Longitudinal healthcare data means patient health information collected over a long time, sometimes for years or decades. This data includes various records from many care places—such as hospital stays, outpatient visits, emergency room visits, radiology, heart reports, mental health checks, and doctors’ notes.
Tracking patient journeys with longitudinal data means watching how a patient’s health changes over time. It shows how different treatments affect results and how care can be better. In the United States, medical administrators and IT managers use this data to plan resources, improve quality, and follow value-based care rules. This helps give a full view of patient experiences.
For example, UPMC Enterprises’ Ahavi platform has records for more than 5 million patients from over 24 hospitals in Pennsylvania. It includes over 156 million patient visits from 2019 and also unstructured data like transcripts and radiology reports back to 2012. The platform links over 80% of structured and unstructured data, giving a full picture of patient health. This is important for research and care.
Similarly, Holmusk’s NeuroBlu database focuses on mental health. It holds 20 years of data from more than 1.3 million patients in over 30 health systems in the U.S. This data covers many mental health issues like depression, anxiety, ADHD, and PTSD with millions of patient records.
Having large, long-term datasets lets healthcare workers and researchers look beyond quick snapshots. They can see whole health stories. This helps with better decisions from diagnosis to long-term care.
AI models in healthcare need a lot of accurate and detailed data to learn patterns and make predictions. Long-term healthcare data gives AI researchers real patient stories that show how treatments work and what the outcomes are.
Ahavi provides AI teams with de-identified data that protects patient privacy while keeping important details. The data goes through six steps: getting the data, defining groups of patients, adding data, removing personal info, checking by a third party, and safely giving it to researchers. This follows privacy laws like HIPAA and keeps patient info safe.
Access to both structured data (like lab tests, medications, and procedures) and unstructured notes (like emergency visit notes) helps AI learn better. For example, notes from doctors in text form tell stories often missing in coded data.
In mental health, Holmusk’s NeuroBlu uses natural language processing (NLP) to pull out useful info from unstructured psychiatric notes. This adds details about symptoms, stress, and social factors. These details help build better AI to diagnose and predict treatment results.
With full longitudinal data, AI can track changes over time, spot risk factors early, and suggest personalized care. For medical managers and IT staff, this means better patient results, fewer readmissions, and stronger health programs.
A key part of good longitudinal data is joining structured data with unstructured clinical notes. Structured data means coded info like diagnosis codes, medicine orders, and lab results. Unstructured data includes free text like notes, radiology reports, and transcripts that tell patient stories.
Ahavi links over 80% of these two types of data. This helps AI understand the full picture and lowers missing information. For example, linking lab results with doctor notes about symptoms or treatment reasons lets AI better connect test results with care decisions.
This linked data helps clinical research too. Researchers can test ideas using richer info, study how treatments work more clearly, and follow disease progress in detail. Medical administrators can then use solid, real-world data to make care plans instead of relying on isolated visits.
Longitudinal datasets are the base for deep clinical outcomes research. This research tracks how patients respond to treatments, how diseases change, and how healthcare is used over time.
For instance, Holmusk’s NeuroBlu Database contains 30 standard assessments such as the PHQ-9 for depression, Clinical Global Impressions (CGI), and the Columbia-Suicide Severity Rating Scale (C-SSRS). These scores give numbers to symptoms and patient health over time.
Researchers combine this data with medicine records for antidepressants, mood stabilizers, antipsychotics, and stimulants. This lets them check how well treatments work in real life for mental health conditions across many patients. This helps improve clinical trials, find which patients benefit most, and support care models that require proof of better outcomes.
In broader health research, platforms like Ahavi give access to many patients from different hospitals. This variety helps make studies more accurate and supports public health monitoring. It also helps medical managers see trends in their patient groups and change care plans as needed.
Using AI and automation in healthcare offices is becoming more common. Hospitals want to make workflows simpler and improve patient contact. For managers and IT teams, AI-powered phone systems can reduce missed appointments, speed up scheduling, and improve communication.
Companies like Simbo AI use AI to answer calls and handle phone tasks so staff are not overloaded. This cuts wait times and missed calls. AI answering systems can confirm appointments, give pre-visit info, and handle simple questions. This frees up staff for harder work.
AI also helps bring patient data into daily work. AI trained on full patient history can warn about high-risk patients or give doctors real-time advice during visits.
Automation helps with required paperwork and follow-ups under value-based care rules. When combined with large data platforms like Ahavi or NeuroBlu, AI tools make operations run better and help improve patient care.
Medical practices in the U.S. will use longitudinal healthcare data more and more. Having access to many years of patient info helps manage chronic diseases, stop hospital readmissions, and tailor treatments based on long-term trends instead of one-time events.
Hospital leaders will invest in data platforms and work with groups like UPMC Enterprises and Holmusk for big datasets. IT teams will focus on keeping data safe and following rules while supporting AI development and research.
Also, combining these tools with AI front-office solutions like Simbo AI helps healthcare centers improve patient interactions and satisfaction. This affects care quality and helps hospitals perform better financially.
In short, longitudinal healthcare data is a strong base for AI tools and clinical research in the U.S. Using patient data over time, healthcare groups can learn more about patient journeys, create trusted evidence, and improve how care is given for both providers and patients.
Ahavi is a real-world data platform developed by UPMC Enterprises that provides primary source-verified, de-identified healthcare data. Its purpose is to enable researchers, scientists, and developers to create curated datasets for accelerating research, clinical trial design, and AI development in healthcare.
Ahavi applies a rigorous six-step process including data acquisition, cohort definition, data augmentation, de-identification, honest broker validation, and researcher portal access, ensuring all patient data is de-identified and privacy-compliant before being made available.
Ahavi offers both structured data (like allergies, labs, medications, procedures) dating back to 2019, and unstructured data (ambulatory documents, ED/inpatient reports, radiology, transcription) dating back to 2012, covering comprehensive patient health information.
The platform provides access to data from over 5 million patients treated at more than 24 hospitals within Pennsylvania, ensuring diverse and representative patient populations across various care settings.
Ahavi achieves over 80% linkage between structured and unstructured data, enabling a holistic view of patient health journeys, which is crucial for robust AI training and accurate clinical insights.
Ahavi primarily serves pharmaceutical companies, clinical trial partners, AI developers, and academic researchers who require high-quality, de-identified healthcare data to support research, AI model training, and clinical development.
Ahavi offers a secure, compliant environment with streamlined workflows that deliver comprehensive, de-identified datasets in as little as four weeks, enabling AI teams to train, validate, and fine-tune models efficiently without compromising data privacy.
Ahavi offers advanced real-world data analytics services that enable scalable, cost-effective exploration of both structured and unstructured data. These services help uncover clinical insights, optimize treatment pathways, and support epidemiological and retrospective research.
Third-party certification ensures that Ahavi’s data processing pipelines meet regulatory-grade standards, guaranteeing primary source verification, data integrity, privacy compliance, and publication readiness essential for trustworthy AI and clinical research.
Ahavi tracks longitudinal patient health journeys by providing access to data that goes back to 2012 for unstructured sources and 2019 for structured data, allowing researchers to analyze long-term health outcomes and trends for AI model development and clinical studies.
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