14, 2010); the second 3 explanations will be
due following the end of week 6 (August 4, 2010) and the final 4 explanations will be due at the end of week 8
(August 18, 2010). There is no penalty for turning in these assignments early.




                                                                                                                  5

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:24
posted:12/5/2011
language:English
pages:5