During this session, presenters will walk through how to engage healthcare organizations in design research using medical data while complying with HIPAA’s privacy rules. Topics will include coordinating a data request, defining a limited data set, and statistical analysis of patient outcomes.
From the moment a person contracts a disease, they begin to experience it emotionally and socially in addition to physically. The same applies to patients’ interactions with medical professionals.
eSource
eSource is an electronic way of collecting data from participants in clinical trials. It is typically used for recording visit information and lab results during a study and is designed to capture all necessary details from research and standard-of-care perspectives. It also eliminates the need for a double-entry process and removes delays between systems that can lead to errors.
Using eSource can boost the speed of clinical trial execution and increase patient recruitment. However, more importantly, it can enable the entire industry to leap forward in conducting trials and treating patients. Analyzing clinical outcomes data helps healthcare providers assess the effectiveness of treatments and make informed decisions to enhance patient care and treatment plans.
Ultimately, the future state of eSource would be for all source data to be collected electronically and transferred directly into clinical trial systems. However, this will require a significant amount of effort and collaboration from all stakeholders to realize. It will also require changing attitudes toward what counts as source data. As such, the benefits of eSource will likely take some time to kick in fully.
Electronic health records (EHRs)
A patient’s EHR contains information about the medical history of that person, critical demographic data, and reports from clinicians who have cared for the patient. It also supports a variety of administrative processes, such as documentation, billing, scheduling, and insurance eligibility checks.
These systems help physicians avoid duplication of work by allowing them to send the same electronic data to multiple providers. This can eliminate paperwork and reduce costs while eliminating errors due to illegibility or misplacement of records.
Additionally, analyzing clinical outcomes data is crucial for healthcare providers to assess the effectiveness of treatments and interventions, ensuring evidence-based decisions for better patient care.
EHRs help physicians identify patterns and accurately diagnose patients, improving patient outcomes. They also enable physicians to use predictive analytics to recognize clinical problems before they occur and recommend potential treatment options. Physicians also benefit from automated documenting tools that improve coding accuracy and increase first-time acceptance rates for insurance claims. EHRs can also provide valuable analytics to identify gaps in care and assist with quality reporting. They can be integrated with other healthcare IT solutions, such as radiology, labs, and pharmacies.
Interoperability
Having interoperable systems that allow information to flow smoothly from one healthcare facility to another boosts efficiency. When this happens, the front-office staff can focus on tasks like entering medical codes for services rendered and preparing insurance claims. In a clinical setting, interoperability means patients can receive treatment without delays.
Achieving interoperability is a challenging task. It requires a commitment to change and significant initial investments that may be out of the reach of many clinics. However, it will help reduce medical errors and lower costs by giving clinicians a fuller picture of their patient’s health history.
Currently, much work is going on to improve interoperability. For example, the FDA is developing software to facilitate data exchange and improve care. This will provide a platform for interoperability between health systems, making it easier to share information across borders. It will also help to ensure patient privacy. The FDA hopes to implement these new tools shortly.
Natural language processing (NLP)
Natural language processing (NLP) is the technology behind tools like Apple’s virtual assistant Siri and Amazon’s voice-controlled Alexa. This technology enables computers to understand what users mean when they speak, translate between languages, and recognize words in documents.
Some of the most critical applications for healthcare NLP include identifying patient needs and risks, analyzing unstructured information, automating coding and billing processes, and enabling clinical decision support. NLP can also help improve medical education and foster shared decision-making.
Some standard NLP techniques include tokenization, which breaks down a string of words into distinct units called tokens. Syntax analysis identifies the syntactic structure of a document and the dependencies between words, represented in a diagram known as a parse tree. Relationship extraction is another NLP method that establishes semantic relationships between terms. For example, it can identify at-risk patients for Kawasaki disease and help emergency departments avoid delays caused by a lack of complete data. NLP also has the potential to accelerate clinical research.