Healthcare Data Aggregation: For Better Efficiency, Care Delivery & Patient Experience
Updated: Oct 13, 2022
Utilizing huge volumes of health data generated regularly is a big challenge. Data aggregation produces interoperative, analytics ready, sharable information that can benefit all - patients, providers and payers! Let’s see how…
The emergency medicine staff of KD hospital is running urgently as John gets admitted with the complaint of severe breathing trouble. A few years ago, when John was traveling to another city, he had a similar complaint and was admitted to a local hospital. They ran a few tests on him and prescribed medication. This has become a chronic problem for him with several hospital visits and diagnostic tests done in multiple cities across the country. Unfortunately, John doesn’t have a copy of all previous tests and prescriptions with him right now.
The administrative staff in KD hospital is trying to connect with John’s previous doctors, clinics, and labs to get his medical records faxed. But, that’s a tedious task. Time is running out and the doctors are forced to make important decisions without John’s previous medical records and test results. Imagine the quality and efficiency of care delivery. Adding to the complexity, John’s health insurer takes weeks to process claims due to manual claims processing.
This was a common scene a decade ago. Fortunately, that paper-age is gone where clinicians communicated with handwritten notes, prescriptions, and diagnostic tests. The new age healthcare systems are moving towards digitizing all records. The healthcare data of individual patients is huge. With wearable technologies and the growing trend of consumers tracking their health information, healthcare data is becoming overwhelming.
Moreover, gone are the days when clinical data remained in silos and doctors could view only fragmented patient records. For improved care delivery, healthcare systems are finding ways to aggregate, analyze and prioritize patient data.
Aggregated data is tracked across time, across organizations, across patient populations, or other variables. This data is liquid and free to move across locations. It can be used more meaningfully, allowing care providers to unlock actionable clinical insights.
Consider the case of John again. The emergency staff could have used aggregator services to get secured aggregated medical records of John with comprehensive views of clinical data, prescriptions, and test results from every doctor visit in various locations in the past. Imagine the difference in care delivery and clinical decision-making in this case.
Now imagine on a larger scale. What if doctors had access not only to John’s patient data but data from thousands of similar cases? Such anonymized aggregated data would have helped doctors see John’s case from a different angle, with a vast amount of supplemental information. This is the power of aggregate health data. It can be lifesaving if utilized properly by healthcare providers. Besides, the aggregated data serves as a tool for insurance companies to fast-track claims processing.
The aggregation of healthcare data is powered by robust data aggregation engines like SquareML. A vast amount of work is done by SquareML to give you aggregated and curated patient data. SquareML extracts information from unstructured formats like prescriptions, doctor’s notes, CT/MRI scans, test results kept in the form of pdfs, images, noisy text, etc. SquareML uses OCR, NLP, and Image analysis technologies and converts unstructured data into structured one. It creates a database in JSON, CSV, and other formats and performs data cleaning by removing empty columns, duplicate rows, column name standardization, etc. Certain micro-operations are performed on the data to make it normalized. NLP is used to extract family/ social/ surgical/clinical history and extract medical named entities, summarize notes, etc. The aggregated and normalized data sets are ready for analysis. Applying technologies such as AI, ML and predictive analytics to health data eliminates variations and allows for better clinical decision-making and care delivery.
Finally, data is presented in the form of single-page summaries and visuals like vitals, frequency, the timeline of events graphically, anatomy-based graphical representation, etc. This way patients, healthcare providers, or payers get a comprehensive view of patient-specific or population-based data in just a few clicks.
Health data aggregation has made possible to combine information available in varied formats from multiple systems to produce interconnected and shareable information. It has a multitude of benefits for patients, providers, and payers.
For patients – Get all records from several different sources, aggregate them into one data set, and view them in a simple, easy-to-read interface. Share health records with a medical professional or anyone else, track important health statistics and immunizations, monitor progress, import information from various wearables, and store it in a single place. Overall, the focus is shifting to a patient-centric approach providing an enhanced patient experience!
For providers – Access to patient and population data improved care delivery and clinical decisions, and better healthcare solutions. All in all, data-driven clinical decision-making and better care delivery!
For payers – Receive secured and reliable medical data quickly with real-time view of the applicant or insured person’s present health condition. A comprehensive and up-to-date information is available for risk analysis and the underwriter can monitor health information, medications prescribed in real-time. They are also better equipped to prevent fraud claims. Above all, speedy claims processing!
As per a report, the healthcare analytics market is expected to reach USD 75.1 billion by 2026. Data aggregation, normalization, and AI-based data analytics are the future of healthcare. All healthcare industry players can tap into this ever-growing area. We at SquareML can help you reach new heights by leveraging the power of your data.