Gradient Health: AI enabled searchable indexed medical data

Healthcare needs an update in its measuring techniques across the world. In some current medical systems in the Global South, especially in Sub-saharan African, medical data is not kept to create historical data streams that can be used in prediction models and are shareable across information systems for further research. Instead, when processing capacity is reached, many low resource hospital systems simply delete their patients' data, removing the possibility of easily tracking primary care improvement over time. Many hospital systems lack the processes to access and share their data to improve international health outcomes. Majority of data used in A.I. applications come from wealthy nations, ultimately affecting the world’s most vulnerable populations. The current medical system represents a fragmented approach to accessing patient data. Hospitals and Healthcare providers are not aware of what currently exists in their picture archiving and communication system (PACS) even in more resourced communities, leading to a stalling of research ability to improve quality of care. 

We seek to improve the index by including marginalized populations, with a focus in Africa and South America. We want to create databases that will update in real-time, available to any academic researcher or AI engineer as they seek to develop algorithms built with large quantities of curated, labeled, and diverse data that can be deployed in low-resource settings. 

This is achieved by Gradient's three-ponged application: 

1. Indexing and dashboard software. Most academic or imaging centers, whether in the US or abroad, are not even aware of the data sitting in their servers.  We designed a dashboard based on radiolong “findings,” a collection of terms and descriptions used by radiologists to describe the pathologies in the images. The dashboard will also reflect patient age, demographics, and geographical location, among other data. Our software can be integrated into any vendor’s PACS and reporting system. The software will update in real-time and will allow for immediate creation of patient cohorts based on their radiology findings. 

2. De-identification software. We continue to refine our de-identification software. Although we anticipate that the majority of DICOM images will already be de-identified, we want to add an additional layer of privacy by detecting and redacting burns out protected health information. 

3. Labeling software. We have developed web-based image labeling software that is not only user-friendly, but encompasses the majority of tasks that machine learning engineers could want, including bounding box and segmentation  (see attached). A major benefit of our software is that multiple users can label images remotely, without the need to download additional software. In addition, the newly labeled data can be transmitted seamlessly and securely to the development team. In addition, our labeling software can also function as a PACS (Picture Archiving and Communication System). Although it lacks some of the post-processing functionality seen with established companies, our software will still represent a significant step-up for certain imaging facilities in Africa.

For especially low resourced areas, we will be focusing on education about data aggregation and radiology resources. 

Gradient Health, Inc is helping connect hospital and healthcare organizations with research institutions to better be aware of patterns in data aggregations. This solution solves A.I. companies and academic researchers’ needs for large, diverse annotated medical images while also serving hospital systems by permitting them to have an active role in the development of A.I., improves their understanding of how AI models are deployed, and access to an indexed and searchable on-premises platform to better care for their patients. In especially low-resource areas, Gradient also provides them a PACS system and educational training for radiologists to optimize primary patient care. 

We have a diverse, accomplished group of people working to solve this problem. Our founders, Josh Miller and Ouwen Huang have previously worked together on a computer vision agricultural technology company FarmShots, a satellite imagery platform that was deployed across Africa and Brazil. Dr. Sophie Chheang is Interventional Radiologist and Assistant Director of Informatics at Yale. Nico Addai is a Research Consultant for MIT STEP lab where she is trained in A.I. ethics development. We are currently partnered with Telelaudo, a teleradiology company that provides remote radiology reports to imaging centers and hospitals in Brazil and other Portuguese-speaking countries. We have a growing team of data engineers and software engineers that prioritize low-resource deployments for any hospital partner we may encournter. 

Gradient Health, Inc is at a point where it is trying to develop deeper connections with its community partners while still meeting the needs of its A.I. company clients. We seeks to prioritize the building of these communities in the Global South which has proven difficult without on the ground assistance. While our solution requires a low level of connectivity, educating underserved communities about the power of their data is imperative for phase 2 of our deployment. 

Gradient Health, Inc services a three-sided marketplace: we integrate the needs of research institutions, health providers and AI companies that use our three products. 

For our health providers, we index their data and make searching for similar cases to their patients' pathologies as simple as a Google search. This product is built for low connectivity or bandwidth with special care to be accessible for low-income countries where Gradient Health covers the cost of setting up their PACS system in especially low-resource areas of the Global South. Gradient Health, Inc goes further in also providing education and training for doctors and providers in low resource areas to improve measurement methods, thus increasing the quality of aggregate healthcare data. They are reimbursed with either a donation to their respective hospitals or a revenue share agreement that places the power of their data in their hands. 

Academic partners at research institutions are given open access to its medical imaging datasets, aiming to accelerate high quality research into AI for healthcare. Using advanced natural language processing techniques to organize the images according to radiology findings, and make those images available to developers  who create algorithms that are fairly and accurately deployed for the very people that they were trained on.

Our A.I. companies act as our customers and purchase access to annotated DICOM images or utilize our DICOM viewer to annotate and analyze their images.

Our goal for the next 5 years is to integrate with at least 10 data sources in Africa and South America in at least 5 different countries. We are in the process of  developing partnerships with private radiology annotation companies to train radiologists and radiologist technicians remotely. We also have begin to deploy our platform and build algorithms for deployment in low resource settings. By the end of five years, our goal is to have integrated our open-source dashboard to allow anyone in the world to look up open source de-identified DICOM images from at least 30 countries. These open source documents will also include metrics on the number of images that are indexed and labeled, algorithms that are in the pipeline and-qualitative feedback on the experience for all stakeholders: researchers, industry, hospitals and imaging centers, radiology labelers.

By indexing the world’s imaging pathologies, we see a direct path to meaningful impact. Radiology data that has been siloed will be surfaced, contextualized, and labeled for specific use cases. Significantly, this index will be made available to any researcher or company with the desire to build useful tools that will also positively impact healthcare delivery. 

This will dramatically scale up the development of computer vision algorithms that can be deployed in environments where radiologists are scarce, which includes much of Africa and South America. The ability to detect severe, potentially life-threatening, imaging findings will not only help the community directly, but will also provide a 30,000 view to global health needs. Imagine a world where algorithms deployed in hospitals throughout developing countries could detect, in real-time,  the incidence and geographical spread of infectious diseases, such as that seen during the COVID-19 pandemic. 

Our database could also visualize worrisome trends that could manifest in radiology images, such as in abnormal obstetrical ultrasounds, congenital anomalies, organ dysfunction, or cancer spread that would warrant additional action by health agencies. Ultimately, at our core, is a strong desire to remove barriers to the efficient and safe delivery of healthcare. We believe that this is not only a sound business strategy, but also one that will hopefully save lives. 

Hospitals and healthcare providers are looking to step into the 21st Century - digitizing and have searchable healthcare documents will lead to having improved patient outcomes. The increased data storage capacity of PACS systems and an emphasis on diagnostic testing has led to a boom in requests for radiology images. While this is exciting news, the human limit for interpretation has been reached, resulting in an increased risk of medical errors, as well as increased risk of fatigue and burn-out for radiologists, leading to disruption to and dilution of patient care. Artificial intelligence is one promising tool to mitigate these risks. 

Beyond developing deep, mutually beneficial relationships with hospitals, we prioritize  Our core solution makes use of many aspects of artificial intelligence: automation, natural language processing (NLP), classification and segmentation.

Gradient Health, Inc partners with hospitals and healthcare partners to collect data that it then indexes, sorts, and annotates upon request. Their incentive lies with the fact that many hospitals and healthcare providers are not aware of the data and how valuable it is when it is  organized and searchable. 

Gradient does not own any data that it aggregates: complete ownership of the data stay with the data owners, namely hospitals and private clinics.  

Gradient Health, Inc is dedicated to enriching its company culture in hiring candidates from diverse backgrounds. Our team comes from a variety of backgrounds, races, religions, and other metrics of diversity. As for our technology, we focus on acquiring medical data that represents both geographical diversity as well as ethnicity, age, sex and race. Gradient Health, Inc is improving medical algorithms robustness by curating large, diverse datasets. Gradient has a global view and continues to build data partnerships with hospital systems and clinics around the world, while also building an international research community to build equity within communities.

We are a for-profit private start-up that aims to bring together A.I. companies needs for medical data with data partners' desires to be fairly reimbursed for their contributions. We provide an annotation service for DICOM images, in addition to a DICOM viewer and access to annotated, off-the-shelf datasets.

We have two major stakeholders in our business: our data partners and our A.I. company partners. Our A.I. companies act as our customers and purchase access to annotated DICOM images or utilize our DICOM viewer to annotate and analyze their images. For our data partners, we create a revenue share model with per utilized image in addition to offering a free PACS system for hospitals in developing countries to store their hospital information. 

Gradient Health is currently generating income over $100,000 per year, with a goal of reaching $750,000 in 2022. We have already raised $2.5M in seed round funding with plans to extend to a seed round to assist in hiring talent and deepening partnerships to reach this goal.