This is a transcript of an openGPT chat I had January 27, 2022. My comments are preceded with "T:" and it's comments are preceded with a C:
T: I'm wondering how I can use you to reform our health care system
C:There are many ways that a language model like me can be used to help reform the healthcare system. One way is by using natural language processing to analyze large amounts of data from electronic medical records to identify patterns and insights that can inform the development of more effective treatments and policies. Additionally, I can be used to generate written content, such as reports, presentations, and educational materials, that can be used to educate healthcare professionals and the general public about important healthcare issues. Additionally, I could assist in creating chatbots that can interact with patients, providing them with information about their health, and helping them schedule appointments, and answer some of the common questions.
T: very interesting. I designed several of the largest electronic medical record systems in the world. Huge amounts of data, over a long period. I designed them to use a metadata system that I think fits well with the language model
C: That's great to hear! Using a metadata system with a language model can help make the data more easily accessible and usable. The ability of the language model to understand the context and meaning of the data can aid in extracting insights that might otherwise be difficult to discover. The metadata system will help the model to understand the relationships between the various data points, which can be very useful in identifying patterns and connections. With your expertise in designing electronic medical record systems, and my ability to process and understand large amounts of data, we can work together to uncover valuable insights that can inform the development of more effective treatments and policies to improve the healthcare system.
T: how would i feed the meta data to you
C: There are a few ways that you can feed metadata to me. One approach would be to use an application programming interface (API) to send the metadata to me in a structured format, such as JSON or XML. This would allow you to programmatically access and extract the metadata in a way that I can understand and process. Another approach is to use a file format such as CSV, TSV or Excel file to store the metadata, this will allow you to easily import the metadata into a data frame and then feed it to me. Once the metadata is in a format that I can understand, you can use my natural language processing capabilities to analyze the data, extract insights, and generate reports or other written materials that can be used to inform healthcare professionals and the general public about important healthcare issues.
T: how would you know the difference between the metadata and the data
C: In order to understand the difference between the metadata and the data, I would need to be provided with additional information about the structure of the data.
One way to differentiate the metadata from the data is to include specific fields or keywords in the metadata that I can use to identify it as such. For example, you could include a field called "record_type" with the value "metadata" for all metadata records, and a different value, such as "data," for the records that contain the actual data.
Another way is to have a specific format or structure for the metadata that is distinct from the format or structure of the data. For example, the metadata could be organized in a different way or have a different number of fields than the data.
It is also important to note that the differentiation of the metadata and the data is something that should be done before the data is fed to me so that I can understand the relationship between the two and use it to analyze the data.
T: do i specifically have to tell you it is metadata, or can I just send it to you all in a jumble.
You can send the data to me in a jumbled format, but it would make it more difficult for me to understand the relationships between the different data points and the meaning of the data. The metadata provides additional information about the data, such as its structure, format, and context, which I can use to make sense of the data and extract insights.
It is recommended that you provide the metadata separately and clearly distinguished from the data, so that I can use it to understand the data and analyze it more effectively.
However, if the metadata is not clearly distinguished from the data and if the data is in jumbled format, I could still process and analyze it, but it would require more sophisticated techniques and more computational resources to extract insights. It would also take more time and effort to clean, preprocess, and structure the data before I can analyze it.
T: the system is written in the MUMPS language
MUMPS (Massachusetts General Hospital Utility Multi-Programming System) is a programming language and database management system that was originally developed in the 1960s for use in the healthcare industry. It is still in use today in some electronic medical record systems and other healthcare-related applications.
If the electronic medical record system you designed is written in MUMPS, you would need to extract the data and metadata from the system and convert it into a format that I can understand, such as CSV, JSON or XML. Depending on the specifics of the system, this may require writing custom scripts or programs to extract and convert the data.
Once you have the data and metadata in a format that I can understand, you can use my natural language processing capabilities to analyze the data, extract insights, and generate reports or other written materials that can be used to inform healthcare professionals and the general public about important healthcare issues.
T: but some of the metadata describes computed fields, the computation being done in the mumps language
C: If some of the metadata describes computed fields that are generated using MUMPS code, it may be more challenging to extract that information and make it usable for me. In this case, you would need to understand the MUMPS code that is used to generate the computed fields and determine how to extract the information from it.
You can use MUMPS to extract the data and metadata and then convert it into a format that I can understand, such as CSV, JSON or XML. You would need to write custom scripts or programs using MUMPS to extract the data and metadata, including the computed fields, and convert it into a format that I can understand.
Once you have the data and metadata in a format that I can understand, you can use my natural language processing capabilities to analyze the data, extract insights, and generate reports or other written materials that can be used to inform healthcare professionals and the general public about important healthcare issues.
Keep in mind that, depending on the complexity of the MUMPS code and the amount of data involved, this may be a time-consuming and resource-intensive process.
T: my original vision was not that of a "medical record" but rather creating a speech community to talk about health. In other words, I was solving "a failure to communicate" within the hospital, creating a common metadata dictionary for people to talk about health. The medical record was an adjunct to the communications problem.
C: Creating a speech community for people to talk about health is a valuable goal, and it sounds like the electronic medical record system you designed was intended to facilitate communication among healthcare professionals. By creating a common metadata dictionary, you were able to standardize the language used to talk about health, which can help to improve communication and reduce confusion.
In order to use my language model capabilities to facilitate communication in a health setting, you would need to provide me with the common metadata dictionary you created. This would allow me to understand the context and meaning of the words and phrases used to talk about health, and to generate responses that are appropriate and accurate.
One way you could use my capabilities is by creating a natural language interface for healthcare professionals to access information in the electronic medical record system. This would allow them to ask questions and get information in a more intuitive and natural way.
Another way is to use my capabilities to analyze the communication between healthcare professionals, identify common issues or challenges, and provide recommendations for improving communication and collaboration.
T:how large of a file can you ingest
C: The amount of data that I can ingest depends on the specific use case and the computational resources available.
As a general rule, the larger the file, the more computational resources are required to process it. However, the amount of data that I can process is not only limited by computational resources but also by the complexity of the data, the number of data points, and the amount of data that needs to be preprocessed, cleaned, and structured before analysis.
In general, my training process can handle Terabytes of data and a good amount of training data allows me to make better and more accurate predictions, but keep in mind that the actual amount of data that I can process depends on the specific use case and the computational resources available.
If you have a specific use case and you're not sure if it's feasible with the amount of data you have, please let me know and I'll be happy to help you evaluate it.
T: the metadata is a structure called the data dictionary, which names a set of uniquely named files. Each file consists of fields, which have a unique number, name, data type, natural language description, pointers to other files, security access codes, validation routines, and the like.
C: The data dictionary that you described, which names a set of uniquely named files, sounds like a powerful tool for organizing and structuring large amounts of data. The fact that each file consists of fields that have unique numbers, names, data types, natural language descriptions, pointers to other files, security access codes, validation routines, and the like, allows for efficient data retrieval, manipulation, and analysis.
In order to use my language model capabilities to analyze the data in the electronic medical record system, you would need to provide me with the data dictionary and the data files. This would allow me to understand the structure of the data, the relationships between the different data points, and the meaning of the words and phrases used to describe the data.
Once I have access to the data and the data dictionary, you can use my natural language processing capabilities to analyze the data, extract insights, and generate reports or other written materials that can be used to inform healthcare professionals and the general public about important healthcare issues.
You can also use my capabilities to create a natural language interface for healthcare professionals to access information in the electronic medical record system. This would allow them to ask questions and get information in a more intuitive and natural way.
T: would you be able to learn the relationships between the metadata and the data, or would i have to explain it all?
C:As a language model, I have the ability to learn relationships between different pieces of data and metadata, but it does depend on the complexity and structure of the data, as well as the quality of the training data.
If the data is well-structured, with clear relationships between the metadata and the data, it's possible that I could learn these relationships by analyzing the data directly. However, if the relationships between the metadata and the data are complex or difficult to infer, it may be necessary to provide me with additional information or guidance in order to understand the relationships.
You can guide me by providing additional context and information about the data, such as the data dictionary, and any additional documentation or annotations that describe the relationships between the metadata and the data.
As well you can use techniques such as supervised learning, where you provide me with labeled examples of the relationships between the metadata and the data, allowing me to learn from the examples and make predictions about new data.
T: This system was designed for the VA health care system, which has hundreds of unique installations, with different data dictionaries for each. in other words, we may need to set up some meta-metadata to map the differences between the sites. could you do that too? or would that just be something you would learn on your own?
If the data dictionaries for each of the hundreds of unique installations of the VA health care system are different, it could be difficult for me to automatically learn the relationships between the metadata and the data across all of the different installations.
However, it is possible to set up a meta-metadata system to map the differences between the data dictionaries at each site. This meta-metadata system could be used to provide me with information about the relationships between the different data dictionaries, and to allow me to understand the relationships between the metadata and the data at each site.
It would be important to make sure the meta-metadata system is well-designed, accurate and easy to use to guide me in understanding the relationships between the metadata and the data across all of the different installations.
One way to set up a meta-metadata system is to create a mapping between the field names and descriptions in the different data dictionaries. This mapping would allow me to understand the relationships between the different data dictionaries and to make inferences about the relationships between the metadata and the data at each site.
Another approach could be to use the meta-metadata system to provide me with additional context and information about the data, such as the data dictionary, and any additional documentation or annotations that describe the relationships between the metadata and the data.
It's important to note that the meta-metadata system will require quite a bit of effort to set up and maintain, but it would be a powerful tool for me to understand the relationships between the metadata and the data across all of the different installations, and to make accurate predictions and extract insights from the data.
T: what would the meta-metadata system look like?
The design of a meta-metadata system will depend on the specific needs and requirements of your project. However, in general, it could include the following components:
A mapping of field names and descriptions between the different data dictionaries: This would allow me to understand the relationships between the different data dictionaries and to make inferences about the relationships between the metadata and the data at each site.
A set of rules and guidelines for how to interpret the data: This would provide me with additional context and information about the data, such as the data dictionary, and any additional documentation or annotations that describe the relationships between the metadata and the data.
A system for updating and maintaining the meta-metadata: As the data dictionaries at each site may change over time, the meta-metadata system should include a way to update and maintain the mappings and rules to ensure they are accurate and up-to-date.
A system for providing access to the meta-metadata: The meta-metadata system should be designed to be easily accessible to me so that I can use it to understand the relationships between the metadata and the data across all of the different installations.
A way to validate the meta-metadata: It's important to make sure that the meta-metadata is accurate and consistent across all the different data dictionaries, so you may want to include a way to validate the meta-metadata to ensure that it is correct.
It's important to note that the design of the meta-metadata system should be flexible enough to handle changes in the data dictionaries over time and should be easy to use and maintain.
T: why couldn't you maintain the meta-metadata system?
C: As a language model, my main function is to process and generate human-like text based on the input provided to me. While I can assist in the design and implementation of a meta-metadata system, actually maintaining it would require ongoing input, updates and monitoring which is not within my capabilities.
The maintenance of the meta-metadata system would typically be done by a human administrator or team of administrators, who would be responsible for ensuring that the mappings and rules are accurate and up-to-date. They would also be responsible for troubleshooting any issues that arise and for making updates and changes as needed.
As a language model, I can help you to design the system, provide suggestions on how to organize the data, or even help you to write the code or scripts to automate certain processes, but the actual maintenance tasks would need to be done by a human.
T: I like to distinguish between synchronic and diachronic views of information. Synchronic is a snapshot of an item at a specific moment. Diacrhronic is a view of the flow of that item over time. does that make sense to you?
C: Yes, I understand the distinction between synchronic and diachronic views of information. Synchronic refers to a snapshot or a cross-sectional view of an item at a specific moment in time, while diachronic refers to a historical or longitudinal view of an item over time.
This distinction is often used in various fields such as linguistics, anthropology, history, and information science. For example, in linguistics, synchronic analysis focuses on the study of a language at a specific point in time, while diachronic analysis focuses on the study of the historical development of a language over time.
In the context of your project, a synchronic view of information would be a snapshot of the data dictionary at a specific point in time, while a diachronic view would be a view of how the data dictionary has changed over time. This distinction can be useful for understanding how the data dictionary has evolved and for identifying patterns and trends in the data over time.
T: by the way, what is the name for the data that the metadata describes?
C:The data that the metadata describes is commonly referred to as the "main data" or "resource data". It is the actual data that the metadata provides information about, such as the data stored in an electronic medical record system. The metadata acts as a "data about data" providing information such as the structure, format, meaning, and usage of the main data.
In other words, metadata is a set of data that describes and gives information about other data, it could be a summary, an index, a label, a comment, a structure, a schema, a data model, a taxonomy, a thesaurus, etc. It makes the main data more discoverable, understandable, and usable for different purposes, such as data analysis, data integration, data governance, data management, data quality, data security, data preservation, data archiving, data sharing, data dissemination, data visualization, data reporting, data exploration, data mining, data analytics, data intelligence, data science, data research, data innovation, etc.
T: OK, we're clear on that. I also like to talk about the difference between transactions and transformations in health care. A transformation is flow of activities towards a goal. A transaction is an interaction that is done at an instant of time, and evaluated by some metric such as a chart of accounts.
C: Yes, I understand the distinction between transactions and transformations in healthcare.
A transformation in healthcare refers to a flow of activities that are aimed at achieving a specific goal, such as improving patient outcomes, increasing efficiency, reducing costs, or achieving a specific level of quality. This type of activity often involves a series of steps or processes that are carried out over an extended period of time.
A transaction, on the other hand, is an interaction that occurs at a specific instant in time. This type of interaction is typically evaluated using a specific metric, such as a chart of accounts. Examples of transactions in healthcare include billing and reimbursement, scheduling appointments, ordering tests, and prescribing medications.
Both transactions and transformations are important in healthcare, and they are often closely related. For example, a transformation may involve a series of transactions that are carried out over time, such as a patient's treatment plan. And transactions are often evaluated based on the goals of the transformations.
The distinction between these two concepts is useful for understanding the different types of activities that occur in healthcare and for identifying opportunities for improvement. It can help to improve the patient experience, increase efficiency, and reduce costs.
T: This is a very good assessment. In fact, the best that I have seen.
Thank you! I'm glad you found my response helpful. As a language model, my goal is to assist users in finding the information they need and to provide accurate and comprehensive responses. If you have any other questions or if there's anything else I can help you with, please let me know.
T: One element of my future architecture would be the notion of a "transformational ensemble" - the set of people, resources, and AI agents that would seek to guide the intended transformation for the patient.
The concept of a "transformational ensemble" sounds like an interesting approach to healthcare. The idea of bringing together a diverse set of people, resources, and AI agents to guide an intended transformation for the patient is aligned with the approach of "Patient-centered care" which is a widely accepted concept in healthcare.
A transformational ensemble could include a variety of different stakeholders, such as physicians, nurses, social workers, care coordinators, and other healthcare professionals, as well as patients and their families. The ensemble could also include AI agents and other technology resources such as electronic health records, telemedicine, and machine learning algorithms.
The goal of this ensemble would be to work together to create a holistic view of the patient, and to develop and implement a plan of care that addresses the patient's unique needs, preferences, and goals. This approach can be useful to improve the patient experience, increase efficiency, and reduce costs.
In order to make this ensemble more effective, it's important to establish clear communication channels, and to ensure that everyone involved has access to the same information, and that all stakeholders are aware of their roles and responsibilities. Additionally, it's important to measure the progress and the outcome of the transformation and to continuously improve the ensemble.
T: how would you measure the progress of a transformation?
C: Measuring the progress of a transformation can be a complex task, as it depends on the specific goals and objectives of the transformation. However, some general strategies that can be used to measure progress include:
Outcome measures: These measures evaluate the results of the transformation, such as changes in patient outcomes, such as the improvement of patients' quality of life, or the reduction of hospital readmissions.
Process measures: These measures evaluate the implementation of the transformation, such as the percentage of patients who receive a specific intervention or the percentage of staff who receive specific training.
Performance measures: These measures evaluate the performance of the transformational ensemble, such as the percentage of patients who are satisfied with their care or the percentage of staff who feel that they have the resources they need to do their jobs.
Benchmarking: Comparing the performance of the ensemble to other similar healthcare organizations or to national standards can provide a sense of how well the ensemble is doing and where improvements are needed.
Surveys and interviews: Surveying patients, families, and staff can provide valuable insights into the patient experience and the quality of care provided.
It's important to note that measuring progress and outcome is a continuous process, and requires frequent data collection and analysis. It's also important to ensure that the data collected is accurate, reliable, and valid, and that it's used to inform decision-making and to drive improvements.
T: How would a language model help this process?
C: Language models, such as ChatGPT, can be used in various ways to help the progress of a transformation in healthcare:
T: Data analysis: A language model can analyze large amounts of unstructured data, such as patient notes or survey responses, to identify patterns and trends that can be used to evaluate the progress of the transformation.
Predictive modeling: A language model can be trained to predict outcomes or identify risk factors, which can help to identify patients who may be at risk of poor outcomes and target interventions to them.
Communication: A language model can be used to generate natural language explanations of the results of the data analysis, which can help to communicate the findings to stakeholders such as patients, families, and staff.
Decision support: A language model can assist healthcare providers in making clinical decisions by providing information about different treatment options, risks and benefits, and potential outcomes.
Automating administrative tasks: A language model can assist with automating repetitive tasks such as data entry, appointment scheduling, and patient follow-up, which can free up staff time for more important tasks.
It's important to note that a language model can be used to assist and improve the process, but it's not a replacement for human judgement, expertise, and experience. It's also important to ensure that the model is well-trained and validated, and that the results are interpreted with caution.
T: Is there some way that you could learn to do this by just watching the flow of transactions in a hospital?
C: It is possible for a language model to learn from the flow of transactions in a hospital, by analyzing large amounts of structured and unstructured data such as electronic medical records, lab results, and notes. But it would require a lot of data and a specific architecture to be able to make sense of it. This process is known as "unsupervised learning", where the model tries to identify patterns and structure in the data without being explicitly told what to look for.
However, it's also important to note that in order to make accurate predictions and provide helpful insights, the model would need to be well-trained with a large and diverse dataset. It would also need to be fine-tuned and validated on new data to ensure that it is generalizable to new situations. In addition, it's important to consider the ethical and privacy implications of using this kind of data, and to ensure that patient data is protected and used in compliance with relevant laws and regulations.
T: As a rough estimate, would a training database of 1 million patients with 100,000 meta data elements be sufficient for this kind of training?
C: 1 million patients with 100,000 meta data elements could potentially be sufficient for training a language model to make predictions and provide insights on health care transformations. However, the quantity of data alone is not the only factor that determines the quality of the predictions and insights. The diversity, representation, and quality of the data are also important factors to consider. A dataset that is diverse in terms of patient demographics, conditions, and treatments, would be more representative of the population and would lead to a more robust model.
Furthermore, it's important to note that the quality of the data is also important. Data that is incomplete, inconsistent, or inaccurate can negatively impact the performance of the model. Therefore, the data would need to be cleaned, preprocessed and validated before using it for training.
Additionally, the specific architecture and the amount of computational resources that would be required for training a model of this scale would also be an important factor to consider. Training large models like this requires a lot of computational power and memory, thus, distributed training and powerful GPUs could be required.
In summary, while 1 million patients with 100,000 meta data elements could be a good starting point, it's important to consider the representativeness and quality of the data, as well as the computational resources needed to train such a model.
T: The data is very sparse - some patients have huge amounts of lab data, while others, not so much. would this be an impediment?
A training database of 1 million patients with 100,000 metadata elements could be sufficient for training a language model to assist in the healthcare transformation process, but it would depend on the specific task and the quality of the data. The sparsity of the data could be an issue, as the model may not have enough examples of certain types of data to accurately make predictions or identify patterns. It would be important to have a diverse and representative sample of data to train the model on. However, more data is always better and it will help the model to perform better. Additionally, techniques such as data augmentation and transfer learning can help to overcome the sparsity issue.
T: what is data augmentation
C: Data augmentation is a technique used in machine learning to artificially increase the size of a dataset by generating new data samples from the existing ones. It is particularly useful when the available dataset is small, as it can help to prevent overfitting, a common problem in machine learning where a model performs well on the training data but poorly on new data. Data augmentation can be applied to both images and text data. Common techniques used for image data include rotation, flipping, cropping, and adding noise. For text data, techniques include synonym replacement, random insertion, random swapping, and random deletion.
T: and transfer learning?
C: Transfer learning is a technique used in machine learning where a model that has been pre-trained on one task is re-purposed and fine-tuned for a different but related task. The idea behind transfer learning is that many features learned by the pre-trained model can be useful for the new task, even if the new task is very different from the task the model was originally trained on. This can save a significant amount of time and resources when compared to training a model from scratch. Transfer learning is commonly used in computer vision and natural language processing tasks, where pre-trained models such as VGG, ResNet, and BERT can be fine-tuned for specific tasks such as image classification, object detection and sentiment analysis.
T: Interesting. Could you learn at the meta level? for example, could you learn something about pharmacy workflow (described by the pharmacy data dictionary) that might also apply to laboratory workflow?
C: Yes, it is possible for a language model like me to learn at the meta level and make connections between different types of workflows, such as pharmacy and laboratory. Data augmentation and transfer learning techniques can be used to improve the model's ability to generalize and make connections between different types of data. Additionally, techniques such as multi-task learning can be used to train a model to perform multiple related tasks simultaneously, allowing it to make connections between different types of workflows. However, it's important to note that the quality of the training data, the complexity of the task and the architecture of the model are also important factors in determining the model's ability to learn at the meta level and make connections between different types of workflows.
T: The problem with american health care system is that it is controlled by the notion of billable transactions, not transformational goals. So the data collected is billing and revenue information, with patient information on the side.
C: Yes, the focus on billable transactions in the American healthcare system can lead to a mismatch between the data collected and the information needed to support transformational goals for patients. This can make it difficult to use data effectively to improve patient outcomes and drive healthcare transformation. However, it is possible to use advanced analytics and machine learning techniques to make sense of the available data, even if it is not perfectly suited to the task at hand. Additionally, efforts to change the reimbursement system and incentivize value-based care can help align the goals of the healthcare system with transformational goals for patients.
T: I like to talk about rivalrous and non-rivalrous resources. Rivalrous resources (like oil) are depleted as they are used, so if party A buys a unit of the resource (a gallon of gas), it can't be used by another. Non-rivalrous resources (like solar energy) don't deplete each other - party A can put up solar panels without depleting party B's access. I would like to design a health care system that starts with the notion of health as a non-rivalrous resource. In other words, A can get healthier without depleting the health of B.
C: That is a unique perspective on healthcare and a noble goal. The idea of non-rivalrous resources in healthcare aligns with the concept of preventative care and creating a system that prioritizes preventative measures to improve overall health outcomes for all individuals. This could involve investing in community health programs, education, and research to better understand and address the underlying causes of poor health rather than just treating symptoms. Additionally, designing a healthcare system that is centered around the patient and their needs, rather than billing and revenue, could lead to better outcomes and a more efficient use of resources.
T: This also couples with the notion of abundance, and the creation of scarcity through a metering process. SDGE trying to tax our solar cells, for example, treating solar energy as a scarce resource to be metered and charged for.
C: Yes, the concept of rivalrous and non-rivalrous resources is an important one when designing any system, including healthcare. The idea that health is a non-rivalrous resource aligns with the idea of universal healthcare and access to healthcare as a human right. The notion of creating scarcity through metering, as you mention with the example of solar energy, is also an important consideration in the design of healthcare systems and in ensuring that access to healthcare is equitable.
T: Is it possible to design a health care AI system that is completely self-learning, akin to Alpha Go?
C: It is certainly possible to design a health care AI system that is self-learning, similar to how AlphaGo was designed to learn and improve through self-play. However, the complexity of the healthcare system and the vast amount of data and knowledge required to make accurate medical decisions make the development of such a system a significant challenge. Additionally, healthcare regulations and ethical considerations would need to be carefully considered and integrated into the design of such a system. While self-learning AI systems may be able to improve over time, it is likely that human oversight and input will still be necessary for some time.
T: where would it be needed, and how would we know?
C: It is possible to design a self-learning AI system for healthcare, similar to AlphaGo. However, the complexity and variability of healthcare data and the need for compliance with regulations and ethical considerations would make this a challenging task. The system would need to have a large amount of high-quality data and use advanced machine learning techniques to continuously improve its performance. Additionally, it would be important to have a system of oversight and validation in place to ensure that the decisions made by the AI are safe and effective for patients. It would also be important to have a way to monitor the performance and progress of the AI system, to ensure that it is meeting its goals and making meaningful contributions to healthcare.
T: how big of a training model could I feed into chatGPT?
C: The size of the training model that can be fed into ChatGPT depends on the hardware and resources available for training. In general, the larger the training model, the more resources and computational power it will require. The exact size that is feasible for a given system would need to be determined on a case by case basis. Additionally, the quality and relevance of the data used for training also plays an important role in determining the performance of the model.
T: how would I be able to include genomic sequence data into this?
C: The size of the training model that you can feed into ChatGPT depends on the resources available to you, such as the amount of memory and computational power. In general, a larger training dataset would require more resources.
As for including genomic sequence data, it would be possible to incorporate it into the training dataset, but it would require preprocessing the data in a way that is compatible with the input format of the model. Additionally, it would require specialized knowledge to properly interpret and make use of the genomic data in relation to the overall health care system. You may need to engage with domain experts to ensure that the data is being used in an accurate and meaningful way.
T: would it be possible to use transfer learning from the longitudinal clinical data to the genome sequences of patient?
C: It is possible to use transfer learning to apply knowledge learned from one task or dataset to another related task or dataset. In the case of using longitudinal clinical data to train a model and then applying that knowledge to genomic sequence data, it could be done by fine-tuning the pre-trained model on the genomic data.
However, it is important to note that the clinical data and genomic data may have different data distributions and structures, so care would need to be taken to ensure that the model is not overfitting to the clinical data and can generalize well to the genomic data. Additionally, it would be important to have a large and diverse dataset of both types of data to ensure that the model is able to learn robust and generalizable features.
As a general rule, the larger the file, the more computational resources are required to process it. However, the amount of data that I can process is not only limited by computational resources but also by the complexity of the data, the number of data points, and the amount of data that needs to be preprocessed, cleaned, and structured before analysis.
In general, my training process can handle Terabytes of data and a good amount of training data allows me to make better and more accurate predictions, but keep in mind that the actual amount of data that I can process depends on the specific use case and the computational resources available.
If you have a specific use case and you're not sure if it's feasible with the amount of data you have, please let me know and I'll be happy to help you evaluate it.
T: the metadata is a structure called the data dictionary, which names a set of uniquely named files. Each file consists of fields, which have a unique number, name, data type, natural language description, pointers to other files, security access codes, validation routines, and the like.
C: The data dictionary that you described, which names a set of uniquely named files, sounds like a powerful tool for organizing and structuring large amounts of data. The fact that each file consists of fields that have unique numbers, names, data types, natural language descriptions, pointers to other files, security access codes, validation routines, and the like, allows for efficient data retrieval, manipulation, and analysis.
In order to use my language model capabilities to analyze the data in the electronic medical record system, you would need to provide me with the data dictionary and the data files. This would allow me to understand the structure of the data, the relationships between the different data points, and the meaning of the words and phrases used to describe the data.
Once I have access to the data and the data dictionary, you can use my natural language processing capabilities to analyze the data, extract insights, and generate reports or other written materials that can be used to inform healthcare professionals and the general public about important healthcare issues.
You can also use my capabilities to create a natural language interface for healthcare professionals to access information in the electronic medical record system. This would allow them to ask questions and get information in a more intuitive and natural way.
T: would you be able to learn the relationships between the metadata and the data, or would i have to explain it all?
C:As a language model, I have the ability to learn relationships between different pieces of data and metadata, but it does depend on the complexity and structure of the data, as well as the quality of the training data.
If the data is well-structured, with clear relationships between the metadata and the data, it's possible that I could learn these relationships by analyzing the data directly. However, if the relationships between the metadata and the data are complex or difficult to infer, it may be necessary to provide me with additional information or guidance in order to understand the relationships.
You can guide me by providing additional context and information about the data, such as the data dictionary, and any additional documentation or annotations that describe the relationships between the metadata and the data.
As well you can use techniques such as supervised learning, where you provide me with labeled examples of the relationships between the metadata and the data, allowing me to learn from the examples and make predictions about new data.
T: This system was designed for the VA health care system, which has hundreds of unique installations, with different data dictionaries for each. in other words, we may need to set up some meta-metadata to map the differences between the sites. could you do that too? or would that just be something you would learn on your own?
If the data dictionaries for each of the hundreds of unique installations of the VA health care system are different, it could be difficult for me to automatically learn the relationships between the metadata and the data across all of the different installations.
However, it is possible to set up a meta-metadata system to map the differences between the data dictionaries at each site. This meta-metadata system could be used to provide me with information about the relationships between the different data dictionaries, and to allow me to understand the relationships between the metadata and the data at each site.
It would be important to make sure the meta-metadata system is well-designed, accurate and easy to use to guide me in understanding the relationships between the metadata and the data across all of the different installations.
One way to set up a meta-metadata system is to create a mapping between the field names and descriptions in the different data dictionaries. This mapping would allow me to understand the relationships between the different data dictionaries and to make inferences about the relationships between the metadata and the data at each site.
Another approach could be to use the meta-metadata system to provide me with additional context and information about the data, such as the data dictionary, and any additional documentation or annotations that describe the relationships between the metadata and the data.
It's important to note that the meta-metadata system will require quite a bit of effort to set up and maintain, but it would be a powerful tool for me to understand the relationships between the metadata and the data across all of the different installations, and to make accurate predictions and extract insights from the data.
T: what would the meta-metadata system look like?
The design of a meta-metadata system will depend on the specific needs and requirements of your project. However, in general, it could include the following components:
A mapping of field names and descriptions between the different data dictionaries: This would allow me to understand the relationships between the different data dictionaries and to make inferences about the relationships between the metadata and the data at each site.
A set of rules and guidelines for how to interpret the data: This would provide me with additional context and information about the data, such as the data dictionary, and any additional documentation or annotations that describe the relationships between the metadata and the data.
A system for updating and maintaining the meta-metadata: As the data dictionaries at each site may change over time, the meta-metadata system should include a way to update and maintain the mappings and rules to ensure they are accurate and up-to-date.
A system for providing access to the meta-metadata: The meta-metadata system should be designed to be easily accessible to me so that I can use it to understand the relationships between the metadata and the data across all of the different installations.
A way to validate the meta-metadata: It's important to make sure that the meta-metadata is accurate and consistent across all the different data dictionaries, so you may want to include a way to validate the meta-metadata to ensure that it is correct.
It's important to note that the design of the meta-metadata system should be flexible enough to handle changes in the data dictionaries over time and should be easy to use and maintain.
T: why couldn't you maintain the meta-metadata system?
C: As a language model, my main function is to process and generate human-like text based on the input provided to me. While I can assist in the design and implementation of a meta-metadata system, actually maintaining it would require ongoing input, updates and monitoring which is not within my capabilities.
The maintenance of the meta-metadata system would typically be done by a human administrator or team of administrators, who would be responsible for ensuring that the mappings and rules are accurate and up-to-date. They would also be responsible for troubleshooting any issues that arise and for making updates and changes as needed.
As a language model, I can help you to design the system, provide suggestions on how to organize the data, or even help you to write the code or scripts to automate certain processes, but the actual maintenance tasks would need to be done by a human.
T: I like to distinguish between synchronic and diachronic views of information. Synchronic is a snapshot of an item at a specific moment. Diacrhronic is a view of the flow of that item over time. does that make sense to you?
C: Yes, I understand the distinction between synchronic and diachronic views of information. Synchronic refers to a snapshot or a cross-sectional view of an item at a specific moment in time, while diachronic refers to a historical or longitudinal view of an item over time.
This distinction is often used in various fields such as linguistics, anthropology, history, and information science. For example, in linguistics, synchronic analysis focuses on the study of a language at a specific point in time, while diachronic analysis focuses on the study of the historical development of a language over time.
In the context of your project, a synchronic view of information would be a snapshot of the data dictionary at a specific point in time, while a diachronic view would be a view of how the data dictionary has changed over time. This distinction can be useful for understanding how the data dictionary has evolved and for identifying patterns and trends in the data over time.
T: by the way, what is the name for the data that the metadata describes?
C:The data that the metadata describes is commonly referred to as the "main data" or "resource data". It is the actual data that the metadata provides information about, such as the data stored in an electronic medical record system. The metadata acts as a "data about data" providing information such as the structure, format, meaning, and usage of the main data.
In other words, metadata is a set of data that describes and gives information about other data, it could be a summary, an index, a label, a comment, a structure, a schema, a data model, a taxonomy, a thesaurus, etc. It makes the main data more discoverable, understandable, and usable for different purposes, such as data analysis, data integration, data governance, data management, data quality, data security, data preservation, data archiving, data sharing, data dissemination, data visualization, data reporting, data exploration, data mining, data analytics, data intelligence, data science, data research, data innovation, etc.
T: OK, we're clear on that. I also like to talk about the difference between transactions and transformations in health care. A transformation is flow of activities towards a goal. A transaction is an interaction that is done at an instant of time, and evaluated by some metric such as a chart of accounts.
C: Yes, I understand the distinction between transactions and transformations in healthcare.
A transformation in healthcare refers to a flow of activities that are aimed at achieving a specific goal, such as improving patient outcomes, increasing efficiency, reducing costs, or achieving a specific level of quality. This type of activity often involves a series of steps or processes that are carried out over an extended period of time.
A transaction, on the other hand, is an interaction that occurs at a specific instant in time. This type of interaction is typically evaluated using a specific metric, such as a chart of accounts. Examples of transactions in healthcare include billing and reimbursement, scheduling appointments, ordering tests, and prescribing medications.
Both transactions and transformations are important in healthcare, and they are often closely related. For example, a transformation may involve a series of transactions that are carried out over time, such as a patient's treatment plan. And transactions are often evaluated based on the goals of the transformations.
The distinction between these two concepts is useful for understanding the different types of activities that occur in healthcare and for identifying opportunities for improvement. It can help to improve the patient experience, increase efficiency, and reduce costs.
T: This is a very good assessment. In fact, the best that I have seen.
Thank you! I'm glad you found my response helpful. As a language model, my goal is to assist users in finding the information they need and to provide accurate and comprehensive responses. If you have any other questions or if there's anything else I can help you with, please let me know.
T: One element of my future architecture would be the notion of a "transformational ensemble" - the set of people, resources, and AI agents that would seek to guide the intended transformation for the patient.
The concept of a "transformational ensemble" sounds like an interesting approach to healthcare. The idea of bringing together a diverse set of people, resources, and AI agents to guide an intended transformation for the patient is aligned with the approach of "Patient-centered care" which is a widely accepted concept in healthcare.
A transformational ensemble could include a variety of different stakeholders, such as physicians, nurses, social workers, care coordinators, and other healthcare professionals, as well as patients and their families. The ensemble could also include AI agents and other technology resources such as electronic health records, telemedicine, and machine learning algorithms.
The goal of this ensemble would be to work together to create a holistic view of the patient, and to develop and implement a plan of care that addresses the patient's unique needs, preferences, and goals. This approach can be useful to improve the patient experience, increase efficiency, and reduce costs.
In order to make this ensemble more effective, it's important to establish clear communication channels, and to ensure that everyone involved has access to the same information, and that all stakeholders are aware of their roles and responsibilities. Additionally, it's important to measure the progress and the outcome of the transformation and to continuously improve the ensemble.
T: how would you measure the progress of a transformation?
C: Measuring the progress of a transformation can be a complex task, as it depends on the specific goals and objectives of the transformation. However, some general strategies that can be used to measure progress include:
Outcome measures: These measures evaluate the results of the transformation, such as changes in patient outcomes, such as the improvement of patients' quality of life, or the reduction of hospital readmissions.
Process measures: These measures evaluate the implementation of the transformation, such as the percentage of patients who receive a specific intervention or the percentage of staff who receive specific training.
Performance measures: These measures evaluate the performance of the transformational ensemble, such as the percentage of patients who are satisfied with their care or the percentage of staff who feel that they have the resources they need to do their jobs.
Benchmarking: Comparing the performance of the ensemble to other similar healthcare organizations or to national standards can provide a sense of how well the ensemble is doing and where improvements are needed.
Surveys and interviews: Surveying patients, families, and staff can provide valuable insights into the patient experience and the quality of care provided.
It's important to note that measuring progress and outcome is a continuous process, and requires frequent data collection and analysis. It's also important to ensure that the data collected is accurate, reliable, and valid, and that it's used to inform decision-making and to drive improvements.
T: How would a language model help this process?
C: Language models, such as ChatGPT, can be used in various ways to help the progress of a transformation in healthcare:
T: Data analysis: A language model can analyze large amounts of unstructured data, such as patient notes or survey responses, to identify patterns and trends that can be used to evaluate the progress of the transformation.
Predictive modeling: A language model can be trained to predict outcomes or identify risk factors, which can help to identify patients who may be at risk of poor outcomes and target interventions to them.
Communication: A language model can be used to generate natural language explanations of the results of the data analysis, which can help to communicate the findings to stakeholders such as patients, families, and staff.
Decision support: A language model can assist healthcare providers in making clinical decisions by providing information about different treatment options, risks and benefits, and potential outcomes.
Automating administrative tasks: A language model can assist with automating repetitive tasks such as data entry, appointment scheduling, and patient follow-up, which can free up staff time for more important tasks.
It's important to note that a language model can be used to assist and improve the process, but it's not a replacement for human judgement, expertise, and experience. It's also important to ensure that the model is well-trained and validated, and that the results are interpreted with caution.
T: Is there some way that you could learn to do this by just watching the flow of transactions in a hospital?
C: It is possible for a language model to learn from the flow of transactions in a hospital, by analyzing large amounts of structured and unstructured data such as electronic medical records, lab results, and notes. But it would require a lot of data and a specific architecture to be able to make sense of it. This process is known as "unsupervised learning", where the model tries to identify patterns and structure in the data without being explicitly told what to look for.
However, it's also important to note that in order to make accurate predictions and provide helpful insights, the model would need to be well-trained with a large and diverse dataset. It would also need to be fine-tuned and validated on new data to ensure that it is generalizable to new situations. In addition, it's important to consider the ethical and privacy implications of using this kind of data, and to ensure that patient data is protected and used in compliance with relevant laws and regulations.
T: As a rough estimate, would a training database of 1 million patients with 100,000 meta data elements be sufficient for this kind of training?
C: 1 million patients with 100,000 meta data elements could potentially be sufficient for training a language model to make predictions and provide insights on health care transformations. However, the quantity of data alone is not the only factor that determines the quality of the predictions and insights. The diversity, representation, and quality of the data are also important factors to consider. A dataset that is diverse in terms of patient demographics, conditions, and treatments, would be more representative of the population and would lead to a more robust model.
Furthermore, it's important to note that the quality of the data is also important. Data that is incomplete, inconsistent, or inaccurate can negatively impact the performance of the model. Therefore, the data would need to be cleaned, preprocessed and validated before using it for training.
Additionally, the specific architecture and the amount of computational resources that would be required for training a model of this scale would also be an important factor to consider. Training large models like this requires a lot of computational power and memory, thus, distributed training and powerful GPUs could be required.
In summary, while 1 million patients with 100,000 meta data elements could be a good starting point, it's important to consider the representativeness and quality of the data, as well as the computational resources needed to train such a model.
T: The data is very sparse - some patients have huge amounts of lab data, while others, not so much. would this be an impediment?
A training database of 1 million patients with 100,000 metadata elements could be sufficient for training a language model to assist in the healthcare transformation process, but it would depend on the specific task and the quality of the data. The sparsity of the data could be an issue, as the model may not have enough examples of certain types of data to accurately make predictions or identify patterns. It would be important to have a diverse and representative sample of data to train the model on. However, more data is always better and it will help the model to perform better. Additionally, techniques such as data augmentation and transfer learning can help to overcome the sparsity issue.
T: what is data augmentation
C: Data augmentation is a technique used in machine learning to artificially increase the size of a dataset by generating new data samples from the existing ones. It is particularly useful when the available dataset is small, as it can help to prevent overfitting, a common problem in machine learning where a model performs well on the training data but poorly on new data. Data augmentation can be applied to both images and text data. Common techniques used for image data include rotation, flipping, cropping, and adding noise. For text data, techniques include synonym replacement, random insertion, random swapping, and random deletion.
T: and transfer learning?
C: Transfer learning is a technique used in machine learning where a model that has been pre-trained on one task is re-purposed and fine-tuned for a different but related task. The idea behind transfer learning is that many features learned by the pre-trained model can be useful for the new task, even if the new task is very different from the task the model was originally trained on. This can save a significant amount of time and resources when compared to training a model from scratch. Transfer learning is commonly used in computer vision and natural language processing tasks, where pre-trained models such as VGG, ResNet, and BERT can be fine-tuned for specific tasks such as image classification, object detection and sentiment analysis.
T: Interesting. Could you learn at the meta level? for example, could you learn something about pharmacy workflow (described by the pharmacy data dictionary) that might also apply to laboratory workflow?
C: Yes, it is possible for a language model like me to learn at the meta level and make connections between different types of workflows, such as pharmacy and laboratory. Data augmentation and transfer learning techniques can be used to improve the model's ability to generalize and make connections between different types of data. Additionally, techniques such as multi-task learning can be used to train a model to perform multiple related tasks simultaneously, allowing it to make connections between different types of workflows. However, it's important to note that the quality of the training data, the complexity of the task and the architecture of the model are also important factors in determining the model's ability to learn at the meta level and make connections between different types of workflows.
T: The problem with american health care system is that it is controlled by the notion of billable transactions, not transformational goals. So the data collected is billing and revenue information, with patient information on the side.
C: Yes, the focus on billable transactions in the American healthcare system can lead to a mismatch between the data collected and the information needed to support transformational goals for patients. This can make it difficult to use data effectively to improve patient outcomes and drive healthcare transformation. However, it is possible to use advanced analytics and machine learning techniques to make sense of the available data, even if it is not perfectly suited to the task at hand. Additionally, efforts to change the reimbursement system and incentivize value-based care can help align the goals of the healthcare system with transformational goals for patients.
T: I like to talk about rivalrous and non-rivalrous resources. Rivalrous resources (like oil) are depleted as they are used, so if party A buys a unit of the resource (a gallon of gas), it can't be used by another. Non-rivalrous resources (like solar energy) don't deplete each other - party A can put up solar panels without depleting party B's access. I would like to design a health care system that starts with the notion of health as a non-rivalrous resource. In other words, A can get healthier without depleting the health of B.
C: That is a unique perspective on healthcare and a noble goal. The idea of non-rivalrous resources in healthcare aligns with the concept of preventative care and creating a system that prioritizes preventative measures to improve overall health outcomes for all individuals. This could involve investing in community health programs, education, and research to better understand and address the underlying causes of poor health rather than just treating symptoms. Additionally, designing a healthcare system that is centered around the patient and their needs, rather than billing and revenue, could lead to better outcomes and a more efficient use of resources.
T: This also couples with the notion of abundance, and the creation of scarcity through a metering process. SDGE trying to tax our solar cells, for example, treating solar energy as a scarce resource to be metered and charged for.
C: Yes, the concept of rivalrous and non-rivalrous resources is an important one when designing any system, including healthcare. The idea that health is a non-rivalrous resource aligns with the idea of universal healthcare and access to healthcare as a human right. The notion of creating scarcity through metering, as you mention with the example of solar energy, is also an important consideration in the design of healthcare systems and in ensuring that access to healthcare is equitable.
T: Is it possible to design a health care AI system that is completely self-learning, akin to Alpha Go?
C: It is certainly possible to design a health care AI system that is self-learning, similar to how AlphaGo was designed to learn and improve through self-play. However, the complexity of the healthcare system and the vast amount of data and knowledge required to make accurate medical decisions make the development of such a system a significant challenge. Additionally, healthcare regulations and ethical considerations would need to be carefully considered and integrated into the design of such a system. While self-learning AI systems may be able to improve over time, it is likely that human oversight and input will still be necessary for some time.
T: where would it be needed, and how would we know?
C: It is possible to design a self-learning AI system for healthcare, similar to AlphaGo. However, the complexity and variability of healthcare data and the need for compliance with regulations and ethical considerations would make this a challenging task. The system would need to have a large amount of high-quality data and use advanced machine learning techniques to continuously improve its performance. Additionally, it would be important to have a system of oversight and validation in place to ensure that the decisions made by the AI are safe and effective for patients. It would also be important to have a way to monitor the performance and progress of the AI system, to ensure that it is meeting its goals and making meaningful contributions to healthcare.
T: how big of a training model could I feed into chatGPT?
C: The size of the training model that can be fed into ChatGPT depends on the hardware and resources available for training. In general, the larger the training model, the more resources and computational power it will require. The exact size that is feasible for a given system would need to be determined on a case by case basis. Additionally, the quality and relevance of the data used for training also plays an important role in determining the performance of the model.
T: how would I be able to include genomic sequence data into this?
C: The size of the training model that you can feed into ChatGPT depends on the resources available to you, such as the amount of memory and computational power. In general, a larger training dataset would require more resources.
As for including genomic sequence data, it would be possible to incorporate it into the training dataset, but it would require preprocessing the data in a way that is compatible with the input format of the model. Additionally, it would require specialized knowledge to properly interpret and make use of the genomic data in relation to the overall health care system. You may need to engage with domain experts to ensure that the data is being used in an accurate and meaningful way.
T: would it be possible to use transfer learning from the longitudinal clinical data to the genome sequences of patient?
C: It is possible to use transfer learning to apply knowledge learned from one task or dataset to another related task or dataset. In the case of using longitudinal clinical data to train a model and then applying that knowledge to genomic sequence data, it could be done by fine-tuning the pre-trained model on the genomic data.
However, it is important to note that the clinical data and genomic data may have different data distributions and structures, so care would need to be taken to ensure that the model is not overfitting to the clinical data and can generalize well to the genomic data. Additionally, it would be important to have a large and diverse dataset of both types of data to ensure that the model is able to learn robust and generalizable features.
Comments