Decision Support: Improving Quality, Safety and Efficiency through Innovative Use of Information Technologies

Transcription

1 Decision Support: Improving Quality, Safety and Efficiency through Innovative Use of Information Technologies Katherine P. Andriole Brigham & Women s Hospital Department of Radiology Center for Evidence-Based Imaging Harvard Medical School Boston, MA

2 Outline Clinical Decision Support (CDS) Medical Imaging Chain Decision Aids in Medical Imaging CPOE, Protocolling, Image Processing and Analysis, Reporting, Critical Results Visualization / Presentation of Data / BA Creating CDS Content Image/Pixel Data and Non-Image Data

3 Clinical Decision Support (CDS) Clinical Decision Support systems link health observations with health knowledge to influence health choices / management by clinicians for improved health care. Dr. Robert Hayward, Centre for Health Evidence

4 Clinical Decision Support (CDS) Knowledge-Based CDS Consists of knowledge base, inference engine, output communication Knowledge base with rules and associations (eg, IF-Then rules) Non-Knowledge-Based CDS Use Artificial Intelligence (eg, machine learning, neural networks) IBM Watson uses both

5 CDS Systems Historically, the healthcare provider entered the patient data and the CDS system output the right decision, that the provider would simply act upon. Today, the provider interacts with the CDS system utilizing both the clinician s knowledge and the CDS suggestions; the provider decides what information is useful or not & makes the final management decision.

6 Current CDS Systems Interactive decision support designed to assist healthcare professionals with decision making tasks. May use personalized patient data to generate case-specific advice. Presented at the point-of-care

7 Components of Successful Implementations Integrated into the Clinical Workflow Fast and Efficient Intuitive, Ease-to-Use GUIs Context-Sensitive Based on Evidence ; Reference to Sources and Levels of Evidence; Dynamically Updated

8 Patient Data Travel through the EMR Healthcare Enterprise Information Management System Patient Hospital Registration Order Exam Schedule Exam Modality DICOM HL7 Event HL7 Event DICOM Worklist Gateway SQL DICOM HIS Demographics MRN Location HL7 RIS MRN AccNum ExamMNE HL7 DataBase SQL PACS Results Reporting HL7 Report Reporting System Archive DICOM Workstation

9 CDS Along the Medical Imaging Chain Examination Ordering Appropriateness Image Acquisition Optimal Protocol Diagnostic Interpretation Increase Conspicuity Processing, Analysis, Understanding Visualization of Representative Comparative Cases Structured Reporting, Communication, Followup Recommendations Reminders and Alerts

10 Ordering of Imaging Examinations Present standard guidelines as intervention / alert for the Ordering Clinician At the time of ordering At the point of care Must have intuitive GUI, be fast, efficient, automated Show evidence, and source and level of evidence upon which the alert is based.

11 CPOE : Electronic Order Entry Computerized Physician Order Entry Context-specific Decision Support Contraindications, Allergies, etc. Over-, Under-, Misuse Reminders - Duplicate Exams - Inappropriateness with suggestions

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13 Exam Drop-Down Menus

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17 DS for Contrast Allergy

18 DS for Contrast Allergy

19 DS for CT - Headache

20 DS for CT - Headache

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22 MRI Safety Advice

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24 DS for MRI for Low Back Pain

25 US Abdomen RUQ MRI Liver CT Liver Screening HCC PRIOR IMAGING STUDY WITH LIVER NODULE Recommendation: According to the AASLD* guidelines CT scan is not recommended to screen patients for Hepatocellular carcinoma. Please consider Ultrasound + AFP each 6 months. Hepatitis B Hepatitis C Does the patient have LIVER FIBROSIS GRADE iii OR IV? Yes Nodule <1cm Nodule 1-2cm Nodule >2cm Recommendation: According to the AASLD* guidelines MRI is not recommended to screen patients for Hepatocellular carcinoma. Please consider Ultrasound +AFP each 6 months. Recommendation: According to the AASLD* guidelines Ultrasound and Alpha-fetoprotein (AFP) are recommended for screening patients with liver cirrhosis due to hepatitis B and C. Does the patient have LIVER CIRRHOSIS? No Does the patient have ACTIVE DISEASE? No Yes Yes Yes No No Surveillance Recommendation: According to the AASLD* guidelines it is recommended to REPEAT US AT 3-4 MONTHS INTERVALS. Recommendation: According to the AASLD* guidelines it is recommended to perform TWO OF THE FOLLOWING DYNAMIC STUDIES: CT SCAN, MRI OR CONTRAST US. Recommendation: According to the AASLD* guidelines it is recommended to perform ONE OF THE FOLLOWING DYNAMIC STUDIES: CT SCAN, MRI OR CONTRAST US. Does the patient have FAMILY HISTORY OF HCC? No Ultrasound and AFP 2x/year Enlarging No Typical vascular pattern in one technique or Atypical in two Atypical vascular pattern Please select patient RACE: African Asian Other Yes Coincidental Typical vascular pattern Typical vascular pattern or AFP>200ng/ml Age Gender Stable m From database US 2x/year No No No Asian men >40yo Asian women >50yo African >20yo Yes Yes Yes Courtesy of Cleo Maehara, MD, MSc Currently JHU Treat as HCC Biopsy * American Association for the Study of Liver Diseases

26 Medical Imaging Process Image quality is affected by the 5 major components of the medical imaging process: the Patient, the Imaging System, the System Operator, the Image itself, and the Observer.

27 Optimal Imaging Protocol Set of instructions (recipe) for performing an imaging examination -Slice Thickness/ Spacing -IV Contrast Volume / Type / Rate -Oral Contrast Volume / Type -3D, Axial, Coronal, Sagittal -Modality (CT or MRI)

28 Protocolling process 1. Patient worklist 2. Clinical history Lab tests 3. Order Contrast 4. Prior imaging Courtesy of Cleo Maehara, MD, MSc Currently JHU

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31 Medical Imaging Process Image quality is affected by the 5 major components of the medical imaging process: the Patient, the Imaging System, the System Operator, the Image itself, and the Observer.

32 Types of Advanced Processing For Computer Analysis Processing of scene data for autonomous machine vision/perception For Human Visualization Improvement of pictorial information for human perception

33 Processing for Machine Analysis Transform for computer to analyze Focuses on procedures for extracting information from an image in a form suitable for computer processing Result often bears little resemblance to visual features humans use in interpreting image content. Example: computer-aided detection (CAD)

34 Image Processing & Analysis Improve Conspicuity of Lesions Processing to make image subjectively visually better. Many of the techniques used are related to our understanding of the human visual system and perception enhance object boundaries, manipulate brightness & contrast. May improve diagnostic interpretation.

35 Tools for Improvements of Perception Standardization of Medical Images Known anatomy/pathology Standard Viewing Projections, Viewing Positions & Display Windows Supporting visual aids Image processing & manipulation CAD & CADx Multi-modality image integration/fusion Use of Color and/or Motion

36 Medical Imaging Process Image quality is affected by the 5 major components of the medical imaging process: the Patient, the Imaging System, the System Operator, the Image itself, and the Observer.

37 Types of Decision Aides for the Radiologist Information Resources Reference Materials Relevant Clinical Data not in PACS Integrate image findings with clinical context Image Examples for Pattern Recognition By Human: View Case-Examples By Machine

38 Dynamic Aides Information on-demand for decision making tasks, often rule-based decision support»hierarchical Trees If-Then Logic»Semantic Nets»Matching»Bayesian Networks (probabilistic)»artificial Neural Networks (train/learn)»case-based Reasoning

39 On-Line Real-Time Accessible Diagnostic Workup Strategies Imaging Teaching Files / Interesting Cases Road-Maps / Flow Charts Logic Decision Tables

40 Exemplary Case Files

41 Differential Diagnosis

42 Literature & Information Searches with Images & Radiological Focus ARRS GoldMiner (Am Roentgen Ray Soc) Provides access to images published in selected peer-reviewed radiology journals. Search by findings, anatomy, imaging technique, patient age/sex, abbreviations peer-reviewed radiology images searchable in 10 languages!

43 Literature & Information Searches with Images & Radiological Focus Understands medical vocabulary using NLM searches of free-text figure captions to retrieve relevant images. Incorporates Medical Subject Heading (MeSH) terms like MEDLINE & PubMed. Uses UMLS Metathesaurus and SNOMED. ARRS GoldMiner

44 Literature & Information Searches with Images & Radiological Focus Yottalook TM Free Medical Imaging Search Engine: References, Teaching Files, Peer-Reviewed Images. Searches Web, Books, On-Line Anatomical Atlases based on Google s indexing with a relevance algorithm by ivirtuoso.

45 Literature & Information Searches with Images & Radiological Focus Based on 3 core technologies Natural Query Analysis: of search term to understand what the radiologist is searching for. Semantic Ontology: Language, thesaurus, synonyms, relationships between terms. Relevance Algorithm: differentiates medical terms from other words to create rankings.

46 Medical Image Databases /Example Case Files Medical Imaging Teaching Files (eg, instances of RSNA MIRC and others) On-line Resources (eg, StatDX) Use of RadLex lexicon for uniform indexing and retrieval of radiologic content.

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49 Integrated Clinical Workstation

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51 Integrated Clinical Workstation

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53 Integrated Advanced Processing

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55 Big Data Visualization CAVE2 University Illinois

56 Same mean, variance, correlation, and linear regression line Uncovering the message in the data.

57 Courtesy Shira Fischer, MD, PhD Beth Israel Deaconess Medical Center Harvard Medical School, BIRT Fellow

58 View Labs Per Organ & Location Tremper, patent pending

59 Wongsuphasawat K, Shneiderman B, ed. LifeFlow: visualizing an overview of event sequences. Proc SIGCHI Conference on Human Factors in Computing Systems; 2011: ACM. 59

60 Reporting Decision Support Intervenes on the Radiologist Reminders for inclusion of pertinent positives and negatives eg. PQRI Stroke Reporting: presence or absence of hemorrhage, mass and acute infarction Structured Reporting and Templates

61 Other Decision Support Aides Communication of Critical Test Results Ticklers, Reminders and Worklists For pending test results For Follow-up

62 Critical Results Communication Follow-Up Recommendations ANCR : Alert Notification of Critical Results Began as Radiology project; now used in Pathology and in Cardiology. Utilizes Web Services (XML SOAP) to integrate to PHS Results Manager (EMR) (which stores Labs, Notes), PHS Paging Directory (PPD), PHS Enterprise Patient Lists (PEPL) Luciano MS Prevedello, MD, MPH & Stacy D. O Connor, MD currently Ohio State University MC & BWH respectively

63 Radiology Example

64 Breast Imaging to Surgeons

65 Echo Cardiology Policies

66 Create Follow-Up Recommendation Alert

67 Create Follow-Up Recommendation Alert

68 Pulmonary Nodule

69 Create Follow-Up Recommendation Alert

70 Business Analytics for Departmental Administration, Operations, Safety Combining imaging data and other relevant non-imaging data to visualize trends, detect gaps, draw correlations. Can be used for operational performance metrics and reporting, as well as clinical.

71 Business Analytics Identify variation within our centers, standardize protocols, optimize dose Prevedello, Warden, Ikuta, O Connell, Bagheri, Sodickson

72 Business Analytics

73 Creating or Compiling Evidence Randomized Controlled Clinical Trials Peer-reviewed Publications Society Guidelines Consensus Local Best Practices

74 Data Mining

75 Discovering / Compiling Evidence Data Mining w/data as the Guide Algorithms Learn from the Data Classification, Clustering, Structured Prediction, Feature Learning, Supervised Learning, Regression, Decision Trees, Bayes, Neural Networks, Support Vector Machine

76 Machine Learning & Data Mining Machine Learning: focuses on prediction based on known properties learned from training data. Data Mining: focuses on the discovery of previously unknown properties in the data.

77 Image Processing & Analysis Much More to Be Done Quantitative Measures International Collaborative Activities Image Feature Tagging (eg, AIM) Enabling Pixel Data Content Search Machine / Signal Processing

78 Image Processing & Analysis Analyze Metadata and Pixel Data Used for Decision Support CAD, Alerts, Evidence-Based Patient Management Used for Patient Personalization of Diagnosis and Treatment Protocols

79 Healthcare Big Data Structured EHR Data Unstructured Clinical Notes & Reports Medical Imaging Data Genetic Data Behavioral & Social Data Epidemiological Data Evidence-Based Practice Data

80 Transforming Healthcare Safety & High Quality Efficiency / Cost-Effective Predictive Analytics Patient-Centric / Personalized Medicine Precision Medicine / Quantitative Evidence-Based Decision Support

81 Medical Imaging Process Image quality is affected by the 5 major components of the medical imaging process: the Patient, the Imaging System, the System Operator, the Image itself, and the Observer. Protocolling Image Processing Interpretation Reporting

82 Medical Imaging Decision Support Examination Ordering Appropriateness Image Acquisition Optimal Protocol Diagnostic Interpretation Increase Conspicuity Processing, Analysis, Understanding Visualization of Representative Comparative Cases Reporting, Communication, Follow-up Recommendations Reminders and Alerts

83 Summary Clinical Decision Support Systems for Imaging Can aid the ordering clinician, radiological technologists and radiologists in performing their tasks by providing patient-specific information, recalling general medical knowledge, and integrating such information to recommend a course of action.

84 Informatics