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Pivoting CDI: The World of Healthcare Watches

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Is CDI about to embark on a long journey to reinvent Itself? There is no arguing that artificial intelligence (AI) and natural language processing (NLP) are making inroads in the healthcare revenue cycle, creating better efficiencies with the automation of a multitude of historically manually performed tasks, thereby reducing positions that were once performed by staff. AI is clearly beginning to take hold and make significant inroads in the clinical documentation integrity (CDI) space. I have noticed serval posts on LinkedIn, as well as in Becker's Healthcare e-newsletters, discussing the role of AI in the revenue cycle. Just recently, there was a blog post published in KevinMD titled "How an AI bot transformed my EHR experience (KevinMD blog)" centering on how AI streamlined the provider's documentation and charting in the electronic health record (EHR) by scanning through the documentation as the note is being completed, providing suggested diagnoses with associated ICD-10 codes.


Unlock patient data insights using Amazon HealthLake

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AWS just announced the General Availability of Amazon HealthLake, a HIPAA-eligible service for healthcare providers, health insurance companies, and pharmaceutical companies to securely store, transform, query, analyze, and share health data in the cloud at petabyte scale. We believe that the combination of the innovation trends in healthcare (such as reimbursement models around data-driven evidence), standardization around interoperability (such as federal and global incentives and mandates in adopting the Fast Healthcare Interoperability Resources standard, or FHIR), and the advancement of scientific methods (such as with deep learning) enable our healthcare and life sciences (HCLS) customers to improve clinical and research efforts. Over the past decade, we've witnessed a digital transformation with healthcare organizations capturing huge volumes of patient information in electronic medical records (EMRs) every day, making the medical record a source of big data containing information regarding sociodemographics, medical conditions, genetics, and treatments. Making sense of all this data provides the biggest opportunity to transform care by tailoring disease treatment and prevention to individuals and populations. This so-called precision medicine takes into account the individual variability in genes, environment, and lifestyle for each individual.


Wound and episode level readmission risk or weeks to readmit: Why do patients get readmitted? How long does it take for a patient to get readmitted?

arXiv.org Machine Learning

The Affordable care Act of 2010 had introduced Readmission reduction program in 2012 to reduce avoidable re-admissions to control rising healthcare costs. Wound care impacts 15 of medicare beneficiaries making it one of the major contributors of medicare health care cost. Health plans have been exploring proactive health care services that can focus on preventing wound recurrences and re-admissions to control the wound care costs. With rising costs of Wound care industry, it has become of paramount importance to reduce wound recurrences & patient re-admissions. What factors are responsible for a Wound to recur which ultimately lead to hospitalization or re-admission? Is there a way to identify the patients at risk of re-admission before the occurrence using data driven analysis? Patient re-admission risk management has become critical for patients suffering from chronic wounds such as diabetic ulcers, pressure ulcers, and vascular ulcers. Understanding the risk & the factors that cause patient readmission can help care providers and patients avoid wound recurrences. Our work focuses on identifying patients who are at high risk of re-admission & determining the time period with in which a patient might get re-admitted. Frequent re-admissions add financial stress to the patient & Health plan and deteriorate the quality of life of the patient. Having this information can allow a provider to set up preventive measures that can delay, if not prevent, patients' re-admission. On a combined wound & episode-level data set of patient's wound care information, our extended autoprognosis achieves a recall of 92 and a precision of 92 for the predicting a patient's re-admission risk. For new patient class, precision and recall are as high as 91 and 98, respectively. We are also able to predict the patient's discharge event for a re-admission event to occur through our model with a MAE of 2.3 weeks.


Healthtech catalyzing efforts to achieve Universal Health Coverage

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Healthcare technology, aka healthtech, is rapidly transforming the way healthcare services are accessed and delivered across the world, particularly to the vulnerable populations in the low and middle-income countries. Health technologies and interventions are critical elements that expand access to effective and affordable health services whilst simultaneously catalyzing efforts to achieve the goal of Universal Health Coverage (UHC). With the advent of electronic health records or digital records, concerns regarding the security and ownership of the sensitive health data have also arisen. For the medical data to be stored and accessed safely, healthcare providers and consumers are utilizing blockchain, the technology behind cryptocurrencies that significantly increases transparency and security by storing and distributing data to all participants across the entire supply chain. Besides data security, the distributed ledger technology is also being used to curb the menace of drug counterfeiting.


Opinion: AI needs patients' voices in order to revolutionize health care

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"Listen to your patient; they are telling you the diagnosis," an aphorism attributed to Dr. William Osler, the founder of modern medicine, still holds true today. The disappearance of patients' stories from electronic health records could be one reason that artificial intelligence and machine learning have so far failed to deliver their promised revolution of health care. The medical industry's fascination with artificial intelligence is understandable. Advancements in medicine have dramatically improved patient outcomes, and there is every reason to believe that machine learning, deep learning, artificial intelligence, and the like will do the same. But before we jump on the AI bandwagon, I offer this caution: consider the source of the data it is dependent on.


Artificial intelligence needs patients' voice to remake health care - STAT

#artificialintelligence

"Listen to your patient; they are telling you the diagnosis," an aphorism attributed to Dr. William Osler, the founder of modern medicine, still holds true today. The disappearance of patients' stories from electronic health records could be one reason that artificial intelligence and machine learning have so far failed to deliver their promised revolution of health care. The medical industry's fascination with artificial intelligence is understandable. Advancements in medicine have dramatically improved patient outcomes, and there is every reason to believe that machine learning, deep learning, artificial intelligence, and the like will do the same. But before we jump on the AI bandwagon, I offer this caution: consider the source of the data it is dependent on.


Extract and visualize clinical entities using Amazon Comprehend Medical Amazon Web Services

#artificialintelligence

Amazon Comprehend Medical is a new HIPAA-eligible service that uses machine learning (ML) to extract medical information with high accuracy. This reduces the cost, time, and effort of processing large amounts of unstructured medical text. You can extract entities and relationships like medication, diagnosis, and dosage, and you can also extract protected health information (PHI). Using Amazon Comprehend Medical allows end users to get value from raw clinical notes that is otherwise largely unused for analytical purposes because it's difficult to parse. There is immense value associated with extracting information from these notes and integrating it with other medical systems like an Electronic Health Record (EHR) and a Clinical Trial Management System (CTMS).


AI Powered EHR

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Artificial intelligence is like the air around us, it is practically everywhere. From the palm of our hands, in the form of our smart watches and phones to our heads; MIT's Alter Ego project is based on a cognitive helmet which allows humans to interact with machines in Natural Language, through the process of brainstorming – without speaking a word. AI powered EHR has had many successes in the healthcare industry; based on a Chinese trial: An AI system programmed to aid brain scans, scored a higher score in accuracy as compared to its human counterparts. The patient which was predicted by the system to be revived from a coma, did indeed woke up. Whereas, the doctors who took part in this experiment had proposed totally different outcomes.


Deep EHR: Chronic Disease Prediction Using Medical Notes

arXiv.org Machine Learning

Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, however, focus on structured fields and lose the vast amount of information inthe unstructured notes. In this work we propose a general multi-task framework for disease onsetprediction that combines both free-text medical notes and structured information. We compareperformance of different deep learning architectures including CNN, LSTM and hierarchical models.In contrast to traditional text-based prediction models, our approach does not require disease specificfeature engineering, and can handle negations and numerical values that exist in the text. Ourresults on a cohort of about 1 million patients show that models using text outperform modelsusing just structured data, and that models capable of using numerical values and negations in thetext, in addition to the raw text, further improve performance. Additionally, we compare differentvisualization methods for medical professionals to interpret model predictions.


Death vs. Data Science: Predicting End of Life

AAAI Conferences

Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to predict with some certainty when the health of a person is going to deteriorate. In this paper, we predict risk of mortality for patients from two large hospital systems in the Pacific Northwest. Using medical claims and electronic medical records (EMR) data we greatly improve prediction for risk of mortality and explore machine learning models with explanations for end of life predictions. The insights that are derived from the predictions can then be used to improve the quality of patient care towards the end of life.