Pathology Informatics and Patient Safety Ppt Powerpoint Presentation slides Mike Becich Medical Slideshow Patient Safety and Public Health Informatics

Pathology Informatics and Patient Safety

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Slide 1 :

Slide 1 Invitation to APIII 2006 August 16-18th, Vancouver, BC http://apiii.upmc.edu

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Slide 2 Pathology and Patient Safety: The Critical Role of Pathology Informatics in Error Reduction and Quality Initiatives2nd Annual Pathology Quality and Patient Safety Meeting – Improving Hospital and Laboratory Safety Friday, May 19th, 2006 Michael J. Becich, MD PhD (becich@pitt.edu) Vice Chairman and Professor of Pathology, Professor of Information Sciences & Telecommunications University of Pittsburgh School Medicine Director, Center for Pathology Informatics http://path.upmc.edu/cpi Soon to be Chairman (approx. 7/1/06), Department of Biomedical Informatics http://www.cbmi.pitt.edu Useful Resources: AHRQ Patient Safety Network – http://psnet.ahrq.gov/ CAP Symposium on Pathology & Patient Safety – http://arpa.allenpress.com/arpaonline/?request=get-toc&issn=1543-2165&volume=129&issue=10 Center for Pathology Quality and Healthcare Research – http://www.pathology.upmc.edu/pathologyquality/ Pathology Quality and Patient Safety Meeting – http://path.upmc.edu/pqps

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Slide 3 Disclosures by MJB Corporate Support for API and APIII 650K projected for 2006 [Cerner, Misys, IBM, Intel, IMPAC CAP Today, Cisco, Verizon, AACI, Aperio, Apollo, Applied Bioinformatics, Ardais, Bayer Healthcare, Beckman Coulter, Bioimagine, DakoCytomation, Chromavision, GE/Amersham Biosciences, General Data, Humintec, Nikon, Olympus, PSA, Psyche, Roche Diagnostics, ScanSoft, SCC Soft Computing, Synchroscopy, SNOMED, Taylor Data, Thermo Shandon, Trestle and Voicebrook and others] Corporate Sponsored Research Agreements 1.3 M in 2006 [Amgen, Ardais Corp, Aurora Interactive, Cerner, Clinical Data, IBM, Intel, Misys, Nikon, Olympus, Pittsburgh Life Sciences Green House & Trestle Corp.] Startup/Public Companies (Founder, Equity & Consultant): Trestle Corporation (NASDAQ: TLHO) formerly InterScope Technologies, Inc. - http://www.trestlecorp.com Member of the Board of Directors, Consultant & Chair, Scientific Advisory Board Provider of high speed/volume whole slide imaging/telepathology systems Ultrarapid whole slide imaging: Gb data transfers, terabyte storage and robotics Clinical Data (NASDAQ: CLDA) formerly, Icoria merged with Pardigm Genetics & TissueInformatics, Inc., 12/06, http://www.clda.com Systems biology as a CRO to pharma, agra and biotech; venture backed TVM, Motorola. Hyperquantitative image analysis, genomics and bioinformatics capabilities. Consultancies (in addition to Trestle Corp.) Pathology Education Consortium (PEC) with Bruce Friedman (volunteer) Misys – Strategic Planning Group and Physician Advisory Board ThermoElectron – Physician Advisory Board

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Slide 4 Pathology Informatics and Patient Safety Introduction Pathology Informatics Tools CAP Checklists and Synoptic Reporting Clinical Context Object Working (CCOW) Group and Health Level 7 (HL7) Intelligent Tutor Systems Proposal for “Real” QA (discussion) Conclusions – Where do we go from here?

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Slide 5 Pathology Informatics and Patient Safety Misread pathology specimens can have serious consequences on the safety of patients Mitchell H, Medley G, Giles G. Cervical cancers diagnosed after negative results on cervical cytology: Perspective in the 1980s. BMJ 1990;300:1622-1626. One study reported that 10 percent to 30 percent of test results were inappropriately classified as normal based on rescreening reviews Wilbur, DC. False negatives in focused rescreening of Papanicolaou smears: How frequently are 'abnormal' cells detected in retrospective review of smears preceding cancer or high-grade intraepithelial neoplasia? Arch Pathol Lab Med 1997 121(3):273-276. One study found major errors (those likely to affect patient care management) in 1.2 percent of the pathology cases examined prospectively Lind AC, Chhanda B, Healy JC, Sims KL. Prospective peer review in surgical pathology. Amer J Clin Path, 1995 104(5):560-566. Diagnostic error rates reported in one study ranged from 0.25 percent to 43 percent Renshaw AA. Measuring and reporting errors in surgical pathology. Lessons from gynecologic cytology. Am J Clin Path 115(3):338-341, 2001. This information coupled with the IOM and Rand report should herald a critical look at the areas in Pathology that are amenable to Informatics implementations that address these problems

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Slide 6 A Roadmap for Progressive Growth = Advancing Practice, Instruction & Innovation Through Pathology Informatics Level 1 – Priming the Clinical Practice Engine Supporting the Anatomic Pathology Laboratory Information System Supporting the Clinical Pathology Laboratory Info System (next year) Distinguishing IT support from Pathology Informatics Level 2 – Clinical Practice Connectivity and Quality Triad Interfaces and Integration Issues with EMR, QA/QC/QI programs, etc… Synoptics, report generation, remote computing, imaging and voice. Level 3 – Supporting the Instructional Mission Web tools, on-line training materials, web cast, specialty training needs Level 4 – Supporting Research Information Services Tissue banking, tissue microarrays, de-identification, honest broker Level 5 – Developing an Innovative Research Program Bioinformatics, imaging, decision support, cancer informatics, outcomes

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Slide 7 Level 1 – Priming the Clinical Practice Engine Supporting the Anatomic Pathology Lab Info Sys (LIS) Automated Histology, Special Procedure and Consult Billing & Reporting Reengineering of Transcription, Electronic Signout/Signature “Nuclear” Reporting – All special procedures/testing linked AP report Synoptic Reporting, Templates and Macros for the Quality Triad Bar Coding, Telephone Interface, Immunostaining/Histology Interfaces Supporting the Clinical Pathology Laboratory Info System Next Year (Robotics with a Pre-Analytical focus, POE, Decision Support) Owning the Reporting, EHR integration, POC testing, Test Integration Interfaces Development and Pricing Strategy (currently over 130) Lab Portal, Web Reporting, “Virtual” Lab, Compliance/Regulatory FX Distinguishing IT support from Pathology Informatics ‘Help Desk’ support is in place 24X7, and backup technical teams are on call at all times for clinical and critical research systems. Outsource to central IT – network, desktop, and PC/peripheral support Focus on Application Support and Research Support

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Slide 8 Level 2 - Clinical Practice Connectivity and the Quality Triad Interfaces and Integration Issues EHR (Electronic Health/Medical Record) – Key Partnership Required 70/70/70 Rule – 70% of health care data in EMR is Pathology data 70% of medical decisions/critical health care events involve Pathology date 70% of all pathology testing is oncology related QA/QC/QI programs – A new initiative to reduce errors Toyota Production Systems and the Quality Triad (I’m a believer!!!) Synoptics, Templates, “Quick Text”, etc Role of common data elements and ISO Standardized report CAP Cancer Checklists SNOMED CT Report generation – AutoFAX, EHRs, interface 3rd party sys Remote computing – VPN, Citrix, remote transcription Imaging - Digital Libraries concept not just telepathology Imaging integration, whole slide imaging and quantitative image analysis

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Slide 9 Level Three: All of the features in Level Two, plus: 1. Systems provide educational and decision-support benefits while reducing errors, improving quality and being cost-effective. 2. Standardized/automated “order sets”& templates to reduce errors. 3. Computer assisted decision support systems for med orders. 4. Computer systems check for allergies and drug interactions. 5. Computer systems support alerting through monitoring of laboratory values. 6. Keystroke level log files for review for safety, quality, etc… 7. Research systems provide decision support. 8. Systems are in place to facilitate clinical care delivery and interfacing to research systems to provide data captured at POC. 9. Clinical/research systems have “production”, “training”, and “development and test” platforms which are separate. Pathology Informatics Capabilities -Research Information Services Quality Triad

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Slide 10 Pathology Informatics and Patient Safety Introduction Pathology Informatics Tools CAP Checklists and Synoptic Reporting Clinical Context Object Working (CCOW) Group and Health Level 7 (HL7) Intelligent Tutor Systems Proposal for “Real” QA (discussion) Conclusions – Where do we go from here?

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Slide 11 UPMC Pathology Informatics 16 laboratories 2,000,000 annual ADT transfers received by the LIS – all via HL7 150,000 specimens processed per year (65K neoplasms) – all report transfers are electronic via HL7 Commitment to collaborations with our Network Cancer Registry for clinical and research initiatives Commitment to standardizing data collection in pathology as evidenced by our experience

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Slide 12 Pathology Centers Of Excellence at UPMC UPMC Presbyterian ENT/Thyroid Pathology Gastrointestinal Pathology Hematopathology Neuropathology Thoracic/Mediastinal Pathology Transplant Pathology UPMC Shadyside Breast Pathology Dermatopathology Genitourinary Pathology Bone/Soft Tissue/Melanoma Pathology Magee-Womens Hospital, UPMC Breast Pathology Gynecologic Cytopathology Obstetric/Gynecologic Pathology

Slide 13 :

Slide 13 Interoperability Opportunities Interoperability Opportunities and Integration Issues EMR (Electronic Health/Medical Record), EMG (Enterprise Middleware Group) & Disease Registries are Key Partners 70/70 Rule for Pathology Support of our UPMC EMR strategy – 70% of medical decisions/critical events involve Path data 70% of all pathology testing is oncology related Pathology systems in 20 of 20 UPMC facilities Cancer Registries provide key outcomes data – migration to a clinical system (in 16 of 20 UPMC facilities) Synoptics or Structured Reporting (Cerner/Misys/ IMPAC) for “compliant” CAP cancer checklist reporting (mandated by ACOS, 1/04) and are coded in SNOMED CT Role of common data elements and ISO compliance (later) CAP Cancer Checklist have been key to establishing this

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Slide 14 Gross and Microscopic Examination of Surgical Specimens, particularly large resections yields comprehensive information of immense value to treatment decisions such as adjuvant therapy, radiation, chemotherapy and other interventions. Traditionally, narrative descriptive reports have been used to convey this valuable information to the patients and their health care teams. This information is of great value to the cancer patient with providing them measures of prognosis and outcomes. Synoptic Reporting in PathologyBackground

Slide 15 :

Slide 15 Traditional narrative and descriptive reports in free text format have significant variability because different pathologists use a multitude of different reporting styles to describe their findings. More often such variability results in pathology reports missing important data elements such as margins, lymphatic invasion etc. Synoptic Reporting, either as part of the pathology report or replacing the free text component, has uniformity with standardized data elements in forms of checklists thus ensuring the pathologist makes note of these findings in their reports. Synoptic Reporting in PathologyBackground

Slide 16 :

Slide 16 UPMC Pathology Experience: Synoptic Evaluation Application (SEA)

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Slide 17 CoPathPlus: Synoptic Worksheet

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Slide 18 Standards for RPP2 Project HL7 - Health Level-7 messaging standard SNOMED codes - Systematized Nomenclature of Medicine - clinical terms codes LOINC codes - Logical observation identifier names and codes – lab codes CAP protocols - College of American Pathology – pathology templates ICD-9-CM - International Classification of Diseases, Clinical Modification - coding system to code signs, symptoms, injuries, diseases, and conditions used by pathology FORDS/SEER/NAACCR

Slide 19 :

Slide 19 UPMC Pathology Experience: Synoptic Evaluation Application (SEA)

Slide 20 :

Slide 20 Disease Registry Data Sources: Pathology Common Data Elements - direct (CAP) or derived values primary site laterality histology tumor behavior grade/differentiation place of diagnosis tumor size/depth of invasion extension to regional /distant tissues TNM, AJCC Stage Group # regional nodes removed # regional nodes positive date of 1st positive biopsy date of initial diagnosis perineural invasion lymphatic invasion margin involvement diagnostic confirmation dx & staging procedures metastatic site(s) progression/recurrence date(s) of pathologically confirmed mets or recurrence microscopic confirmation CoPath PLUS

Slide 21 :

Slide 21 Cancer Registry Data Sources: PathologyCommon Data Elements - direct (CAP) or derived values Cancer Identification & Staging Categories

Slide 22 :

Slide 22 Pathology Informatics and Patient Safety Introduction Pathology Informatics Tools CAP Checklists and Synoptic Reporting Clinical Context Object Working (CCOW) Group and Health Level 7 (HL7) Intelligent Tutor Systems Proposal for “Real” QA (discussion) Conclusions – Where do we go from here?

Slide 23 :

Slide 23 CCOW–Synchronized Sign On Clinical Context Object Workgroup = CCOW CCOW is a vendor independent standard developed by the HL7 (see below) organization to allow clinical applications to share information at the point of care. Using a technique called "context management", CCOW allows information in separate healthcare applications to be unified so that each individual application is referring to the same patient, encounter or user. This means that when a clinician signs onto one application within a CCOW environment, and selects a patient, that same sign-on is simultaneously executed on all other applications within the same environment, and the same patient is selected in all the applications, saving clinician time and improving efficiency.

Slide 24 :

Slide 24 Phase Two CCOW Integration

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Slide 25 TEST, ABBY

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Slide 28 Doe, John

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Slide 32 Interoperability OpportunitiesBRIDG and HL7v3 Biomedical Research Integration Domain Group Collaborative project between CDISC, HL7 and caBIG CDISC – Clinical Data Interchange Standards Committee Goals: Develop a semantically robust representation of the domain of clinical trials research Harmonize CDISC standards and application development in caBIG Model will be used as the semantic foundation for HL7 messages related to clinical research High value to our clinical trials and translational research efforts Bottom Line: UPMC, UPP and U Pitt need to get more involved in standards committees as these foundational elements will be critical to our interoperability efforts.

Slide 33 :

Slide 33 Pathology Informatics and Patient Safety Introduction Pathology Informatics Tools CAP Checklists and Synoptic Reporting Clinical Context Object Working (CCOW) Group and Health Level 7 (HL7) Intelligent Tutor Systems Proposal for “Real” QA (discussion) Conclusions – Where do we go from here?

Slide 34 :

Slide 34 The SlideTutor Project Rebecca Crowley, MD, MS University of Pittsburgh School of Medicine

Slide 35 :

Slide 35 Intelligent Tutor Systems Background Developing expertise in pathologic diagnosis is difficult and time-consuming In domains outside of medicine, intelligent computer-based training is common Aviation simulators Nuclear and power plant simulators Military Research in many domains has shown that computer systems can simulate the well-known benefits of one-on-one teaching

Slide 36 :

Slide 36 Intelligent Tutoring Systems Adaptive, flexible, individually tailored instruction Not ‘text and test’ but rather coached practice environments System able to ‘solve the problem’ on it’s own and therefore able to provide feedback on student actions Monitor student’s progress and change teaching based on how the student is learning

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Slide 37 Student Model Pedagogic Knowledge Interface Expert Module Allow correct steps Correct errors Give hints on next step Collect data on what student does Make predictions on what student knows Provide data for pedagogic decision making Case sequence When to intervene How much to intervene How to intervene Intelligent Tutoring Systems

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Slide 39 :

Slide 39 Case Diagnosis Testing Interface

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Slide 40 Performance Improvement with ITS *** ***

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Slide 41 Pathology Informatics and Patient Safety Introduction Pathology Informatics Tools CAP Checklists and Synoptic Reporting Clinical Context Object Working (CCOW) Group and Health Level 7 (HL7) Intelligent Tutor Systems Proposal for “Real” QA (discussion) Conclusions – Where do we go from here?

Slide 42 :

Slide 42 Pathology Informatics and Patient Safety Introduction Pathology Informatics Tools CAP Checklists and Synoptic Reporting Clinical Context Object Working (CCOW) Group and Health Level 7 (HL7) Intelligent Tutor Systems Proposal for “Real” QA (discussion) Conclusions – Where do we go from here?

Slide 43 :

Slide 43 2006 Pathology Informatics Projects To Improve Patient Safety Synoptic Reporting For Biopsy Specimens Bar-coding In Anatomical Pathology Labs Whole Slide Imaging For QA/QC And Primary Diagnosis Telepathology For Frozen Section Diagnosis Attract More Users To Use And Adapt New Technologies To Improve Patient Care And Pathology Reporting BOTTOM LINE: Focus on quality triad!!!

Slide 44 :

Slide 44 What does caBIG offer in terms of interoperability? Three common threads for caBIG (grid) development #1 - Development of common data elements (CDEs) using Enterprise Vocabulary Services (EVS) Depositing CDEs developed across systems in caDSR caDSR – Data Standards Repository as a CDE management tool Key is to use a common concepts/terms for data interchange 3 – “Information” objects enabled which can be deposited in caBIO 2 – Ensure data exchange via ISO11179 compliant CDEs 1 – EVS ensures all are “speaking” the same language Courtesy of Peter Covitz, NCICB

Slide 45 :

Slide 45 ISO11179 Metadata Standardcourtesy of Peter Covitz, NCICB

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Slide 46 Patel AA, Kajdacsy-Ball A, Berman JJ, Bosland M, Datta MW, Dhir R, Gilbertson J, Melamed J, Orenstein J, Tai KF, Becich MJ. The development of common data elements for a multi-institute prostate cancer tissue bank: The Cooperative Prostate Cancer Tissue Resource (CPCTR) experience. BMC Cancer. 2005 Aug 21;5(1):108. Becich MJ, Gilbertson JR, Gupta D, Grzybicki DM and Raab, SS. Patient Safety and Healthcare Research: The Critical Role of Path Informatics in Error Reduction and Quality Initiatives. Clin Lab Med. 2004 Dec;24(4):913-43. Berman JJ, Datta M, Kajdacsy-Balla A, Melamed J, Orenstein J, Dobbin K, Patel A, Dhir R, Becich MJ. The tissue microarray data exchange specification: implementation by the Cooperative Prostate Cancer Tissue Resource. BMC Bioinformatics. 2004 Feb 27;5(1):19. Melamed J, Datta MW, Becich MJ, Orenstein JM, Dhir R, Silver S, Fidelia-Lambert M, Kadjacsy-Balla A, Macias V, Patel A, Walden PD, Bosland MC, Berman JJ. The cooperative prostate cancer tissue resource: a specimen and data resource for cancer researchers. Clin Cancer Res. 2004 Jul 15;10(14):4614-21. Lyons-Weiler J, Patel SV, Becich MJ, Godfrey T. Tests for finding complex patterns of differential expression in cancers: towards individualized medicine. BMC Bioinformatics 2004, 5:110-116. Mitchell KJ, Becich MJ, Berman JJ, Chapman WW, Gilbertson J, Gupta D, Harrison J, Legowski E, and Crowley RS Implementation and Evaluation of a Negation Tagger in a Pipeline-based System for Information Extraction from Pathology Reports Proc Med Info. 2004:663-7. Li S, Becich MJ, Gilbertson J. Microarray Data Mining Using Gene Ontology. Proc Med Info. 2004:778-82. Gilbertson JR, Gupta R, Nie Y, Patel AA, Becich MJ. Automated Clinical Annotation of Tissue Bank Specimens. Proc MedInfo, 2004: 607-610. Yagi Y, Ahmed I, Gross W, Becich MJ, Demetris AJ, Wells A, Wiley CA, Michalopoulos GK, Yousem SA, Barnes B, Gilbertson JR. Webcasting pathology department conferences in a geographically distributed medical center. Hum Pathol. 2004 Jul;35(7):790-7. Crowley RS, Gadd CS, Naus G, Becich M, Lowe HJ. Defining the role of anatomic pathology images in the multimedia electronic medical record--a preliminary report. Proc AMIA Symp 2000:161-5. Landman A, Yagi Y, Gilbertson J, Dawson R, Marchevsky A, Becich MJ. Prototype web-based continuing medical education using FlashPix images. Proc AMIA Symp. 2000; 462-6. Becich, M.J.. Information management: moving from test results to clinical information. Clin Leadersh Manag Rev. 2000 Nov-Dec;14(6):296-300. Becich, M.J. The role of the Pathologist as tissue refiner & data miner:The impact of functional genomics on the modern path laboratory & the critical role of Pathology Informatics & Bioinformatics Molec Diag. 5(4):287-299, 2000 Recent Publications by Our Team – NOTE: Please e-mail me at becich@pitt.edu if you want PDFs

Slide 47 :

Slide 47 Invitation to APIII 2006 August 16-18th, Vancouver, BC http://apiii.upmc.edu

Slide 48 :

Slide 48 End of Talk – e-mail me at becich@pitt.edu if you have questions/clarifications not covered in the discussion. NOTE: Please e-mail me if you want PDFs of articles or presentation. Thank you for the invitation to participate in the Pathology Quality and Patient Safety Meeting.

	becich@pitt.edu 1 Years ago. Category: Pathology

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