Automating Anatomic Pathology

Transcription

1 Automating Anatomic Pathology J. Mark Tuthill, MD Division of Pathology Informatics, Henry Ford Hospital Detroit, MI Pathology Informatics Summit May, 2015

2 Disclosures In accordance with ACCME guidelines, any individual in a position to influence and/or control the content of this ASCP CME activity has disclosed all relevant financial relationships within the past 12 months with commercial interests that provide products and/or services related to the content of this CME activity. The individual below has responded that he/she has no relevant financial relationship(s) with commercial interest(s) to disclose: J. Mark Tuthill, MD

3 Objectives Describe how automation and the future of the Anatomic Pathology LIS (AP-LIS) are inextricably related List the areas of anatomic pathology that may be capable of being automating Enumerate some of the pre-requisites for anatomic pathology automation and LIS interfaces Present examples of evolving automation, their impact, and associated AP-LIS Requirements

4 What Can We Envision Automating? The Big Picture Pre analytic Prior to receiving or analyzing the sample Preparing samples for analysis Analytic The process of analyzing the tissue Post analytic The reporting of diagnostic information Preparing for additional analytic studies

5 What Can We Envision Automating? The Big Picture Electronic order to AP-LIS Biopsy/Label (sample procurement) Transport including tracking and routing Accession Tissue Gross Exam Processing, including fixation Embedding Cutting/ Slide Labeling Routine Staining/Cover slipping Case Collation Delivery Microscopic Exam Special Stains/Re-cut Orders/materials retrieval Diagnosis Dictation Transcription Report Sign out Materials filing and storage Imagine automating and integrating these process using computerization and advanced robotics!

6 What Can We Envision Automating? The Big Picture Electronic order to AP-LIS Biopsy/Label (sample procurement) Transport including tracking and routing Accession Tissue Gross Exam Processing, including fixation Embedding Cutting/ Slide Labeling Routine Staining/Cover slipping Case Collation Delivery Microscopic Exam Special Stains/Re-cut Orders /materials retrieval Diagnosis Dictation Transcription Report Sign out Materials filing and storage

7 Automating Anatomic Pathology Prerequisites

8 Why has AP Automation Lagged Behind the Clinical Pathology Laboratory? The variability of specimens Versus CP specimens which come in tubes, AP sample vary with each surgery Required manual processes Manual dissection, embedding, sectioning A lack of interest? Do anatomic pathologists lack exposure to the ideas of automation used in the clinical laboratory? Why change what works? The harsh environment Chemicals, paraffin, bloody tissue etc

9 Prerequisites for Anatomic Pathology Automation Sophisticated electronic medical records system Electronic orders interface for Anatomic Pathology Bar code labeled assets with the laboratory Assets with unique identifiers Development of robotic technologies (a few examples) Grossing Sectioning Tissue transport Storage systems: cassettes and slides Sampling

10 Prerequisites for Anatomic Pathology Automation Sophisticated electronic medical records systems Such systems will enable clinical orders to be sent to the anatomic pathology information system as well as supporting: Decision support Gathering of accurate and required information Positive patient identification Generation of laboratory ready labels to the point of service Tracking of samples to the laboratory including monitoring of conditions

11 Prerequisites for Anatomic Pathology Automation Electronic orders interface to the Anatomic Pathology Laboratory Information System (AP-LIS) Similar to the clinical laboratory, a flow of orders to the LIS will enable: Sample receipt Tracking Routing Processing Automation of several elements of case accessioning Decrease errors Increased throughput

12 Prerequisites for Anatomic Pathology Automation Bar code labeled assets within the laboratory This is most essential early prerequisite to achieve automation within the laboratory Bar coding of assets allows for: Bar code driven workflow Identification error reduction due to mislabeling Improved efficiency by reducing manual labeling Automation of subsequent activities Integration whole slide imaging, interface devices This is the key requirement for all many automation requirement, and thus the AP-LIS

13 Prerequisites for Anatomic Pathology Automation Bar code labeled assets within the laboratory In addition to simple bar coding of assets with a accession number or medical record number it will be critical for each asset to have an unique ID embedded in the bar code This will allow each block and each slide to be managed uniquely supporting Sophisticated routing Tracking of assets Digital Pathology (unique ID on slides will be essential!) Systems interfaces Without uniquely identified assets the clinical laboratory could not have achieved the level of automation currently experienced

14 Examples of Anatomic Pathology Automation Real World Examples and Status Updates

15 Examples of AP Automation Automation of histology orders (stain protocols) Auto stainers and auto cover slippers Interfaced immunostain orders to automated immunostain platform Bar code labeling automation Automated production of cassettes at accessioning Cassette driven generation of labeled slides Tracking, routing and storage Automated tissue embedding Automated microtome's

16 Examples of AP Automation Automated block sampling Automated slide sampling Laser capture micro dissection Conveyor belt systems, tubes, roving robots Slide collation robotics Automatic diagnostics Whole slide imaging algorithms for immunostains quantification Automated pap smear readers for thin layer cytology

17 Examples of AP Automation Histology Protocols Automated ordering histology protocols for different sample types at case accession When a particular part is accession the appropriate blocks and initial stain orders are generated Initial billing fee codes are applied Histology logs are electronic sent and printed providing early notification of work This has increases efficiency and allows for LEAN processes Work is standardized Revenues were enhanced through better charge capture This is not easy and required iterative re-work and constant attention to defects to get the most satisfactory end result

18 Examples of AP Automation Bar Code Labeling Essential first step to widespread AP automation Note the impact of re-labeling in the automated staining platform As previously stated the implications of bar code labeled cassettes drives all other processes By themselves, the impact of automation of cassette labeling following by label generation are profound

19 Examples of AP Automation Bar code labeling Bar code specified surgical pathology 2D bar codes were not available so we created a solution and integrated it using middleware Interfaced to CoPath eliminating dual entry Cassettes are etched at the time of accession Uses predefined protocols Provides nicely formatted cassettes with 2D bar codes Bar coded cassettes are used to create slides labels just in time at the cutting bench Stainer shield

20 Examples of AP Automation Bar code labeling Phase 1: (Sunquest) Misys CopathPlus MHv 4.1 (Misys Healthcare, Raleigh, NC), Phase 2: Sunquest CoPath 6.0 (Sunquest Information Systems, Tucson, AZ) Custom interface to laser cassette etchers that use Labelase software, both from General Data (General Data, Cincinnati, OH) 2 Dimensional barcodes etched on the cassettes through a custom report: HFH Label by scan Cassettes are read at the microtomy stations Bar code labels for slides are generated (now with 2D bar codes) placed on glass slides prior to labeling

21 Historical Workflow After processing, sections were cut and mounted onto slides then hand labeled with the case accession number and part designation Stains were performed based on electronic requests that were available at the histology department through the LIS

22 Historical Workflow After staining, paper labels were batch printed from the LIS and affixed onto the corresponding slides

23 CoPath to Slide Etcher: Communication Pathway 2 RS232 serial ports on PC USB Connect to Etcher CoPath Sends on Com A Labelase Listens to Com B Formats Bar Code and Sends Data Etcher Receives Data and Engraves COM C USB COM A COM B 23

24 Accession Station U-shaped Cell CoPath terminal Barcode label printer -Lab tag -Specimen containers Lab tag scanner, bar code reader Cassette etcher- 2D barcode Microtome Station U-shaped Cell CoPath terminal 2D Barcode reader -Individual cassettes Slide label printer -Chemical resistant slide labels -Print 1 cassette not batch 24

25 Microtomy Station 25

26 Barcode Specified Work Processes This case is submitted in 3 specimen containers consisting of: part A - sigmoid colon biopsy, part B - transverse colon biopsy and part C - stomach biopsy with standing preorder for Helicobacter pylori immunostain. Protocol driven information is reflected in the slide labels dictating 2 levels cut for each part. The stomach biopsy protocol, part C, calls for an additional 2 blanks slides to be cut, one for the immunostain & a 4th left unstained. 4 26

27 Problems Encountered Issues: Label was misaligned Occurred 50% of the time Reasons for barcode variance: Label size (limited real-estate for essential information and bar code) Stock alignment Printer mechanical problems: Each printer had to be adjusted individually and frequently on a daily basis Bottom line: with code 128 (linear bar code) tolerances were too tight to succeed

28 Problems Encountered Impact: Up to 20% of case slides could not be pinged by pathologists on a daily basis Resulted in increased sign-out delays, slower turnaround time, and frustration (through failed reads) Required subsequent manual entry of cases into the LIS by pathologists

29 Solution We replaced linear barcodes with 2D bar codes as part of an upgrade of our LIS This included replacement of the old labeling subsystem with 'real-time labeling Native Sunquest CoPath solution ( They got it!) The new functionality in the LIS allowed seamless, automated slide label printing in real-time as each histology tissue block was scanned before sectioning

30 Benefits of Real Time Labeling: Prints a 2D bar code vs. a 1D linear label 2D bar codes require little space on the label and are % readable 2D bar codes in the upgrade created a Unique ID (part, block, slide) as an internal identifier that is used for specimen tracking Requires no keystrokes to run, as labels print automatically and the block is automatically verified

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32 Outcome With 'real-time labeling' the batch printing process has now been entirely eliminated Specimen misidentification rates have been further reduced Workflow efficacy in the histology lab has increased as cassette reading defects have been eliminated Barcode reading defects required the histotechnologist to manually type in cases numbers, leading to increased risk of patient misidentification

33 Results: Misidentification Rates % % % Number Mis-ID Defects Percent of Cases 0 1 Baseline Linear Bar Codes (Jan. 2007) 2D Bar Codes (June 2012)

34 Dirty Laundry and Bar Codes Bar codes will fail to read! Cassettes and slides have more problems than paper Root causes are multi-factorial Unique ID issues and workflow; a whole new world!» Cassette rework, re-run protocols Cassette colors impact reading Processing impacts bar codes Maintenance of etcher and printers Need for inline bar code analyzer as per the cereal box factory

35 Examples of AP Automation Automated Staining Platforms Perhaps the most commonly automated process in the current AP lab Included automated cover slipping This saves hundreds of man hours per year Has more consistent results versus manual staining and cover slipping Interfaces with AP LIS will further enhance productivity and decrease errors

36 Automated Immunostaining Interface Design The completed project was rolled out in four phases: Phase 1: A unidirectional HL7 interface was created between CoPath version 4.1 and the Dako autostainer Link 48 platforms Allowed IHC orders placed in CoPath to be directly transmitted and received by the DakoLink instrument control software

37 Automated Staining Platforms Design Phase 2: Our CoPath information system was upgraded to version 6.0 which provided the capability to uniquely identify and track case assets The upgrade allowed CoPath to assign unique identifiers to each and every case asset (i.e. parts, blocks, and slides) With this in place, unique slide IDs (linked to IHC orders in the AP-LIS) were transmitted to Dako autostainer control software Dako autostainer instruments could then read and utilize native CoPath labels

38 Unique Asset Identification As each asset in a case is created in the AP-LIS, the system gives each one a unique tag, associated with the case accession number This unique ID is permanent and stored in the stored in the AP-LIS As new stains are ordered, a unique ID is assigned This is encoded into a Data Matrix barcode printed on the slide label to be used for the stain order

39 Design Phase 3: A bi-directional HL7 interface was implemented between CoPath version 6.0 and the Dako Automated IHC platforms This allowed slide status information (i.e. pending, complete) to be transmitted from the autostainers and back to CoPath Phase 4: A bi-directional HL7 interface was implemented between Sunquest CoPathPlus version 6.0 and the Dako Automated Special Stains platforms

40 FCID Utilization The DAKOLink software uses the AP-LIS unique ID as FCID CoPath

41 CoPath Dako Interface Unique ID Transmission

42 CoPath Dako New Workflow 1. Stains are ordered in CoPathPlus. 2. Stain orders are released to the interface on demand or scheduled. 3. Interface creates an HL7 message and delivers to Dako 4. Message is routed to instrument. Slide labels print from CoPath. 5. Slides are cut in the laboratory and Slide labels print from CoPath and labels are applied to slides. 6. Slides are placed into the instrument. Label is scanned by instrument which indicates the reagent stain workup. 7. Slides are processed.

43 New workflow after deployment of the automated stainer interface Slide are cut & labeled CoPath Stains Ordered Interface Creates HL7 message Slides placed onto instruments Slide labels scanned IHC Slides processed Special stains

44 From CoPath Printer to Slides to Dako Bypassing Dako Relabeling

45 Results: Phases 1-2 Activity Pick up and sort slides from histology Look up patient name in CoPath and record on sheet Daily QC sheet Program Stainer: Enter name, acc#, path. initials, stain orders Number of Slides Time 12.0 mins. 7.0 mins mins IHC Autostainer #1 44 slides 11.5 mins. - Label slides and place in rack 7.0 mins. IHC Autostainer #2 32 sildes 10.5 mins. - Label slides and place on rack 5.5 mins. IHC Autostainer #3 36 slides 10.0 mins. - Label Slides and place on rack 5.5 mins. Approximate time saved per run: Assuming 2.0 runs per day X 56.5 minutes = Assuming 1.9 hours X 303 days worked per year = 56.5 mins hours Hrs/YR

46 Results: Phase 4 Special Stain Times per Run (with three runs per day) Activity Sort slides and compare to logs Pull controls Deparaffinize Dry Slides Program Stainer: Enter name, acc#, path. initials, stain orders) Assign carousel stations Time 5.0 mins. 1.0 min mins 1.0 min mins. 1.0 min. Approximate time saved per run: Assuming 3.0 runs per day X 7.75 minutes = Assuming 0.39 hours X 303 days worked per year = 7.75 mins hrs Hrs/YR

47 Results With elimination of relabeling the slides and dual order entry through automation markedly decreases assay run time This saves upward of ~700 hours of manual effort per year while eliminating errors, and improving laboratory throughput Increases order accuracy by reducing keystroke errors. Enhances operational efficiency by automating processes. Enforces safe, consistent, efficient handling of specimen.

48 AP Orders Interface Project initiated to transmit AP orders from our EMR to CoPath This will solve several problems: Elimination of unsolicited results as orders will be fulfilled in the EMR Proper encounter selection Routing to provider inboxes Better tracking or AP tissue More efficient accessioning ADT, MD, Part Type, ICD, Clinical History and ask at order entry questions will be transmitted from EMR to CoPath

49 AP Orders Interface Project initiated to transmit AP orders from our EMR to CoPath Estimated impact in preliminary time studies 1.5 minute average decrease in case accession 100,000 cases as a baseline for our math Savings of 2500 hours Minimum of $50,000, direct cost savings Doesn t address complexities of mis-accessioned cases and required resolution Elimination of defect in encounter selection alone will have huge impact

50 Conclusions

51 Conclusions More aspects of anatomic pathology can be automated than are typically envisioned Automation of AP process have significant cost savings Automation of manual process eliminates defects, improves efficiency, and frees up time for value added tasks Elimination of defects results in improved patient safety Electronic orders interfaces to CoPath will markedly impact the accession process as well as clinical customer satisfaction and patient safety

52 References Khalbuss WE, Pantanowitz L, Parwani AV. Digital imaging in cytopathology. Patholog Res Int. 2011;2011: Markin RS. Laboratory automation systems. An introduction to concepts and terminology. Am J Clin Pathol. 1992;98(4 Suppl 1):S3-10. Markin RS, Newcomb MC. Selection of laboratory automation technology: instruments, workcells, and systems. In: Clinical diagnostic technology: the total testing process. Volume 2: The analytical phase. Ward-Cook KM, Lehmann CA, Schoeff LE, Williams RH (eds). AACC Press, Washington, DC. 2005; 16: Najmabadi P, Goldenberg AA, Emili A. Hardware flexibility of laboratory automation systems: analysis and new flexible automation architectures. Clin Lab Med. 2007;27:1-28. Pantanowitz L, Hornish M, Goulart RA. The impact of digital imaging in the field of cytopathology. Cytojournal. 2009;6:6. Sasaki M, Kageoka T, Ogura K, Kataoka H, Ueta T, Sugihara S. Total laboratory automation in Japan. Past, present, and the future. Clin Chim Acta. 1998;278: Zarbo RJ, Tuthill JM, D'Angelo R, Varney R, Mahar B, Neuman C, Ormsby A. The Henry Ford Production System: reduction of surgical pathology in-process misidentification defects by bar code-specified work process standardization. Am J Clin Pathol. 2009;131: College of American Pathologists system review series: Laboratory automation systems & workcells. CAP Today [ Tissue-Tek AutoTEC Automated Embedder Laser Capture Microdissection

53 Automating Anatomic Pathology Questions? J. Mark Tuthill, MD Division of Pathology Informatics, Henry Ford Hospital Detroit, MI Pathology Informatics Summit May, 2015