HIPAA PROTECTION FOR MEDICAL IMAGES

Methods, systems, and apparatus for receiving, from a provider server and at an image processing server, medical data including at least one image; determining, by the image processing server using a neural network, the at least one image within the medical data contains PII; in response to determining that the at least one image in the medical data contains PII, classifying, by the neural network, at least one medical characteristic of the image; generating, by the image processing server using the medical characteristic classification, at least one metadata tag that describes the medical characteristic; modifying, by the image processing server, the medical data to replace the image with the generated metadata tag; and transmitting, by the image processing server to a transaction server, the modified medical data.

BACKGROUND

The Health Insurance Portability Accountability Act (HIPAA) outlines requirements for physicians, caregivers, and insurance providers when handling healthcare Personally Identifiable Information (PII) in order to protect this information from fraud or theft. Violations of HIPAA can result in stiff financial penalties for the offending party.

SUMMARY

This specification relates to a system that provides HIPPA protections when handling medical imagery data. In some instances, medical data must be transferred to different parties within a healthcare system during the end-to-end process of administering care to a patient. For example, when submitting an insurance claim to a provider, the provider will generally require some form of evidence to verify the nature and scope of the care that was provided by the physician. Medical data is one form of evidence that can be used to verify the level of care.

However, precautions need to be taken to avoid violating HIPAA requirements when handling medical data that contains PII. For example, medical imagery data can fall under HIPAA protection if the images can be used to identify a specific patient. While imagery data can be helpful in efficiently and succinctly communicating a medical condition or status, sending of this imagery data to other parties (e.g., an insurance provider) presents financial risk to physicians if these parties are not equipped to handle PII in a manner that satisfies HIPAA requirements.

While physicians are capable of summarizing the nature of imagery data, as opposed to providing the imagery data directly, this process is time consuming and not practicable in view of the large amounts of patients that a physician may treat in the course of their practice. Moreover, written descriptions can be unreliable ways to communicate information clearly if they are not legible or if unfamiliar medical nomenclature is used. Delegating the responsibility of summarizing imagery data to an assistant or helper who is not a trained physician risks losing critical medical information in the resulting translation. Additionally, manually transcribing descriptions of medical data does not have the advantage of trend analysis and machine learning that can be performed by Artificial Intelligence techniques.

One technique that can be used to accurately, efficiently, and securely describe medical imagery data is the use of Artificial Intelligence (AI) to summarize this information such that it can be shared in manner that satisfies HIPAA requirements. For example, a neural network can be deployed to parse medical claims to determine if any images contain PII and replace any sensitive information with metadata tags that accurately and securely convey the medical characteristics present in the image. As a result, the provider is presented with information sufficient to expediently process a physician's claim, while the physician is protected from any penalties that would result from accidently sharing PII with an unauthorized party.

In some examples, this neural network operates in an image processing server that receives claims from caregiver servers prior to transmission to billing servers. Upon receiving the claim, the image processing server deploys the neural network to parse the claim for sensitive images, classify medical characteristics within the images, replace these medical characteristics with metadata tags, and forward the modified claim to the billing servers. In some examples, this process is made seamless for the caregiver such that when submitting a claim, the caregiver need only designate the intended end recipient of the claim (e.g., an insurance provider's billing server or service). In this example, the image processing server would intercept the claim traffic, perform any necessary processing, and release the claim to be transmitted to the originally indicated recipient.

The subject matter described in this specification can be implemented so as to realize one or more of the following advantages. The use of neural networks to process medial imagery data can prevent physicians from violating HIPAA requirements when sharing medical data with insurance providers and other parties, thus avoiding considerable financial penalties. Additionally, the use of these techniques will allow patients to have confidence that their healthcare provider or physician is adequately protecting their PII. The knowledge that such information is shared securely with these techniques will also streamline the claim process, as physicians will more confidently share corroborating information with providers. The use of Al to review medical imagery data can also identify medical and financial patterns and trends otherwise beyond the perception of a human being. Moreover, Al can identify patterns between medical conditions and characteristics by leveraging the combined learning experience gain by processing many claims and imagery data across a community of patients.

DETAILED DESCRIPTION

FIG.1is an example medical provider and transaction system100. The example system100includes various user devices102, a network104, a caregiver server112, an image processing server114, and a billing server116.

In some examples, user devices102serve as the originating point for medical data. Examples of user devices include, but are not limited to, smartphones, desktop computers, wearable devices, smartwatches, monitoring devices, implanted devices, diagnostic equipment, scanning devices, or any other device suitable to collect medical data.

In some examples, a “caregiver” is an individual or entity involved in the healthcare of a patient. Some examples of caregivers include physicians, doctors, dentists, orthodontists, specialized practitioners, nurses, assisted living and nursing home personnel, and assistants for all of the previously mentioned roles. Other examples of caregivers are possible. When a caregiver collects medical data from a user device102, this information is uploaded to a caregiver server112for electronic storage, processing, and further disposition. For example, a patient who has seen a doctor for a broken bone can receive an X-ray to allow the doctor to evaluate the extent of the injury. After collecting the X-ray from a machine102, the resulting scans are then uploaded110(“A”) to the doctor's server112for storage. In another example, a dermatologist may collect images of a patient's skin condition for record keeping. Because the dermatologist sees many patients, this particular patient's records are organized in a file specific to that patient on a server112.

While the term “server” is used to describe the caregiver's data storage, it should be understood that the caregiver server112can also be any device that has suitable electronic storage and transmission capabilities. For example, a caregiver server112can also be the care giver's desktop computer, smart device, or a combination of these devices.

The caregiver server112is connected to a network104that allows communication to various other network destinations. For example, network104can be the internet, an intranet, a wireless network, or a cellular network.

The network104connects the caregiver server112to various billing servers116. In some examples, billing servers116are associated with insurance providers that finance medical treatment for patients. While a “server” is used to describe billing server116, like the caregiver server112, various other electronic devices may perform the roles of the billing server116(e.g., a desktop computer, smart device, or a combination of these devices). In some examples, a billing server116can also be accessed by the caregiver server112through an electronic payment portal or application that is executing on the caregiver server112, billing server116, or another electronic device (not pictured).

In the previous example where X-rays are collected, a physician uploads an insurance claim118(“B”) to the insurance provider's billing server116through network104to receive payment for the treatment. This claim includes evidence that corroborates the level of care the physician claims they provided to the patient. In this example, the physician includes the collected X-rays as this evidence. However, the X-ray in this example is PII as it identifies the particular patient from which it was collected. In many cases, the physician or caregiver server112may not have direct knowledge of all of the parties at the billing server116that handle or process the claim. If the billing server116or insurance provider personnel are not equipped to handle the PII in the transmitted claim118, the sharing of the X-ray scans would result in a HIPPA violation and substantial financial penalties for the physician and insurance provider.

The image processing server114monitors the traffic from the caregiver server112to the billing server116over network104and detects the claim118(“C”). In some examples, the image processing server114monitors all network traffic from the caregiver server112. In other examples, the image processing server114monitors only claims118that are submitted, or only traffic that has been flagged for review. When reviewing the claim118, the image processing server114parses the claim118to determine if the claim118contains any images containing PII are present.

To parse the claim118, the image processing server114deploys a neural network within an image processing algorithm (IPA)115capable of recognizing medical characteristics. The image processing server can employ any neural network suitable for feature recognition, for example, a convolutional neural network (CNN), a Recursive Cortical Network (RCN), or other types of neural networks suited for feature recognition.

When reviewing images, the IPA115examines the image to determine holistically if the image should be classified as containing PII. Once an image has been determined to contain PII, the IPA115then decides which features within the image are medically relevant by classifying medical characteristics within the image. For example, when reviewing an X-ray of a broken bone, the IPA115would identify the bone break as a relevant medical characteristic.

What medical characteristics are relevant can vary depending on the nature of the claim and associated imagery data. For example, medical characteristics classified by the IPA115can include bone breaks, lacerations or abrasions, medical conditions, or other features that a physician or provider would deem medically relevant. In addition to classifying the medical characteristic, the IPA115can also determine traits that describe the medical characteristic. For example, when reviewing a bone break, the IPA115can determine which bones are affected, as well as the type of fracture. Other examples of traits that can be identified include the severity, scope, or extent of a medical condition, the length or depth of a laceration, or other traits that would be relevant to determining the extent of a medical characteristic.

In some examples, to assist the IPA115in correctly identifying medical characteristics, the IPA115is trained on other instances of medical data and claims. For example, the IPA115can be provided with a training set of bone breaks along with the necessary information to correctly classify any relevant traits. In some examples, a generic set of training data is provided to the IPA115for initial training. In other examples, the IPA115can be continually trained and retrained on medical data it encounters during its use. For example, after generating a metadata tag defining a medical characteristic, the IPA115and image processing server114can receive correction data indicating that the classification and/or description of the medical characteristic generated by the IPA115is incorrect. In this case, the IPA115uses the correction data to retrain on the correct classification and/or description.

In some examples, the IPA115can reference previous relevant classifications of medical characteristics made in other instances of medical data for the same patient, a different patient, or a combination of patients. For example, when classifying a bone break in one patient, the IPA115can reference previous classifications of similar injuries in other patients to determine that this type of fracture will likely lead to further medical complications. Upon determining this correlation, the IPA115outputs a metadata tag that describes the relationship between the two classifications (e.g., the bone break in the one patient and the future complications from other patients). In this example, the IPA115creates a metadata tag that notes the high probability of future complications for this patient. The process of generating metadata tags is described in greater detail below. With this capability, physicians can leverage machine learning techniques to proactively identify trends in medical data that may otherwise be beyond human perception. Moreover, the financial data from trending claims can be used by insurance providers to improve their business analysis, for example, preemptively identifying that a patient may incur future medical costs for a specific injury or condition.

In some examples, the IPA115can reference narratives in the claim when determining which medical characteristics are relevant. For example, the IPA115can perform text recognition on the claim and determine the claim is being submitted for a broken bone. With this knowledge, the IPA115would then consider bone breaks within the image to be highly relevant and search for these features preferentially. In some examples, the IPA115assigns a weighting to relevant medical characteristics discovered through text recognition which are then used by the neural network in feature recognition.

After the IPA115has classified the medical characteristics and determined any relevant traits, the IPA115then generates metadata tags to describe the medical characteristics and traits. These metadata tags are then used to replace each instance of sensitive imagery data in the claim118. This process is repeated for each medical characteristic within the imagery data of the claim118. Once all metadata tags have been generated, the IPA115then modifies the claim118to generate a modified claim120. The modified claim120is then transmitted to a recipient over network104by the image processing server114(“D”). In some examples, the image processing server114returns the modified claim120to the caregiver server112for review prior to transmission to the billing server116(“E”). In other examples, the image processing server114transmits the modified claim120to the billing server116in a seamless process.

FIG.2is an example process200for replacing medical images with metadata. Process200includes receiving medical data, to include at least one image202. As described with respect toFIG.1, image data can originate from a variety of sources or devices.

Process200includes determining the at least one image within the medical data contains PII204. As described with respect toFIG.1, an image processing algorithm is deployed that can recognize sensitive images.

Process200includes classifying at least one medical characteristic of the image204. As described with respect toFIG.1, medical characteristics can be classified based on various factors, and what medical characteristics are relevant can depend on the nature of the claim. This step of process200is repeated for each medical characteristic within the image.

Process200includes generating at least one metadata tag that describes the medical characteristic208. As described with respect toFIG.1, the metadata tag describes the medical characteristic and any relevant traits. This step of process200is repeated for each medical characteristic within the image.

Process200includes modifying the medical data to replace the image with the generated metadata tag210. As described with respect toFIG.1, the metadata tag describes the medical characteristic and any relevant traits. This step of process200is repeated for each generated metadata tag.

Process200includes transmitting the modified medical data212. As described with respect toFIG.1, this transmission can be to a caregiver server for final approval, or to a billing server for disposition of the claim.