Machine Learning

Top 3 Use Cases for Actionable Data

Utilizing data for secondary use cases to improve efficiency and accuracy of operations enables a healthcare system to provide better quality care and ensures that the health system can better suit the needs of a variety of patients. Learn how you can generate actionable data and the top three use cases to significantly improve your healthcare system.

The healthcare industry constantly inputs and outputs data relating to patient care. This data is mandatory for core hospital activities, like patient care, reporting and reimbursement. However, its low quality, due to the manual and often retrospective processes associated with coding and auditing, both limits its short-term impact within core activities and inhibits its use for important secondary use cases. Low quality data stands in the way of a more effective, value-based, and data-driven healthcare system.

This blog post will both help imagine the future of healthcare data, by identifying the top three use cases for actionable data, and act as a guide to help health systems achieve this reality by immediately making data quality improvements.

With high-quality, actionable data, hospitals and health systems can start:

1. Predictive analytics for population health management

Machine learning (ML) predictive algorithms can immensely empower population health management initiatives through the identification of high-value trends.

Predictive learning models increase efficiency gains, automate the identification of key trends, and can enable healthcare facilities to intervene faster at the primary care level. For example, healthcare providers may remark that certain patients are coming to the hospital for particular symptoms whereas there are underlying causes that are representative of broader population health issues. With high quality data, healthcare providers can perform predictive modeling to improve population health and develop new, more advanced education initiatives around public health.

2. Value-based, patient-centric care

AI and ML algorithms can help to reduce costly operational and financial processes in healthcare facilities.

On the operational side, ML can be used to significantly streamline various administrative processes that can be otherwise expensive and difficult to manually process. This can reduce the amount of repetitive work, increasing the capacity of staff towards analytical thinking and focused efforts on high-value opportunities. All elements of the revenue cycle can be improved as soon as a patient is admitted.

High quality data can also enable value-based care by taking into account the cost of expected health outcomes, based on historical experiences, with the actual and total cost of a particular encounter. The integration of high quality data within a learning health system can result in reasonable changes to traditional payment systems. As a result, patients can receive greater value for the medical care they receive. AI and ML, in this way, can be deployed to enable these alternative payment models, actively improving the financial systems.  

Value-based, patient-centric care relies on high-quality coded and claims data. Without tackling data-quality as a first step, the future of patient care is limited.

3. Real-time operational visibility

The process of examining patient medical records, interpreting outputs, and disseminating unstructured EHR data can be a time-consuming process. Automated processes are able to provide an efficient and effective way to interpret this information based on specific data types. In doing so, this offers real-time operational visibility to ensure system operations are functioning correctly and capturing accurate data, turning your staff’s attention to those items needing their focus. Listed below are five information types generated in the healthcare industry and the use case of these particular data types:

  1. Financial Data
  2. Biometric Data
  3. Scientific and Research Data
  4. Machine to Machine Data
  5. Data created by Physicians

Accurate coding leads to appropriate and timely claims payments for hospitals and physicians, and extensive documentation leads to precise coding. Above all, thorough documentation can result in better, more effective patient care. Therefore it largely depends on the type of structured data and how this data is interpreted for further use. Without high quality data, operational visibility is limited in scope and largely inaccurate.

The importance of quality data in healthcare

Data is fundamental to hospital processes, from operational decision making, to the revenue cycle, to patient care. Low data quality results in limited improvement to patient care, lower facility administration efficiencies, and lower reimbursements. Importantly, low quality data can actively inhibit visibility into health system performance and inhibit potential efficiencies in care delivery, operational processes, and financial performance.

Data quality improvements are necessary in healthcare to facilitate all aspects of care and ease administrative burden of financial processes.

A major contributor to low data quality issues are due to medical record errors. These errors can be traced to manual coding issues (due to misidentification of coding errors) as well as inefficient auditing processes. To counteract these data quality issues, there must be high-quality data audits to ensure compliance with applicable guidelines and accurate identification of MRDx. To achieve these data quality improvements, AI and deep learning technology can be used to perform high quality data audits to ensure that relevant data is captured and data quality is improved.

How AI can be used to generate high quality data

Advancements in NLP (Natural Language Processing), ML, and AI (Artificial Intelligence) enabled us at Semantic Health to create a state of the art medical coding and auditing platform that will allow health systems to improve data quality, ensure compliance with coding guidelines & standard, and boost coder accuracy.

The Semantic Health Information Platform analyses clinical notes as they are written to develop longitudinal patient narratives, detect data quality issues and assign medical codes based on clinical evidence. As a result, this acts as a solution to primary data use cases within population health management and value-based, patient-centric care. Moreover, the Semantic Health Platform provides real time operational visibility by detecting any missing or underspecified codes, directing the auditor's attention to the precise spot in the documentation that needs to be examined. The information may be utilized to launch new data-driven, high-quality, and relevant clinical documentation improvement initiatives. In doing so, the platform is able to ensure data accuracy by initiating high quality managed data audits to automatically double-check the identification of patient information and data quality compliance. Using advanced AI, NLP, and ML technologies Semantic Health effectively acts as a solution by producing high-quality and real time data that can then be used for top use cases that enable the future of healthcare.

About Semantic Health

Semantic Health helps hospitals and health systems unlock the true value of their unstructured clinical data. Our intelligent medical coding and auditing platform uses artificial intelligence and deep learning to streamline medical coding & auditing concurrent with patient admission, improve documentation quality, optimize reimbursements, and enable real-time access to coded data for secondary analysis.

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