SBIR-STTR Award

Substantial evidence gathered over the last 50 years shows that adherence poses a crucial barrier to effective treatment and survival for cancer and other chronic diseases. At least one in five cancer patients do not adhere to treatment regimen, with much higher disease-specific rates. This non-adherence, or deviation from the recommended and expected clinical path, can dramatically increase costs of care, hospitalizations, adverse outcomes and the chance of preventable death. What causes non-adherence to treatment regimens is currently not rigorously understood. Current adherence research methods largely rely on survey instruments that have limited scale and scope, provide lagging information that inhibits timely intervention, and offer little actionable information to help patients to adhere to their care regimens. Further, the nature and timing of intervention to improve adherence have not been researched in depth. With continuous changes in cancer treatment, newer proactive approaches and methods for surveillance of patient adherence and targeted interventions are needed. In this project, we examine the feasibility and validity of a novel approach that uses a computational model to glean fine-grained attributes of cancer patients from standard electronic medical records. Our preliminary work has shown that electronic records to contain free-form text describing patient sentiment, vitals, medical condition, side effects, social history and family status written by physicians, nurses, medical assistants, and other staff during every visit encounter. With the steady adoption of electronic medical records by clinicians across the US (currently 29% and rising at 12% per year), clinical notes found in electronic records offer a tantalizing source of insight into patient adherence and behavior. Current adherence research has not tapped this rich source of data, even though many disciplines including biomedical informatics have employed natural language processing and text-mining techniques to glean patterns in semi- structured biomedical data. We aim to employ similar but novel, scalable computational models to glean a rich set of risk factors for patient non-adherence from 1 million patient encounter records, corresponding to 24,050 patients that span a 10 year time-horizon. Our objectives are to estimate the risk of a patient's ability to adhere to a prescribed regimen and enable targeted and timely interventions by using computational analysis of unstructured and structured fields in standard clinical documentation.

Public Health Relevance:
We aim to show the feasibility of an early warning system that detects and estimates a cancer patient's risk of non-adherence to treatment regimens by analyzing unstructured text in standard medical records. This technology has tremendous relevance for improved quality of care, proactive management of chronic diseases and patient safety.

Public Health Relevance Statement:
Project Narrative We aim to show the feasibility of an early warning system that detects and estimates a cancer patient's risk of non-adherence to treatment regimens by analyzing unstructured text in standard medical records. This technology has tremendous relevance for improved quality of care, proactive management of chronic diseases and patient safety.

NIH Spending Category:
Behavioral and Social Science; Bioengineering; Cancer; Clinical Research; Clinical Trials

Project Terms:
Active Follow-up; Adherence; Adherence (attribute); Adoption; Adverse effects; Behavior; Behavioral; CCOP; Cancer Patient; Cancer Treatment; Cancers; Caring; Cell Communication and Signaling; Cell Signaling; Cereals; Cessation of life; Chronic Disease; Chronic Illness; Clinical; Clinical Paths; Clinical Trials; Clinical Trials, Unspecified; Community Clinical Oncology Program; Community Oncology; Compliance behavior; Computer Analysis; Computer Simulation; Computerized Medical Record; Computerized Models; Data; Data Set; Data Sources; Dataset; Death; Discipline; Disease; Disorder; Documentation; Drugs; Electronic Medical Record; Electronics; Emotional; Employment; Epidemiology, Family Medical History; Family Medical History; Family history of; Glean; Grain; HOSP; Hospitalization; Individual; Intervention; Intervention Strategies; Intracellular Communication and Signaling; Malignant Neoplasm Therapy; Malignant Neoplasm Treatment; Malignant Neoplasms; Malignant Tumor; Mathematical Model Simulation; Mathematical Models and Simulations; Medical; Medical Record, Computerized; Medical Records; Medication; Methodology, Research; Methods; Methods and Techniques; Methods, Other; Modeling; Models, Computer; Natural Language Processing; Nature; Nurses; Nutritionist; Oncology Programs; Outcome; Patient Compliance; Patient Cooperation; Patient Non Compliance; Patient Non-Adherence; Patient Nonadherence; Patient Noncompliance; Patients; Pattern; Personnel, Nursing; Pharmaceutic Preparations; Pharmaceutical Preparations; Phase; Physicians; Processings, Natural Language; Protocols, Treatment; Psychologist; QOC; Quality of Care; RGM; Records; Regimen; Research; Research Methodology; Research Methods; Risk; Risk Assessment; Risk Estimate; Risk Factors; SBIR; SBIRS (R43/44); Scientific Advances and Accomplishments; Semantic; Semantics; Signal Transduction; Signal Transduction Systems; Signaling; Simulation, Computer based; Small Business Innovation Research; Small Business Innovation Research Grant; Social Workers; Source; Structure; Surveillance Methods; Survey Instrument; Surveys; System; System, LOINC Axis 4; TXT; Techniques; Technology; Text; Time; Treatment Compliance; Treatment Protocols; Treatment Regimen; Treatment Schedule; Treatment Side Effects; Visit; Weight; Work; Writing; anticancer therapy; base; biological signal transduction; biomed informatics; biomedical informatics; cancer care; cancer therapy; chronic disease/disorder; chronic disorder; clinical investigation; clinical practice; compliance cooperation; computational analysis; computational modeling; computational models; computational simulation; computer based models; computerized modeling; computerized simulation; cost; discovery mining; disease/disorder; drug/agent; effective therapy; follow-up; improved; in silico; innovate; innovation; innovative; insight; interventional strategy; literature mining; literature searching; malignancy; mathematical algorithm; natural language understanding; neoplasm/cancer; new approaches; novel; novel approaches; novel strategies; novel strategy; patient adherence; patient safety; psychologic; psychological; public health relevance; scientific accomplishments; scientific advances; side effect; social; text mining; text searching; therapy adverse effect; therapy compliance; therapy cooperation; treatment adverse effect; virtual simulation

Substantial evidence gathered over the last 50 years shows that non-adherence to treatment poses a crucial barrier to effective care and survival for cancer and other chronic diseases. At least one in five cancer patients do not adhere to treatment regimen, with much higher disease-specific rates. This non-adherence, or deviation from the recommended and expected clinical path, can dramatically increase costs of care, hospitalizations, adverse outcomes and the chance of preventable death. What causes non-adherence to treatment regimens is currently not rigorously understood. Current adherence research methods largely rely on survey instruments that have limited scale and scope, provide lagging information that inhibits timely intervention, and offer little actionable information to help patients to adhere to their care regimens. With continuous changes in cancer treatment, newer proactive approaches and methods for surveillance of patient adherence and targeted interventions are needed. In this Phase 2 SBIR project we will examine the validity of a novel approach (based on the completed Phase-1 project) that uses a novel computational algorithm to glean fine- grained attributes of cancer patients from standard electronic medical records. Our preliminary work has shown that most electronic medical records contain free-form text describing patient health progress, sentiment, vitals, medical condition, side effects, and social history written by physicians, nurses, medical assistants, and other staff during every visit encounter. With the steady adoption of electronic medical records by clinicians across the US (currently 29% and rising at 12% per year), clinical notes found in electronic records offer a tantalizing source of insight into patient adherence and behavior. In this Phase-2 SBIR project we aim to commercialize a novel, scalable prototype that can glean a rich set of risk factors for patient non-adherence from 1.5 million patient encounter records, corresponding to 30,050 patients that span a 10 year time-horizon from a community cancer clinic. Our objectives are to estimate the risk of a patient's ability to adhere to a prescribed regimen and enable targeted and timely interventions by using computational analysis of unstructured and structured fields in standard clinical documentation. We also aim to monitor and measure important patient treatment and adherence metrics (e.g. as defined by the American Society of Clinical Oncology) that can play a significant role in tracking high-risk patients for improved patient treatment outcomes, adherence, quality and safety. Our approach represents a significant, actionable advance over the lagging indicators offered by survey-based methods prevalent in adherence research. Our proposed approach has deep implications for improved quality of care, proactive management of chronic diseases, retention of patients in clinical practice and clinical trials, patient safety, improved patient follow-up and risk assessment, drug and disease surveillance, enablement of new care models, targeted intervention, and improved outcomes by helping patients to better adhere to their regimens.

Public Health Relevance:
We aim to show the feasibility of a commercial early warning system that detects and estimates a cancer patient's risk of non-adherence to treatment regimens by analyzing unstructured text in standard medical records. This technology has tremendous relevance for improved quality of care, proactive management of chronic diseases and patient safety.