1  Organization, Roles, and Skills  Methodology  Standards Analysis  Tool Evaluation Terminology Collaboration Business Plan  Project Identification  Mission Effect Analysis  Financial Effect Analysis  Risk Analysis  Business Case  Goals and Objectives  High-Level Requirements  Architecture  Process Design  Functional Requirements  Non-Functional Requirements  Infrastructure Deployment  System Configuration  Testing Phased Implementation Solution Outline Validation Whitepaper Knowledge and Vocabulary Management Methodology

2 Data Standards Development

3 Change Proposal Process

4 Tools

5 System Context

6

7

8

9

10 System Context

11

12 Terminology Management

13 Why Terminology Management? Expansion and growth Localized decision making for system selection and configuration Multiple versions of the electronic medical record Fragmentation of patient information within the medical record, something that Dr. Plummer’s unified paper medical record was created to eliminate

14 Why Terminology Management? Differing medical records, clinical systems, and terminology have resulted in diverse clinical processes in the delivery of care. Provides considerable challenge as we seek Enterprise-wide improvements in outcomes and safety of care Provision of the best of the entire Mayo Clinic for each patient Efficiencies of operation and public reporting

15 Why Terminology Management? Standardization of Clinical Systems Policies Core Clinical Processes Terminology Develop and access standardized evidence-based protocols and guidelines Promote accuracy and Consistency of Patient Care Delivery Increase Access to Patient Data Decrease Costs Improve Patient Outcomes

16 Why Terminology Management? “…no matter where a patient is at Mayo Clinic, that patient should know all the Mayo Clinic resources are at his or her disposal.” “…a promise to our patients, staff, and other customers that we are organized and function as a system with a single purpose: our mission. And the mission, as we all know, is to provide the best care to every patient every day…” - Glenn Forbes, M.D.

17 Clinical Examples

18 Clinical Notes Document Naming Problem Many different ways to view clinical note documentation (nearly 10 systems!) Standards are implemented at a site, not enterprise level Record review is inefficient and complex How do I navigate? Do I have the entire record? How do we aggregate data? Can we?

19 Clinical Notes Document Naming Scope Develop enterprise naming standard for Clinical Notes Documents in EMR. Develop standardized value sets for each component of naming standard

20 Clinical Notes Document Naming

21 Clinical Notes Document Naming

22 Clinical Notes Document Naming

23 Clinical Notes Document Naming

24 Vital Signs Need Identify and apply best practices (policies, processes, and terminology) across Mayo Clinic to promote accuracy and consistency of care delivery

25 Vital Signs - Scope Height and Weight Temperature Respiratory Rate Pulse Blood Pressure Oxygen Saturation Pain Score Body Mass Index, Body Surface Area Head Circumference Fetal heart tone/rate monitoring with qualifiers (OB patients) Central Venous Pressure plus monitored

26 Vital Signs

27 Nursing Assessments Business Need Identify standard value sets for use by EMR applications Provide a semantic context for the value sets and align the model to the Enterprise Data Model Document a gap analysis between existing and standard terminologies to identify areas for further work

28 Business Information Model

29 Enterprise Context EDM Concept Unified Model New

30 Unified Nursing Assessment Model EDM Concept Unified Model Problem Specific

31 Problem-Specific Models Unified Model Problem Specific

32 Data Model to Vocabulary Vocabulary Data Model

33 Inpatient Pain Management Problem Inpatient pain service wanted to proactively find patients with unresolved pain, reduce dependency on manually generated referrals Solution Reported pain scores recorded in EMR Data replicated into analytic databases Nightly report was generated to identify patients whose reported pain scores did not fall below a specified value Clinical guidelines were applied Multi-disciplinary provides patient care (Clinical Nurse Specialists, Pharmacists, Pain Clinic)

34 Inpatient Pain Management Solution illustrates Simple use of controlled terminology for patient focused data capture and inference (data) Information delivered to multi-specialty practice team about a dynamic patient population (information) Actions taken by multidisciplinary team (knowledge driven care)

35 Problem List Problem ICD 9 standard (for billing) does not adequately meet the needs of the practice to define a clinical problem Limits workflow and automated decision support Would like to map terms to multiple standards Approach Enterprise data modeling Process analysis and design Data and system requirements

36 Race and Ethnicity Need for enterprise standardization Research Meet internal and external (e.g., funding agencies) research requirements Education Conform to The US Department of Education (USDOE) accreditation requirements Practice Collect the data necessary to assure diversity does not create disparities in care and every patient receives the best care at Mayo

37 Laboratory

38 Laboratory – System Integration Transactions to:RES Type:13ASTM From:MNL On:10/02/2001 TX Seq Transaction Data MSH|^~&\|Antrim|MNL|A |MML| |C |ORU^R01||P|2.2 PID|1| | | |HANSEN^JEANE M || |F|| |||1001:PD00011R|||| ORC|RE|K OBR|1|K ||9387^PTH Whole Molecule, ||| |||C ^Immanuel-St Josephs Hosp^ |N|||||^STOROLE Storvic||L |1001:PD00011R||||C|CH|A||^^^^^R~R OBX|1|ST|2238^Calcium^ROCLIS||8.9|mg/dL| |N|||F| ||| NTE|1|N^| OBX|2|ST|7596^Calcium, S^ROCLIS||DNR|mmol/L| |N|||F| ||| NTE|1|N^| (Females > or = 19 years) OBX|3|ST|2239^Creatinine^ROCLIS||5.8|mg/dL| |h|||F| ||| NTE|1|N^| (Females > or = 9 years) OBX|4|ST|2240^Phosphorus^ROCLIS||3.9|mg/dL| |N|||F| ||| NTE|1|N^|

39 Laboratory – System Integration Transactions to:RES Type:13ASTM From:MNL On:10/02/2001 TX Seq Transaction Data MSH|^~&\|Antrim|MNL|A |MML| |C |ORU^R01||P|2.2 PID|1| | | |HANSEN^JEANE M || |F|| |||1001:PD00011R|||| ORC|RE|K OBR|1|K ||9387^PTH Whole Molecule, ||| |||C ^Immanuel-St Josephs Hosp^ |N|||||^STOROLE Storvic||L |1001:PD00011R||||C|CH|A||^^^^^R~R OBX|1|ST|2238^Calcium^ROCLIS||8.9|mg/dL| |N|||F| ||| NTE|1|N^| OBX|2|ST|7596^Calcium, S^ROCLIS||DNR|mmol/L| |N|||F| ||| NTE|1|N^| (Females > or = 19 years) OBX|3|ST|2239^Creatinine^ROCLIS||5.8|mg/dL| |h|||F| ||| NTE|1|N^| (Females > or = 9 years) OBX|4|ST|2240^Phosphorus^ROCLIS||3.9|mg/dL| |N|||F| ||| NTE|1|N^|

40 Laboratory – System Integration Transactions to:RES Type:13ASTM From:MNL On:10/02/2001 TX Seq Transaction Data MSH|^~&\|Antrim|MNL|A |MML| |C |ORU^R01||P|2.2 PID|1| | | |HANSEN^JEANE M || |F|| |||1001:PD00011R|||| ORC|RE|K OBR|1|K ||9387^PTH Whole Molecule, ||| |||C ^Immanuel-St Josephs Hosp^ |N|||||^STOROLE Storvic||L |1001:PD00011R||||C|CH|A||^^^^^R~R OBX|1|ST|2238^Calcium^ROCLIS||8.9|mg/dL| |N|||F| ||| NTE|1|N^| OBX|2|ST|7596^Calcium, S^ROCLIS||DNR|mmol/L| |N|||F| ||| NTE|1|N^| (Females > or = 19 years) OBX|3|ST|2239^Creatinine^ROCLIS||5.8|mg/dL| |h|||F| ||| NTE|1|N^| (Females > or = 9 years) OBX|4|ST|2240^Phosphorus^ROCLIS||3.9|mg/dL| |N|||F| ||| NTE|1|N^|

41 Laboratory – System Integration Transactions to:RES Type:13ASTM From:MNL On:10/02/2001 TX Seq Transaction Data MSH|^~&\|Antrim|MNL|A |MML| |C |ORU^R01||P|2.2 PID|1| | | |HANSEN^JEANE M || |F|| |||1001:PD00011R|||| ORC|RE|K OBR|1|K ||9387^PTH Whole Molecule, ||| |||C ^Immanuel-St Josephs Hosp^ |N|||||^STOROLE Storvic||L |1001:PD00011R||||C|CH|A||^^^^^R~R OBX|1|ST|2238^Calcium^ROCLIS||8.9|mg/dL| |N|||F| ||| NTE|1|N^| OBX|2|ST|7596^Calcium, S^ROCLIS||DNR|mmol/L| |N|||F| ||| NTE|1|N^| (Females > or = 19 years) OBX|3|ST|2239^Creatinine^ROCLIS||5.8|mg/dL| |h|||F| ||| NTE|1|N^| (Females > or = 9 years) OBX|4|ST|2240^Phosphorus^ROCLIS||3.9|mg/dL| |N|||F| ||| NTE|1|N^|

42 Laboratory – System Integration Transactions to:RES Type:13ASTM From:MNL On:10/02/2001 TX Seq Transaction Data MSH|^~&\|Antrim|MNL|A |MML| |C |ORU^R01||P|2.2 PID|1| | | |HANSEN^JEANE M || |F|| |||1001:PD00011R|||| ORC|RE|K OBR|1|K ||9387^PTH Whole Molecule, ||| |||C ^Immanuel-St Josephs Hosp^ |N|||||^STOROLE Storvic||L |1001:PD00011R||||C|CH|A||^^^^^R~R OBX|1|ST|2238^Calcium^ROCLIS||8.9|mg/dL| |N|||F| ||| NTE|1|N^| OBX|2|ST|7596^Calcium, S^ROCLIS||DNR|mmol/L| |N|||F| ||| NTE|1|N^| (Females > or = 19 years) OBX|3|ST|2239^Creatinine^ROCLIS||5.8|mg/dL| |h|||F| ||| NTE|1|N^| (Females > or = 9 years) OBX|4|ST|2240^Phosphorus^ROCLIS||3.9|mg/dL| |N|||F| ||| NTE|1|N^|

43 Laboratory – System Integration Transactions to:RES Type:13ASTM From:MNL On:10/02/2001 TX Seq Transaction Data MSH|^~&\|Antrim|MNL|A |MML| |C |ORU^R01||P|2.2 PID|1| | | |HANSEN^JEANE M || |F|| |||1001:PD00011R|||| ORC|RE|K OBR|1|K ||9387^PTH Whole Molecule, ||| |||C ^Immanuel-St Josephs Hosp^ |N|||||^STOROLE Storvic||L |1001:PD00011R||||C|CH|A||^^^^^R~R OBX|1|ST|2238^Calcium^ROCLIS||8.9|mg/dL| |N|||F| ||| NTE|1|N^| OBX|2|ST|7596^Calcium, S^ROCLIS||DNR|mmol/L| |N|||F| ||| NTE|1|N^| (Females > or = 19 years) OBX|3|ST|2239^Creatinine^ROCLIS||5.8|mg/dL| |h|||F| ||| NTE|1|N^| (Females > or = 9 years) OBX|4|ST|2240^Phosphorus^ROCLIS||3.9|mg/dL| |N|||F| ||| NTE|1|N^|

44 Referral Optimization Project Enterprise Data Trust Institutional Appointment Reporting Categories Enterprise Category Code Enterprise Category Code Description MCR Reporting CategoriesMCJ Reporting CategoriesMCA Reporting Categories ADMAdministrative Non-patientNon-Patient UNDUndifferentiatedSlot OnlyOPEN CONConsultConsult/Limited Exam Consultation/PAME Consult/PAME MEMulti-System EvaluationMulti-system Evaluation Evaluation Direct SVSubsequent Visit Return Visit/ Acute IllnessEstablished VIRTVirtual Visit Virtual Visit (Future) OTHOtherOther (Receptionist Only Visit, Research Visit, Educational Visit), Procedure (Blood Test, Procedure/Diagnostic Testing, Radiology, Specimen Collection, Therapy) AncillaryOther – Registration, Receptionist Visit, Procedure, Ancillary

45 Patient Focused Data Capture Point of care knowledge execution Practice Based Evidence Evidence Based Practice Clinical Guidelines Clinically Derived Knowledge Bases Expert Systems Transactional Databases Inform Research, evaluation, & performance measurement Storage and Processing Consolidate with other evidence, other knowledge bases Analytic Data Repositories Replication and modeling Data Inference Knowledge Management Decision support Vision Terminology

46 Questions?