TITLE Dori Whittaker, Director of Solutions Management, M*Modal

Transcription

1 TITLE Dori Whittaker, Director of Solutions Management, M*Modal

2 Challenges Impacting Clinical Documentation HITECH Act, Meaningful Use EHR mandate and adoption Need for cost savings Migration to ICD 10

3 Clinical Documentation Paradigms MRN: DOS: 09/11/2009 CHIEF COMPLAINT: Patient is a 25 year old woman complaining of feeling frequently fatigued. She reported also occasional dizziness, sleeping difficulties and morning headaches. OBJECTIVE: Recent bout with the flu. PHYSICAL EXAMINATION: Vital signs are normal with a blood pressure of 120/80, pulse 62, temperature 98.6 degrees, weight 108 pounds. ASSESSMENT: Although flu symptoms were in remission, patient has not fully recovered yet. PLAN: Place patient on Biaxin for the next two weeks. The patient will call us if there is no improvement, any worsened or new symptoms. Dictation with Transcription Fast and easy, expressive Transcription can be expensive Longer turn around times Documents are neither structured nor encoded Compliance to clinical document best practices hard to enforce Must dictate everything including items available in chart (e.g. Medications) Direct Data Entry by Physician Structured and encoded information Physician time is expensive Tedious manual process Data capture limited to anticipated fields Documentation lacks expressiveness of natural language Easier to enforce compliance to clinical document best practices Can insert items from chart (e.g. Medications), but you might not know why the patient is not taking their medications

4 Progress of Clinical Documentation Transition to EHR

5 Time Needed to Create Patient Documentation

6 Drawbacks of Increased Time to Document Can be more expensive than transcription Physicians that are spending half of their time documenting may see less patients per day which impacts revenue Physicians have very little time, so tend to take documentation short cuts by: Using texting language Copying pasting Pulling forward

7 Drawbacks of Increased Time to Document Risks to quality and patient care, revenue and compliance must be weighed against cost of transcription or investment in efficient frontend speech recognition technology to encourage quality narrative Cost of decreased compliance Dangerous abbreviations Cost of risk to patient safety Real dangerous abbreviations (using texting language) Inappropriate copy/paste Condition that was ruled out, for example ends up getting copied forward Cost of decreased patient satisfaction Physician focus on computer Treatment by template; not examination

8 Patient satisfaction counts JAMA 2012; 307 (23):

9 Drawbacks of Increased Time to Document Quality and patient care issues Unlike traditional transcription where someone is making sure the text is accurate on the back end before it is made available for patient care Now Coders often become the quality assurance process which can be after the patient has left the facility and not in time to mitigate patient care issues as a result of quality Due to quality problems with documentation in the EHR, some facilities are bringing their transcriptionists back to review documents in the EHR The EHR is not optimized for transcription and so it is difficult to verify what the physician intended (i.e. no audio file)

10 Drawbacks of Increased Time to Document Most physicians have neither the patience nor temperament to create a documentation record that optimally communicates all of the necessary clinical information to other care givers to permit optimal hand offs This may potentially undermine optimal quality, safety, or service outcomes Meaningful Use Problem list Quality» Core measures» PQRS Clinical Decision Support ICD 10 Specificity ~5 times the number of Diagnosis codes ~19 times the number of Procedure codes Impact of data entry Accountable care and value based reimbursement

11 What isn t working? Continuing with fully dictated reports: Is not sufficient in unstructured narrative Is not efficient even if structure can be applied via Natural Language Processing (NLP) Compliance to clinical document best practices hard to enforce Must dictate everything including items available in chart (e.g. Medications) Relying solely on structured data entry via EHRs Is not sufficient due to the cost of physician time and quality

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14 Weighing the Benefits of a Better Solution Decreased transcription costs against cost of physician time Transcription workflows must be lean, economical and effective Transcription turnaround times must be reduced to be in line with the expectations facilities are held to from a patient care and length of stay perspective Potential degradation of quality and completeness of record against encouraging narrative Options to use front end speech recognition with self edit or send to transcription to include the physician s: Thought process; Reasoning behind decisions; and "If/then" statements that could guide decision making Benefits of faster TAT with physician documentation in EHR against: Offering a combination of EHR templating with narrative for more complex sections (e.g. Partial Dictation with send to transcription): HPI, Findings, Assessment Inpatient Discharge Summary sections Options to allow collaborative documentation creation e.g. Physician Assistant starts the document and Attending physician follows with other narrative sections

15 Value of Encouraging Narrative The value of combining quality narrative with EHR structure and collaborative documentation creation will result in: Improvement in patient care and reduction of time for physician Options that are conducive to the use of narrative for free expression of the patient s story Send to transcription Front end speech recognition Improvement in quality and appropriate reimbursement to offset the cost of documentation Use of Natural Language Understanding (NLU) technology to structure the narrative for use with CDI, ICD10 and Quality of Care goals Use of NLU technology to provide notification to physician of more specificity needed at the time of document creation and upon review when returned from transcription

16 Using Technology to Create Structure from Narrative Using HL7 CDA it is possible to structure and encode (SNOMED CT) narrative using Natural Language Understanding (NLU) technology

17 Using Technology to create structure from Narrative Relevant Technologies Natural Language Understanding (NLU): Technology that enables computers to derive meaning from natural human language as found in medical documentation Semantic Reasoning: Technology to infer useful consequences ( actions ) from asserted clinical facts Neither technology is perfect, so any useful solution requires humans in the loop

18 Using Technology to create structure from Narrative Natural Language Understanding (NLU) Syntax grammatical structure of sentences Semantics word meanings and relations Pragmatics context contributing to meaning SUBJ V PT NEG ANATOMY SYMPT TIME she has no chest pain today Who? Where? When?

19 Using Technology to create structure from Narrative Natural Language Understanding (NLU) Word sense disambiguation: Patient suffers from severe depression. Electrocardiogram shows ST depression in lead 5. Expressions of certainty: diagnosis of pneumonia doubtful at this point nausea and vomiting possibly indicating concussion Controlled Medical Vocabularies Taxonomies Ontologies (SNOMED CT)

20 Contextual Understanding In addition to identifying clinical concepts and assigning a SNOMED code, NLU also recognizes the context in which a clinical concept is documented By assigning a contextual modifier along with a SNOMED code, users can find results in correct contexts, and not any result with a matching SNOMED concept Example: Congestive Heart Failure The following slides show the same query looking for documents mentioning Congestive Heart Failure (SNOMED code ) within different contexts Certainty Certain vs. Maybe vs. Hypothetical vs. Negative Subject Patient vs. Non Patient

21 Certainty Certain.problem code=snomed CT/ , certainty=certain

22 Certainty Maybe.problem code=snomed CT/ , certainty=maybe

23 Certainty Maybe.problem code=snomed CT/ , certainty=maybe

24 Certainty Hypothetical.problem code=snomed CT/ , certainty=hypothetical

25 Certainty Negative.problem code=snomed CT/ , certainty=negative

26 Subject Family vs. Patient.problem code=snomed CT/ , subject=nonpatient

27 How many different ways are there to say that a patient has cancer? Using the SNOMED hierarchy, when searching for neoplasm we can find all related concepts. Then, add in patient, subject, and temporality contextual modifiers to ensure we only find relevant results, not any document mentioning cancer.

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30 Examples of how to build upon these concepts to solve clinical problems 1. Retrospective Analysis Population Management 2. Concurrent Review Clinical Documentation Improvement 3. Real Time Computer Assisted Physician Documentation

31 Retrospective Analysis Population Management Looking across patient populations to identify care gaps

32 Abdominal Aortic Aneurysm Identify the real AAA patient population according to radiology report narrative Reconcile with those patients already known about to identify those that are falling through the cracks of care Stratify results by aneurysm size to prioritize follow up

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35 Concurrent Analysis Clinical Documentation Improvement (CDI) Improve CDI Specialists workflow by showing them where in the encounter documentation conditions are indicated or underspecified

36 Building an Evidence Sheet Turn the medical record inside out by looking for underspecified conditions and relevant evidence to aid in decision making and chart review workflow Ability to see what is present in documentation and what was looked for but not found The following three slides show for one patient the presence of 5 important conditions in the current inpatient encounter, 3 of which are lacking documentation specificity or evidence.

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40 Real Time Understanding Computer Assisted Physician Documentation (CAPD) Present feedback to physicians at time of documentation to ensure documentation is complete, accurate, and compliant and drive patient care and outcomes EHR feedback alerts that do not use Natural Language Understanding are based only on the structured data entered discretely and not the complete patient story They often become meaningless and intrusive and providers may suffer from alert fatigue The only way to address the issue of feedback alert fatigue is to incorporate narrative documentation into the alert data and process it as caregivers do

41 CAPD Use what is learned from Concurrent and Retrospective analysis to drive real time feedback to physicians Ensure documentation is complete, accurate, and compliant at time of capture prevent CDI and Coding queries Make documentation actionable

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45 Collaborative Intelligence through Structuring of Narrative Data Engage Capture Narrative Through Speech Understanding: Using any Device Within EHR Systems Getting Feedback to Improve the Quality of Documentation Population Data Patient Story Act Derive Insights That Drive Actions: Coding for Billing, ICD 10 Clinical Documentation Improvement Quality Reporting Meaningful Use Improved Outcomes Collaborate Collaborate on Concise Patient Story: Interoperability across IT Systems Contextualized & Customized Views

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