Advanced Analytics for Healthcare Insights
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Lecture at World Health Organization - Family of International Classifications Network Annual Meeting 2014
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Advanced Analytics for Healthcare Insights
- 1. © 2014 IBM Corporation Xavier Constant Núñez – Business Analytics Architect October - 2014 Advanced Analytics for Healthcare Xavier Constant xavier.constant@es.ibm.com WHO-FIC Network Annual Meeting October 2014
- 2. © 2014 IBM Corporation For Healthcare Institutions, harvesting the data profusion with analytics opens up new sources of value What patient-centric factors are most predictive of outcomes from a treatment regimen ? What patient-level interventions could help improve treatment adherence ? How can you provide clinicians with targeted assistance ? How can you best meet the information and support needs of patients and caregivers? What channels of communication work best ? … and many more Physician notes and discharge summaries Patient history and symptoms Pathology reports Tweets, text messages and online forums Satisfaction surveys Claims and case management data Forms-based data and comments Emails and correspondence Trusted reference journals and portals Paper based records and documents * AIIM website, accepted industry percentage Over 80% of stored health information is unstructured* Data from a variety of sources is now available for harvesting. These sources generate huge volumes of data … … and can help identify significant points of leverage 15 petabytes Amount of new information created each day - eight times more than the information in all US libraries Clinical Outcomes Operational Outcomes Health data growing 35% per year*
- 3. © 2014 IBM Corporation time spent manually interpreting data would become time spent healing patients. •Aggregate, activate and enrich relevant patient information beyond what is known •Surface earlier, more accurate insights to drive incipient intervention opportunities •Adaptive and proactive delivery drives individualized, patient centered care Confirm what I think or suspect? Show me something new or unexpected? How many are being missed? How do we move faster and anticipate change? If we could only activate the relevant information to bring insights to the point of care when needed most Knowledge, guidelines and best practice measures Adapt care to changing conditions and new information Indentify intervention opportunities Longitudinal “data driven” insights Information should aid us, not lie hidden and dormant
- 4. © 2014 IBM Corporation Carilion Clinics flags patients at risk for developing Congestive Heart Failure (CHF)
- 5. © 2014 IBM Corporation Use of Content Analytics to enrich the Predictive Models used to identify patients at risk for developing CHF
- 6. 66 © 2014 IBM Corporation #ibmiod Patient Age: 65 Gender: Male Race: White Diagnosi s Melanoma Stage: 2 Social Marital status: single Labs AJCC: T2 Risk of metastasis 47% Content Analytics Predictive Analytics Recommended Add’l Treatment DTIC Similarity Analytics Care Manager 100’s or 1000’s of patients 100’s or 1000’s of patients One Patient Goals Avoid remission Activities Avoid UV radiation Regular screening Transportation assistance A 65-year old white male has been diagnosed with stage 2 melanoma. He is widowed and lives alone. AJCC: T2 Raw Information (e.g. EMR and Claims) 10’s of thousands of patients 6 Technical Solution Overview Capture, Analyze, Activate
- 7. 77 © 2014 IBM Corporation #ibmiod Examples of NLP Challenges in Healthcare • Accurately identify and extract facts from text including negation “55%” = LVEF “Patient does not show signs” = Negative Symptom • Accurately interpret and assign values to ambiguous statements “around 55%” = LVEF “Shows slightly elevated levels” = if condition A = 10%, if condition B = 20% • Infer meaning from non-contextual content “Cut back from two packs to one per day” = Smoker • Cleanse, enhance and normalize raw data “Myocardia infarction” and “heart attack” = equal same thing Correct misspellings and abbreviations through NLP Enhance or augment by assigning correct RxNorm, SNOMED, ICD-10 or other codes / terminology • Preserve and structure facts and concepts from contextual content: 7 A 42-year old white male presents for a physical. He recently had a right hemicolectomy invasive grade 2 (of 4) adenocarcinoma in the ilocecal valve was found and excised. At the same time he had an appendectomy. The appendix showed no diagnostic abnormality. Patient Age: 42 Gender: Male Race: White Procedure hemicolectomy diagnosis: invasive adenocarcinoma anatomical site: ileocecal valve grade: 2 (of 4) Procedure appendectomy diagnosis: normal anatomical site: appendix Content Analytics
- 8. 88 © 2014 IBM Corporation #ibmiod Content Analytics Applied to Improve Patient Outcomes 8 Content Analytics Language IdentificationLanguage Identification Spell checkingSpell checking Lexical analysisLexical analysis Part of Speech, DisambiguationPart of Speech, Disambiguation Named Entity RecognitionNamed Entity Recognition Part of a SentencePart of a Sentence Semantics (Relationships), DisambiguationSemantics (Relationships), Disambiguation Synonims, ConceptsSynonims, Concepts Objectiveness: opinion, doubt, fact, questionObjectiveness: opinion, doubt, fact, question IdeasIdeas Indexing Evolving topics Sentiment Analysis Interest analysis life-events QA Deep QA (Watson) Brand Analytics Classification Custom applications text-analytics NLPDocument Summarization
- 9. © 2014 IBM Corporation Enrich & Improve Predictive Models with information trapped in unstructured data • Content Analytics capabilities : • Trend, Pattern, Anomaly, Deviation and Context Analysis • Medical Fact, Relationship and Outcome Annotation • Healthcare Accelerators speed time to value: • Annotators focused on extracting medical terms • Approximately 800 pre-built rules developed in IBM Content Analytics Studio • Included diagnoses, procedures, labs, many drugs • Transforming unstructured data to CPT, ICD9, and SNOMED concept ID outputs • Detecting negations • Detecting family histories Content Analytics
- 10. © 2014 IBM Corporation Enrich & Improve Predictive Models with information trapped in unstructured data Content Analytics
- 11. © 2014 IBM Corporation Real case: Readmissions at Seton The Data We Thought Would Be Useful … Wasn’t • 113 candidate predictors from structured and unstructured data sources • Structured data not available, not accurate enough without the unstructured content • Unexpected Indicators Emerged … Readmission is a Highly Predictive Problem! • 18 population specific predictors surface previously unknown intervention opportunities 11 Unstructured data unlocks hidden insights Predictor Analysisc % Encounters Structured Data % Encounters Unstructured Data Ejection Fraction (LVEF) 2% 74% Smoking Indicator 35% (65% Accurate) 81% (95% Accurate) Living Arrangements <1% 73% (100% Accurate) Drug and Alcohol Abuse 16% 81% Assisted Living 0% 13% Content Analytics
- 12. 1212 © 2014 IBM Corporation Output: alerts predictions recommendations categorization visualization … Training Analysis Medical health records / provided services / lab tests Statistical Prediction Models 2. Analysis – runtime process 1. Learning A new Patient / change in disease state / new condition Periodic Training: Learn from the latest data Collect real data Predictive Analytics Predictive Analytics
- 13. 1313 © 2014 IBM Corporation Representing Patients using Information Obtained from Multiple Sources of Data Feature Extraction Feature Extraction Patient Feature Vector x1 xN x2 Patient Predictive Analytics
- 14. 1414 © 2014 IBM Corporation 14 IBM Confidential 18 de desembre de 2014 Similarity Visualization Patient Representation Similarity Identification Patient Similarity Analytics - Given an index patient, find clinically similar patients Similarity Analytics
- 15. © 2014 IBM Corporation Congestive Heart Failure (CHF) Onset Prediction (Results achieved in 6 weeks)
- 16. © 2014 IBM Corporation IBM Advanced Care Insights provide analytical capabilities enabling holistic, individualized approaches to Smarter Care Objective Find clinically similar patients for decision support and Comparative Effectiveness Patient Similarity Objective Identify most effective treatment option for a given patient Personalized Comparative Effectiveness Predict Patient Clinical Pathway Patient / Provider Matching Visualize Population Cohorts Visualize Disease Pathways Predictive Analytics Multimodal Longitudinal Patient Data (e.g. Structured + Unstructured [text, image, genetics, …], potentially social media) Chance of Adverse Event = 80% X months Objective Analyze patient’s longitudinal records to model the future risk of developing adverse conditions Objective Predict and visualize patient disease progression Objective Visualize populations through interactive multi-dimensional exploration of inter- cluster and intra-cluster relationships Objective Match patients with providers based on similarity analytics and optimal performance characteristics` Differentiating Identifying Challenging Patients
- 17. © 2014 IBM Corporation Thank You
Editor's Notes
- AIIM (Association for Information and Image Management Doc clásica: historias clínicas, resultados de laboratorio, pruebas diagnosticas, medicación (prescrita/consumida), … Publicaciones médicas, portales, foros… Sensores Points of leverage (influenciar) Lo importante es si toda esta información nos ayuda a tomar las mejores decisions posibles
- En esta marea de información se puede dar la paradoja del náufrago: estar rodeado de agua y morirse de sed Los sistemas analíticos han de extraer de esta marea únicamente la información relevante en el momento adecuado. No vale con presenter cuadros de mando complejos con decenas de indicadores (se pierde tiempo de atención). Han de ser capaces de proporcionar información muy concreta que permitan identificar acciones, a ser posible preventivas.
- CHF = Insuficiencia Cardiaca Ejemplo de analítica avanzada, que toca 3 pilares: 1) cómo extraer información de fuentes no estructuradas, 2) definir un modelo predictivo que permita anticipar riesgo de IC antes del diagnóstico 3) Proponer acciones preventivas Calidad de vida del paciente - Costes
- Principales componentes de la solución: Content Analytics como extractor/intérprete de información no estructurada Modelos predictivos de riesgo de IC, entrenados en base al repositorio de información (estruc+no estruc) Modelos de identificación de pacientes similares ‘ad-hoc’. Cada paciente es diferente y las guías y protocolos se desarrollan en base a grupos estandarizados de pacientes. Por otro lado los ensayos clínicos se centran típicamente en pacientes con una sola enfermedad. Pero en la vida real cada paciente es diferente y pueden coexistir diferentes enfermedades.
- Vamos a ver un ejemplo de la solución aplicada a un proceso oncológico 1) AJCC: Estadificación del cancer = T2 Se extraen de manera precisa datos medicos de orígenes no estructurados. Se combinan con datos estructurados. 2) Se define un grupo de estudio ‘al instante’ con todos los miembros similares al paciente, analizando hasta 30K puntos de comparación. 3) Se hace un modelo de scoring y se predice la probablilidad de la incidencia para ese grupo de pacientes 4) Análisis de los ‘caminos’ de la enfermedad DTIC (dacarbazine). Purpose: DTIC is given to shrink or slow the growth of melanoma tumors that have spread throughout the body.
- Retos: Negaciones (LVEF= Fracción de Eyección del ventrículo izquierdo) Frases ambiguas Inferir significado cuando no hay contexto Normalizar los datos (fundamental la codificación)
- Análisis por oleadas (UIMA Pipeline): 1) Language Identification = 22 Languages (Spanish being one) 2) Based on the Language uses linguistics to do lexical analysis (parts of speech, normizing lemas (run, running) 3) Developed models for classification, disambiguation, relationship extraction Patient: Elizabeth Doctor: Professor O’Mohony (uh-naf-er-uh resolution of Elizabeth to form the relationship) Disease: Myocardial infarction Not a Disease: hypertension, diabetes, etc Ralationships
- IBM ha desarrollado reglas específicas de extracción de información clínica (diagnósticos, procesos, laboratorios, medicamentos,…) Se codifica esta información según normas muy importante para poder hablar el mismo idioma y poder comparar casos. Reglas específicas de detección de negaciones o de historias familiares
- Un ejemplo de una caso real de codificación automática de diagnósticos en base al catálogo ICD-9 para identificar procedimientos secundarios que no han sido facturados. Esta funcionalidad podría ayudar a evitar errores en la codificación (discrepancias entre el código ICD informado en la historia clínica y las notas del curso clínico)
- What Really Causes Readmissions at Seton Results and Highlights We started our analysis by identifying 113 candidate predictors (13 came from unstructured data only (ICA part of ICPA). We originally thought that Ejection Fraction (LVEF) and Smoking were the two primary unstructured predictors of interest but once we used actual predictive analysis, two other ICA variables actually surfaced: Assisted Living and Drug/Alcohol Abuse One of the key values of ICA in this use case was providing data to this process that was not available in structured form. If you look at the right side of the slide you can see the importance of the unstructured data … the structured data was less reliable then unstructured data which increased the reliance and usefulness on unstructured data (compare the values). The unstructured data was also more reliable … Smoking was only 65% accurate and the other data sources were too small to effectively gauge reliability. In the case of the Drug and Alcohol Abuse (ranked 3 of 18 predictors), ICA enhanced the encounters resulting in reducing the missing values from 84% to 16% making a much stronger predictor. In the case of Assisted Living (ranked 7 of 18 predictors), even though only 13% of the encounters had a yes value, it was significant enough to rank in the top 18 predictors. For this variable, the value was only from ICA. Applying this predictive model to CHF readmissions at the Seton Healthcare facilities, compared with a random effect(s) model or variance components model previously used where only 20% of readmitted cases are seen, the predictive model “captured” 49% of readmitted cases in the first quintile. On the predictive side (left), the gain chart shows that when compared to a normal distribution (red line), the ICPA predictive model scored 49% of the readmissions in the first 20 percentile compared to only 20% in the random model. This analysis shows that the ICPA model is very predictive. In the 20th percentile, the model captures 49% of readmitted cases compared to the no-model situation where only 20% of readmitted cases are seen In the 80th percentile, the model captures 97% of readmitted cases compared to no-model scenario which only captures 80%.
- Can do more complex models – using machine learning
- Como base para el modelo predictivo se construye una estructura de datos de tipo fecha, atributo, valor. Se tratan cientos de atributos de diferentes orígenes de datos (por ejemplo, de laboratorio un atributo puede ser la presión arterial). Datos estructurados y no estructurados. Al final para cada paciente se tiene una ventana temporal con decenas o cientos de miles de registros.
- La búsqueda por similaridad compara el vector de atributos de un paciente en el momento actual con todos los vectores de pacientes a lo largo del tiempo