TECHNICAL FIELD

Various embodiments described herein are directed generally to health care. More particularly, but not exclusively, various methods and apparatus disclosed herein relate to generating and applying subject event timelines for various purposes.

BACKGROUND

As populations of patients (or more generally, “subjects”) grow it becomes increasingly important to be able to generate succinct and uniform information about the subjects for a variety of purposes, such as reliably comparing subjects, identifying similar subjects (i.e. a “subject cohort”), detecting patterns amongst subjects, identify subject risks, and so forth. This may be particularly advantageous in scenarios such as clinical trials, insurance investigations/research, and/or in building analytics tools for whole institutions or clinical networks. Moreover, to assess a patient problem or make an intervention decision, clinicians often must combine and correlate large amounts of information, such as the patient's status, symptoms and treatments, stored in the patient health record over time. The adoption of healthcare informatics systems such as Electronic Medical Record (“EMR”), Radiology Information System (“RIS”) and Laboratory Information System (“LIS”) allowed medical personal to have access to an enormous amount of patient data. Unfortunately, these data are spread across different healthcare silos and under different management, leading to data heterogeneity. Consequently, it is currently difficult to compare subjects, identify similar subjects, and/or detect patterns amongst subjects without performing significant manual data manipulation. This leads to substantial costs, increased time to market for clinical drugs, and/or longer cycle times for analytics results. In addition, considerable human and computing resources may be required for this manual analysis.

SUMMARY

The present disclosure is directed to methods and apparatus for generating and applying subject event timelines for various purposes. For example, in various embodiments, a plurality of “medical events” and/or “medical concepts” associated with a subject may be extracted from a plurality of disparate data sources. The medical events may include, for instance, diagnoses, lab results, observed and/or reported symptoms, applied treatments, drug prescriptions, subject activities, insurance claims, etc.

The disparate data sources from which the medical events are extracted may include, for instance, structured data sources that include structured, non-narrative data records that convey information indicative of medical events. For example, hospital information systems (“HIS”), electronic medical records (“EMR”), laboratory information systems (“LIS”), and other data sources may include lab results, vital sign measurements, and other quantitative data points, and/or medical codes that are “structured,” i.e., they are quantitative in nature and easily extracted. The disparate data sources may also include narrative data sources that include, for instance, narrative data records that describe medical events using free-formed prose. For example, clinical notes composed by a clinician may not necessarily be structured, and different clinicians may document the same medical event using different prose, even though both are trying to convey the same semantic meaning. In various embodiments, techniques such as natural language processing, topic classification, etc., may be employed to extract concepts associated with medical events from narrative data records.

In various embodiments, timestamps associated with the extracted medical events may also be determined. In most cases the timestamps may be obtained from the underlying data source, e.g., in the form of metadata, database record field, etc. Once the medical events and associated timestamps for a given subject are determined, they may be used to generate/assemble what will be referred to herein as a “timeline data structure” that represents a temporally ordered sequence of medical events associated with the given subject. The timeline data structure may be generated in various formats, such as a linked list, an array of medical events and associated timestamps, a sequence of database records, etc. Once the timeline data structures are assembled, they may be stored in one or more databases so that they can be used for various purposes.

In some embodiments, each timeline data structure may be used to render, e.g., as part of a graphical user interface (“GUI”), a visual timeline that includes graphical elements representing medical events associated with the respective subject. Due to the temporal ordering and/or timestamps, the graphical elements are rendered in a temporal order in which they occurred, so that a user such as a clinician, insurance investigator, researcher, etc., is able to view the subject's clinical trajectory over time in an intuitive manner. Additionally, in some embodiments, each graphical element may be operable to cause additional information about the respective medical event to be output. Suppose a particular graphical element represents diagnosis of left ventricular hypertrophy (“LVH”). When the user clicks on the particular graphical element, a new interface (e.g., a pop-up window) may be rendered that displays at least part of the underlying record that lead to this diagnosis. For example, all or a portion of an electrocardiogram (“ECG”) may be rendered so that the user can take a closer look.

Additionally or alternatively, in some embodiments, the graphical elements of a visual timeline may be operable for other purposes. For example, in some embodiments, one or more graphical elements of a visual timeline may be operable (e.g., selectable) to formulate a search query that is usable to find similar subjects. For example, a user may select two or more medical events of a visual timeline, e.g., by clicking on them. Because the elements are already ordered temporally, the user has, by definition, also selected an inherent temporal relationship between the two or more selected medical events. In some embodiments, the user may be able to specify one or constraints on the search, such a demographic constraints, temporal constraints, and so forth. For example, a user may specify, for two selected events, a maximum or minimum time interval that must occur between them in order to satisfy the search query.

Next, one or more responsive timeline data structures associated with one or more other subjects may be retrieved from a database based on the search query and any associated constraints. In some embodiments, additional visual timelines may be rendered on the display that are based on the retrieved one or more responsive timeline structures. In this manner, a user is able to view similar subjects, e.g., a patient cohort, that is defined based on the user's query. In various implementations, these other visual timelines may also be operable, e.g., to see the underlying data records associated with medical events of the other subjects.

In another aspect, timeline data structures associated with subjects may be analyzed to identify inconsistencies and/or conflicts between medical events/concepts. For example, a subject's diagnosis of type II diabetes might raise a conflict if found within a short time of another diagnosis for hypoglycemia. A variety of different approaches may be applied, alone and/or in combination, to identify inconsistencies/conflicts, including but not limited to rules-based approaches (e.g., heuristics), machine learning models, etc. Identified inconsistencies/conflicts may be displayed to a variety of different types of users (e.g., clinicians, insurance adjusters, researchers, patients, etc.) for a variety of different purposes. These purposes include but are not limited to clinical decision support (“CDS”), analysis of care provided across a health care entity/system, for insurance claim investigation, etc.

Generally, in one aspect, a method may include: retrieving, from a plurality of data sources, data indicative of a plurality of medical events associated with a subject; associating each of the plurality of medical events with a respective timestamp; assembling, based on the plurality of medical events and associated timestamps, a timeline data structure associated with the subject; analyzing the timeline data structure to identify one or more inconsistencies between two or more medical events of the plurality of medical events associated with the subject; and causing a visual timeline indicative of the plurality of medical events to be rendered on a display based on the timeline data structure, wherein each respective medical event of the plurality of medical events is represented in the visual timeline by a graphical element, and wherein two or more of the graphical elements that are associated with the two or more medical events for which the one or more inconsistencies were identified are visually distinguished from other graphical elements.

In various embodiments, the analyzing may include semantically analyzing the plurality of medical events, and the one or more inconsistencies include one or more semantic inconsistencies. In various embodiments, the semantic analyzing may include searching for identity relationships between two or more of the plurality of medical events. In various embodiments, the analyzing may include applying attributes of at least two of the plurality of medical events as input across a trained machine learning model to generate output, wherein the output is indicative of the one or more inconsistencies.

In various embodiments, the method may further include classifying each of the plurality of medical events into one of a predetermined number of temporal categories. In various embodiments, the analyzing includes refraining from comparing two medical events of the plurality of medical events that are classified into incompatible temporal categories. In various embodiments, the analyzing may include navigating a phenotype decision tree based on attributes of at least two of the plurality of medical events.

In addition, some implementations include one or more processors of one or more computing devices, where the one or more processors are operably coupled with memory and are configured to execute instructions stored the memory, and where the instructions are configured to cause performance of any of the aforementioned methods. Some implementations also include one or more non-transitory computer readable storage media storing computer instructions executable by one or more processors to perform any of the aforementioned methods.

It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. It should also be appreciated that terminology explicitly employed herein that also may appear in any disclosure incorporated by reference should be accorded a meaning most consistent with the particular concepts disclosed herein.

