Patent Publication Number: US-2022238215-A1

Title: Workflow Predictive Analytics Engine

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This patent arises as a continuation of U.S. patent application Ser. No. 16/456,656, which was filed on Jun. 28, 2019, which claims priority to U.S. Provisional Patent Application Ser. No. 62/770,548, which was filed on Nov. 21, 2018. U.S. patent application Ser. No. 16/456,656 and U.S. Provisional Patent Application Ser. No. 62/770,548 are hereby incorporated herein by reference in their entireties. Priority to U.S. patent application Ser. No. 16/456,656 and U.S. Provisional Patent Application Ser. No. 62/770,548 is hereby claimed. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to improved medical systems and, more particularly, to improved workflow predictive analytics engine systems and associated methods. 
     BACKGROUND 
     The statements in this section merely provide background information related to the disclosure and may not constitute prior art. 
     Healthcare environments, such as hospitals or clinics, include information systems, such as hospital information systems (HIS), radiology information systems (RIS), clinical information systems (CIS), and cardiovascular information systems (CVIS), and storage systems, such as picture archiving and communication systems (PACS), library information systems (LIS), and electronic medical records (EMR). Information stored can include patient medication orders, medical histories, imaging data, test results, diagnosis information, management information, and/or scheduling information, for example. A wealth of information is available, but the information can be siloed in various separate systems requiring separate access, search, and retrieval. Correlations between healthcare data remain elusive due to technological limitations on the associated systems. 
     Further, when data is brought together for display, the amount of data can be overwhelming and confusing. Such data overload presents difficulties when trying to display, and competing priorities put a premium in available screen real estate. Existing solutions are deficient in addressing these and other related concerns. 
     BRIEF DESCRIPTION 
     Systems, methods, and apparatus to generate and utilize predictive workflow analytics and inferencing are disclosed and described. 
     Certain examples provide a predictive workflow analytics apparatus. The example apparatus includes a data store to receive healthcare workflow data including at least one of a schedule or a worklist including a patient and an activity in the at least one of the schedule or the worklist involving the patient. The example apparatus includes a data access layer to combine the healthcare workflow data with non-healthcare data to enrich the healthcare workflow data for analysis with respect to the patient. The example apparatus includes an inferencing engine to generate a prediction including a probability of patient no-show to the activity by processing the combined, enriched healthcare workflow data using a model and triggering a corrective action proportional to the probability of patient no-show. 
     Certain examples provide a computer-readable storage medium including instructions. The instructions, when executed by at least one processor, cause the at least one processor to at least: combine healthcare workflow data with non-healthcare data to enrich the healthcare workflow data for analysis with respect to a patient, the healthcare workflow data including at least one of a schedule or a worklist including the patient and an activity in the at least one of the schedule or the worklist involving the patient; generate a prediction including a probability of patient no-show to the activity by processing the combined, enriched healthcare workflow data using a model; output the prediction; and trigger a corrective action proportional to the probability of patient no-show. 
     Certain examples provide a method to apply predictive analytics to drive a patient care pathway. The example method includes combining, by executing an instruction using at least one processor, healthcare workflow data with non-healthcare data to enrich the healthcare workflow data for analysis with respect to a patient, the healthcare workflow data including at least one of a schedule or a worklist including the patient and an activity in the at least one of the schedule or the worklist involving the patient. The example method includes generating, by executing an instruction using the at least one processor, a prediction including a probability of patient no-show to the activity by processing the combined, enriched healthcare workflow data using a model. The example method includes outputting, by executing an instruction using the at least one processor, the prediction. The example method includes triggering, by executing an instruction using the at least one processor, a corrective action proportional to the probability of patient no-show. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  illustrates an example predictive analytics inferencing architecture. 
         FIG. 2  illustrates a more detailed view of an implementation of the example architecture of  FIG. 1 . 
         FIG. 3  depicts an example implementation of the inferencing engine of  FIGS. 1-2 . 
         FIGS. 4-5  show example flow charts integrating artificial intelligence-driven prediction and modeling into an example patient care pathway. 
         FIGS. 6-9  depict example interfaces generated by the example systems and methods of  FIGS. 1-4 . 
         FIG. 10  is a block diagram of an example processor platform capable of executing instructions to implement the example systems and methods disclosed and described herein. 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific examples that may be practiced. These examples are described in sufficient detail to enable one skilled in the art to practice the subject matter, and it is to be understood that other examples may be utilized and that logical, mechanical, electrical and other changes may be made without departing from the scope of the subject matter of this disclosure. The following detailed description is, therefore, provided to describe an exemplary implementation and not to be taken as limiting on the scope of the subject matter described in this disclosure. Certain features from different aspects of the following description may be combined to form yet new aspects of the subject matter discussed below. 
     When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements. The terms “first,” “second,” and the like, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. As the terms “connected to,” “coupled to,” etc. are used herein, one object (e.g., a material, element, structure, member, etc.) can be connected to or coupled to another object regardless of whether the one object is directly connected or coupled to the other object or whether there are one or more intervening objects between the one object and the other object. 
     As used herein, the terms “system,” “unit,” “module,” “engine,” etc., may include a hardware and/or software system that operates to perform one or more functions. For example, a module, unit, or system may include a computer processor, controller, and/or other logic-based device that performs operations based on instructions stored on a tangible and non-transitory computer readable storage medium, such as a computer memory. Alternatively, a module, unit, engine, or system may include a hard-wired device that performs operations based on hard-wired logic of the device. Various modules, units, engines, and/or systems shown in the attached figures may represent the hardware that operates based on software or hardwired instructions, the software that directs hardware to perform the operations, or a combination thereof. 
     In addition, it should be understood that references to “one embodiment” or “an embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. 
     Aspects disclosed and described herein provide systems and associated methods to provide predictive analytics including customer-driven workflow predictions and corresponding responsive actions. For example, predictive analytics disclosed and described herein can be used to avoid breaches in service level agreement (SLA) with respect to reporting, etc., by providing a real-time reporting worklist at a point of decision (e.g., in a radiology information system (RIS), etc.) along with a probability of breach. In another example, patient no-shows can be prevented by identifying examinations having a high probability of no-show and triggering a corrective action such as a reminder, an overbooking analysis, a ride sharing assist, etc., as disclosed and described herein. In another example, patient waiting time can be predicted to improve patient experience and revenue opportunity by computing and announcing an estimated waiting time as disclosed and described herein. In another example, workload and capacity can be managed by planning strategically on a machine and reporting resources needed for each service as disclosed and described herein. 
     For example, patient no-shows for radiology appointments can be predicted using historical patterns and artificial intelligence processing of patient information (e.g., age, gender, history, etc.), appointment age (e.g., how long since the appointment was made, etc.), date/time of appointment, weather forecast, other historical pattern data, etc. Certain examples leverage artificial intelligence (AI) such as a random forest, artificial neural network (such as a convolutional neural network (CNN), etc.), etc., to provide an integrated prediction and corrective action framework to address likely patient no-show. Patient no shows are costly (e.g., ˜$1 M loss in yearly opportunity for magnetic resonance imaging exams at a 4% patient no-show rate). A machine learning algorithm and associated model can factor in elements such as weather forecast, location, time, traffic, etc., to predict likely patient no-shows, and a reduced in no-shows increases responsiveness to patient health needs, increased productivity in a healthcare environment, increased revenue, etc., through algorithm-based reconfirmation/replacement strategies, for example. 
       FIG. 1  illustrates an example predictive analytics inferencing architecture  100 . The example apparatus  100  includes and/or interacts with one or more workflow information systems  110 , such as an electronic medical record (EMR) system, radiology information system (RIS), picture archiving and communication system (PACS), etc. The information system(s)  110  provide healthcare workflow data  115  to a data store  120 , such as an ElastiCube, other data cube, other data store, etc. The workflow data  115  can related to a schedule or workflow of activities involving patients, resources, personnel, etc., for a healthcare facility, for example. The workflow data  115  can be mined using extract, transform, and load (ETL) operations to provide the data  115  to the storage  120 , for example. The data storage  120  provides the data to a predictive analytics dashboard  130  as well as a data access layer  140 . The dashboard  130  can display prediction(s) from the data  115 , for example. The data access layer  140  receives data from the data store  120  (e.g., via a Representational State Transfer (REST) get request, etc.) and combines the data with additional information such as weather forecast information  150  (traffic information, non-healthcare event information, etc.). The data access layer  140  combines the healthcare data  115 , such as appointment data, patient data, hospital resource data, etc., with weather forecast information  150  (e.g., looking at a 5-day window around the time of the appointment, etc.) and/or other information such as location, traffic, etc., to form combined, enriched healthcare workflow data, and provides the combined, enriched information (e.g., via a REST post operation, etc.) to a machine learning inferencing engine  160 , which includes one or more AI models  165  to process the information and generate a prediction, for example. Results are provided (e.g., via a REST result operation, etc.) back to the data access layer  140  to be conveyed to the data store  120  as well as to the information system(s)  110  as one or more integrated workflow predictions  170 . 
     Thus, data can be aggregated and processed by one more machine learning algorithms implemented using models  165  (e.g., random forest, CNN, etc.) to provide predictive output  170  to the information system(s)  110 . The algorithm can change based on a goal of the analysis, degree of probability estimation, accuracy, priority, etc. The example dashboard  130  can provide both predictive and retrospective visualization(s) of information, such as prediction of one or more patient no-shows, etc. In certain examples, a confidence interval can be provided with the predictive estimate. For example, using the prediction of a patient no-show and an associated confidence interval or score (e.g., 90%, 50%, 30%, etc.), the system  110  can decide whether it wants to make an adjustment or change to that patient&#39;s appointment (e.g., a reminder, a confirmation, a replacement or substitution of that time slot and associated resource(s), etc.). The confidence interval can be a confidence in the prediction based on available information, and/or the confidence interval can be an indication of confidence that the patient will show up for his/her scheduled appointment, for example. 
     For example, the prediction can analyze the schedule three days in advance to identify patient(s) associated with a low confidence interval (e.g., &lt;50%), then follow-up with them to confirm whether or not they will be showing up. While rescheduling on the same day is difficult, the schedule can be adjusted up to one day in advance to accommodate a patient who will not or is not likely to attend his/her scheduled appointment. In certain examples, a more urgent patient can be scheduled in place of a patient with a low likelihood of attendance. If a patient is not likely to attend, degradable materials such as nuclear medicine isotopes can be saved, postponed, used instead for another patient, etc., rather than going to waste because the half-life does not allow storage for delayed use. 
     In certain examples, the output  170  can include a worklist with an indication of confidence/likelihood in attendance/no show, etc. In certain examples, the worklist is generated for follow-up, and patients on the list are prioritized or ranked based on their priority, their likelihood of no show, and available capacity when the list is too long to follow up with everyone. 
     In certain examples, the worklist can be processed by looking for patients with a same or similar procedure scheduled in the next month to see if a slot can be filled with someone else if the patient currently in that slot does not make his/her appointment. In certain examples, patient address can be compared to clinic location and combined with traffic information to priority patient(s) who can more easily make it to the hospital to fill a time slot. 
     In certain examples, the data store  120  transforms the data  115  before providing the data to the data access layer  140  and inferencing engine  160 . For example, the data store  120  can transform a format of the data  115 , can organize/arrange the data  115 , etc. Thus, data  115  can be transformed from the RIS to generate a priority list, for example. The model  165  provides output to the data store  120  to be used by the dashboard(s)  130  to present predictive results. The data store  120  can transform the output from the model(s)  165  of the inferencing engine  160  to form a predictive dashboard display  130 , for example. Thus, data modeled in the data store  120  (e.g., cleaned, standardized/normalized/transformed, and prepared for output, etc.) can be used to train model(s)  165  and generate prediction(s), for example. The data store  120  can be configured to select a particular subset of the data  115 , rather than all the data  115 , from the information system(s)  110  that matches certain constraint(s), criterion(-ia), etc., and the data store  120  can organize that data in a certain way for the dashboard(s)  130 , model(s)  165 , etc. 
     The model(s)  165  are trained using the prepared data from the data store  120  as further combined with other information such as weather  150 , traffic, etc., via the data access layer  140 . The data and constraints train, test, and transform the model(s)  165  into particular algorithms customized for the specific data set(s)  115  and observed patient pattern(s) for the particular healthcare environment&#39;s system(s)  110 . Thus, the model(s)  165  become a customized algorithm or set of algorithms that function for a particular environment, scenario, set of resources, patient population, etc. 
     In certain examples, the data access layer  140  can send results to the relevant system(s)  110 , such as a RIS, PACS, EMR, etc., and appointments can be directly flagged in the RIS scheduling system with a high probability of no-show. Action can be taken to confirm those appointments, cancel or reschedule those appointments, fill in empty time slots with other available patients, etc. 
       FIG. 2  illustrates a more detailed view of an implementation of the example architecture  100 . In the example of  FIG. 2 , the architecture  100  is implemented as a virtual machine or appliance running at a healthcare facility (e.g., a hospital, clinic, doctor&#39;s office, etc.). In the example implementation of  FIG. 2 , the data store  120  is divided into a MS data cube  120  and a result cube  125 , and the data access layer  140  includes a data access service  142  and a data access controller  144 . The data access layer  140  provides a result that is saved in a result file  210 , which is provided to the result cube  125 . The scheduled build of predictive results from the result file  210  can be used to drive the dashboard  130  interface display(s) and associated action(s). 
     As shown in the example of  FIG. 2 , an event at a workflow information system  110  triggers (e.g., based on an appointment or scheduling request, daily schedule generation, etc.) exchange of data and processing of event data, patient data, and other data (e.g., non-health data such as weather, traffic, resources, etc.) to generate an interactive dashboard display and schedule modification. The data cube  120  merges data from multiple sources and enables components of the system  100  to manipulate and query the data as if it was one consolidated data set. Using the cube  120 , data from one or more sources  110  at one or more locations can based “mashed” together to represent data in fields in which a value in one field has a corresponding value in another field to enable data in a field to be processed with respect to data in any other field. By allowing data to be analyzed in the context of other data from the same or disparate data source, the cube  120  enables powerful query and analysis of large amounts of data from disparate data source(s) to be processed by the data access layer  140  in real time (or substantially real time given a data retrieval, storage, and/or processing latency, etc.). 
     In certain examples, the data  115  can be provided to the cube  120  via extract, transform, and load (ETL) operation(s). Using ETL, data  115  can be copied from a source in one context to a destination in another context. Thus, the ETL operation(s) process data retrieved from one or more source(s)  110 , cleanse the data to remedy deficiency, inconsistency, etc., from an expected format and/or context, and transform the data into a format/context on which the data access layer  140  can act. In certain examples, ETL operation(s) on the data  115  form the data  115  into a comma separated value (CSV) file and/or other spreadsheet, data file, etc., for retrieval and processing by the data access layer  140 . 
     In certain examples, the data access layer  140  creates a layer of abstraction between the data cube  120  and the inference engine  160 . The abstraction of the data access layer  140  allows different logical models to be used with respect to data in the data cube  120  and processing via the inference engine  160  and its model(s)  165 , for example. In certain examples, the data access layer  140  can include business logic to tailor queries of data via the data cube  120  and provide an incoming query of the data cube  120  (e.g., data gathered from the cube  120  via a REST get query, etc.) and an outgoing result for the result cube  125 . As shown in the example of  FIG. 2 , the data access layer  140  includes a data access service  142  and a data access controller  144 . The data access controller  144  regulates the data access service  142  to get and combine data, process the data, trigger inferencing by the inferencing engine  160 , etc. The data access controller  144  can help ensure quality and quantity of data retrieved by the data access service  142  and can help ensure authentication and authorization to retrieve, combine, and process data, for example. For example, the data access controller  144  can control (e.g., via a hypertext transfer protocol (HTTP) request, etc.) the data access service  142  to gather patient and schedule data  115  as well as weather information  150  for a particular time/location to form an execution request for the inference engine  160 . 
     Thus, the data access layer  140  receives data from the data store  120  (e.g., via a REST get request, etc.) and combines the data with additional information such as weather forecast information  150  (traffic information, non-healthcare event information, etc.). The data access layer  140  combines the healthcare data  115 , such as appointment data, patient data, hospital resource data, etc., with weather forecast information  150  (e.g., looking at a 5-day window around the time of the appointment, etc.) and/or other information such as location, traffic, etc., and provides the combined information (e.g., via a REST post operation, etc.) to the machine learning inferencing engine  160 , which includes one or more AI models  165  to process the information and generate a prediction, for example. The inference engine  160  trains and deploys AI model(s)  165  such as machine learning models (e.g., neural networks, etc.), etc., to process incoming data and determine a likely outcome such as a no-show prediction, etc. The model(s)  165  can be trained, for example, on prior, verified data indicating that certain patient conditions, weather, time/location, etc., result in a patient no-show for an appointment, for example. Results are provided (e.g., via a REST result operation, etc.) back to the data access layer  140  to be conveyed as an output, such as a CVS file  210 , etc., to the result data cube  125  as well as to the information system(s)  110  as one or more integrated workflow predictions  170 , for example. 
       FIG. 3  depicts an example implementation of the inferencing engine  160  of  FIGS. 1-2 . As shown in the example of  FIG. 3 , the inferencing engine  160  can be implemented as a container or virtual machine including a plurality of elements or actions  302 - 314 . For example, the inferencing engine of  FIG. 3  includes an HTTP request receiver  302  to perform input validation, request processing, etc. The receiver  302  provides the processed data to a context creator  304 , which creates a patient/schedule context using the input data. For example, context such as reason for exam, patient condition, location, time, etc., can be associated with the data. The data with context is then provided to a preprocessing algorithm  306 , which prepares the data for processing by the AI model(s)  165  to generate a prediction (e.g., a no-show prediction, an SLA breach prediction, a wait time prediction, a workload prediction, etc.). The prediction is then output to an algorithm postprocessor  310  to take the model  165  result(s) and formulate the result(s) for use in display, records, schedule adjustment, communication, other output, etc. The post-processed result(s) are provided an output contextualizer  312  to provide context (e.g., patient context, schedule context, etc.) to the output. The contextualized output is then provided to a response generator  314  to create a response (e.g., an HTTP response, etc.) to be send to the data access controller service  144  of the data access layer  140 , for example. 
     Thus, the inferencing engine  160  is a framework component that provides connectivity and expansibility to accommodate one or more algorithm models  165 , pre- and/or post-processing, and scalability to scale up algorithm(s) to support a workflow across one or more hospital departments, teams, etc. The engine  160  can scale predictive analytics in the model(s)  165  for a number of sources, number of recipients, intended audience/environment, etc. In certain examples, a variety of models  165  can be plugged in to the engine  160  depending on target goal/objective, patient population, healthcare environment, etc., the model(s)  165  are incorporated into the engine  160  transparent to the user and/or healthcare system  110 . The engine  160  provides a framework to accept the algorithm model  165  and adapt that model  165  to a real world system  110 , for example. 
     For example, the model  165  is unable to connect to other parts of the system  110 , and the engine  160  connects the model  165  to the system  100 , allows it to be changed, enables it to be used, etc. The framework of the engine  160  anchors the model  165  and establishes connections with other parts of the system  100 . For example, data from which the prediction is made comes from the database/cubes  120 , forwarded via the data management service of the access layer  140 , and the inferencing engine  160  exposes an HTTP endpoint, for example, to receive the data and process the data to help ensure quality, format, etc. The pre-processed data is then forwarded to the model  165 . Code executed by the engine  160  before the model  165  and after the model  165  preprocesses data going into the model  165  and post-processes data coming out of the model  165  to be used by the system  100  after the model  165 . 
     In certain examples, the model  165  is generated as a random forest model. Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting a class that is a mode of included classes (classification) or a mean prediction (regression) of the individual trees, for example. Random decision forests correct for decision trees&#39; habit of overfitting to their training set, for example. That is, decision tree structures can be used in machine learning, but, when the tree grows deep, the tree can learn irregular patterns, resulting in low bias but high variance as the decision tree overfits its training data set. Random forests average multiple deep decision trees, trained on different parts of the same training set, to reduce variance. The reduction in variance can come at the expense of a small increase in the bias and some loss of interpretability, but, generally, greatly boosts performance in the final model. Random forests can be used to rank importance of variables in a regression or classification problem, such as a likelihood or probability of patient no-shows, in a natural way. 
     In certain examples, random forest predictors can lead to a dissimilarity measure among observations. A random forest dissimilarity measure can also be defined between unlabeled data. A random forest dissimilarity can be used to process mixed variable types because it is invariant to monotonic transformations of the input variables and is robust to outlying observations. The random forest dissimilarity accommodates a large number of semi-continuous variables due to its intrinsic variable selection. For example, a random forest dissimilarity can be used to weigh a contribution of each available variable according to how dependent the variable is on other variables. The random forest dissimilarity can be used to identify a set of patient(s) among a group of scheduled patients who are likely to not show for their scheduled appointment based on past history, weather, traffic, etc. 
     Machine learning techniques, whether random forests, deep learning networks, and/or other experiential/observational learning system, can be used to locate an object in an image, understand speech and convert speech into text, establish correlations and/or prediction of an event such as a patient no-show, improve the relevance of search engine results, etc., for example. Deep learning is a subset of machine learning that uses a set of algorithms to model high-level abstractions in data using a deep graph with multiple processing layers including linear and non-linear transformations. While many machine learning systems are seeded with initial features and/or network weights to be modified through learning and updating of the machine learning network, a deep learning network trains itself to identify “good” features for analysis. Using a multilayered architecture, machines employing deep learning techniques can process raw data better than machines using conventional machine learning techniques. Examining data for groups of highly correlated values or distinctive themes is facilitated using different layers of evaluation or abstraction. 
     Deep learning in a neural network environment includes numerous interconnected nodes referred to as neurons. Input neurons, activated from an outside source, activate other neurons based on connections to those other neurons which are governed by the machine parameters. A neural network behaves in a certain manner based on its own parameters. Learning refines the machine parameters, and, by extension, the connections between neurons in the network, such that the neural network behaves in a desired manner. 
     Deep learning that utilizes a convolutional neural network segments data using convolutional filters to locate and identify learned, observable features in the data. Each filter or layer of the CNN architecture transforms the input data to increase the selectivity and invariance of the data. This abstraction of the data allows the machine to focus on the features in the data it is attempting to classify and ignore irrelevant background information. 
     Deep learning operates on the understanding that many datasets include high level features which include low level features. While examining an image, for example, rather than looking for an object, it is more efficient to look for edges which form motifs which form parts, which form the object being sought. These hierarchies of features can be found in many different forms of data such as speech and text, etc. 
     Learned observable features include objects and quantifiable regularities learned by the machine during supervised learning. A machine provided with a large set of well classified data is better equipped to distinguish and extract the features pertinent to successful classification of new data. 
     A deep learning machine that utilizes transfer learning may properly connect data features to certain classifications affirmed by a human expert. Conversely, the same machine can, when informed of an incorrect classification by a human expert, update the parameters for classification. Settings and/or other configuration information, for example, can be guided by learned use of settings and/or other configuration information, and, as a system is used more (e.g., repeatedly and/or by multiple users), a number of variations and/or other possibilities for settings and/or other configuration information can be reduced for a given situation. 
     An example deep learning neural network can be trained on a set of expert classified data, for example. This set of data builds the first parameters for the neural network, and this would be the stage of supervised learning. During the stage of supervised learning, the neural network can be tested whether the desired behavior has been achieved. 
     Once a desired neural network behavior has been achieved (e.g., a machine has been trained to operate according to a specified threshold, etc.), the machine can be deployed for use (e.g., testing the machine with “real” data, etc.). During operation, neural network classifications can be confirmed or denied (e.g., by an expert user, expert system, reference database, etc.) to continue to improve neural network behavior. The example neural network is then in a state of transfer learning, as parameters for classification that determine neural network behavior are updated based on ongoing interactions. In certain examples, the neural network can provide direct feedback to another process. In certain examples, the neural network outputs data that is buffered (e.g., via the cloud, etc.) and validated before it is provided to another process. 
       FIG. 4  shows an example flow chart integrating AI-driven prediction and modeling into an example patient care pathway  400 . At block  410 , a request is generated. Machine learning-based prescription support can be provided with an exam request, scheduling request, prescription request, etc., as appropriate to given context (e.g., user context, application context, healthcare context, etc.). At block  420 , scheduling is performed. For example, a predictive no-show algorithm model  165  can be applied to provide smart scheduling and reduce missed appointments, underutilized resources, delayed patient care, etc. Additionally, a predictive wait time algorithm can provide an improved patient experience, and imaging insights can facilitate improved asset utilization and case load management, for example. At block  430 , data acquisition is conducted. For example, acquisition can leverage the predictive wait time algorithm and imaging insights, imaging optimization (e.g., MR optimization, etc.), smart hanging protocol configuration, AI-based computer-aided diagnosis (CAD), etc. At block  440 , reading and reporting can be provided using the smart hanging protocol, AI-based CAD output, and predictive reporting regarding potential SLA breach, auto-population of findings, etc., for improved performance. 
       FIG. 5  provides an example illustration of the scheduling (e.g., block  420 ) of patient care including prediction of and reaction to patient no-shows. At block  510 , a patient care workflow is identified (e.g., provided in an exam request/reason for exam, extracted from a patient record, identified in a departmental schedule, etc.). For example, a schedule of one or more patients to be seen by one or more healthcare practitioners at a hospital can be retrieved from a hospital information system  110 . At block  520 , data related to the identified patient and/or patient care workflow is mined for predictive analytics. For example, data  115  for the patient(s) on the schedule, healthcare practitioner(s) involved in the schedule, resource(s) involved in the schedule, etc., can be extracted from one or more systems  110  such as a HIS, RIS, CIS, CVIS, PACS, LIS, EMR, etc., and mined for predictive analysis. The data cube  120  can format the data  115 , combine the data  115 , and/or otherwise transform the data  115  to be processed by the inferencing engine  160 , for example. 
     At block  530 , mined data is combined with non-healthcare data such as appointment data, weather data, traffic data, resource information, etc. For example, the mined healthcare data  115  is enriched with weather data  150 , traffic information relative to patient, provider, and/or other healthcare location, etc. Thus, the healthcare data can be enriched with non-healthcare data providing context, environment, conflicting schedule constraints, etc., that can affect predictive analysis of a future event such as a patient no-show for a scheduled appointment, etc. 
     At block  540 , the combined information is provided to the machine learning inference engine  160  to generate a prediction regarding an outcome associated with the information (e.g., a likelihood or probability of a patient not showing up for an appointment, etc.). For example, a random forest model  165  can be used to represent schedule data, workflow data, patient information, weather and/or traffic projection(s), etc., using a plurality of decision trees. The decision trees can be constructed at training time using known or “ground truth” verified data. Upon deployment in the inference engine  160 , the model(s)  165  can output a mode regression and/or mean classification of the decision trees representing a probability of patient no-show, for example. 
     At block  550 , a confidence score and/or interval associated with the prediction is computed. For example, the model  165  may output a yes or no answer and/or a percentage probability that a patient under review will not attend his/her appointment (a no show). The confidence interval associated with the model determination can be formed, for example, by determining a mean probability of patient no show and a standard deviation from the mean over a plurality of determinations (e.g., taking a square root of squared differences in range of available determinations, etc.) and calculating a margin of error using the mean, standard deviation, a desire confidence level (e.g., 90%, 95%, 99%, etc.). The margin of error can be subtracted from the mean and added to the mean to determine your confidence interval around the calculated value from the model  165 , for example. 
     At block  560 , an output is generated. For example, a worklist with the prediction, confidence score, and a recommendation/adjustment to the schedule and/or other workflow element are generated and provided based on the combination of prediction and confidence score and/or interval. A result generated by the inference engine  160  and provided to the data access layer  140  via a REST command can be used to drive dashboard  130  output as well as provide output to a scheduler associated with one or more information systems  110  to adjust equipment, personnel, patient, and/or other resource allocation based on integrated workflow prediction(s)  170 , for example. Output can be used to help ensure compliance with service level agreement(s) (SLA), reduce and/or maintain patient wait time, and trigger a reminder and/or other preventative and/or remedial action for one or more patients when the inference engine  160  indicates a high likelihood of patient no-show. Such action, triggered by the engine output  130 ,  170 , etc., can improve resource utilization, patient care, and system responsiveness, for example. 
       FIGS. 6-9  depict example interfaces generated by the example systems and methods of  FIGS. 1-5 . For example,  FIG. 6  shows an example predictive no-show interface  600 . The example interface  600  illustrates predictive patient no-show for radiology examinations based on machine learning from the inference engine  160 , for example. For each scheduled patient, a tile  610 - 612  representing the patient and their appointment is shown in conjunction with weather forecast information  620 - 622  for their appointment. Alternatively or in addition to weather forecast information, traffic information, etc., can be provided via the example interface  600  in conjunction with the patient  610 - 612  and prediction  630 - 632  information. A probability of no-show  630 - 632  is displayed on the interface  600 , and a rescheduling option  640  is presented when the patient has a high probability of missing the scheduled appointment (e.g., &gt;50%, etc.). 
       FIG. 7  shows an example dashboard  700  listing a set of patients and their information, scheduled appointment information, predicted probability of no-show, etc. A user can interact with the example dashboard  700  to evaluate a schedule or workflow and patients included in that schedule/workflow/worklist. In certain examples, a user can select a patient&#39;s no-show probability to view additional information that lead to the generation of the corresponding probability. Selecting a patient and/or other person listed in the dashboard  700  can retrieve contact information for the person and/or another person to contact them in the event of a no-show, in advance of a probable no-show, etc., to prompt the person to attend, to fill the spot with another person, etc. Via the example interface  700 , a daily schedule, weekly schedule, monthly schedule, etc., can be viewed and modified, for example. In certain examples, a schedule or worklist can be viewed by modality and/or other criterion via the example dashboard interface  700 . The example interface  700  can provide predicted no-shows for a given modality for a next day&#39;s appointments for a user, department, etc., for example. 
       FIG. 8  shows an example graph  800  of predicted no-show probabilities for each hour of a day. Thus, for a particular modality (e.g., x-ray, ultrasound, MR, etc.), a probability of patient no-show can vary by hour throughout the day. The example graph  800  conveys the probabilities to a user, for example. Using the example graph  800 , a user can view attendance prediction(s) and plan for demand, strategize to increase demand, etc. In certain examples, the graph  800  is connected to the dashboard  700  to allow a user to view a wait list and/or contact information to try and fill empty schedule slots, help ensure a person shows for an appointment, etc. 
       FIG. 9  shows an example random forest output  900  processing attendance data collected over a period of time (e.g., one year, two years, three years, etc.). In the example of FIG.  9 , a confusion matrix can be generated for monitored patient shows and no-shows such as used in training of the machine learning model for no-show prediction. In the example of  FIG. 9 , a random forest trained and deployed at a healthcare facility is analyzed to identify a number of correctly identified shows, a number of correctly identified no-shows, a number of missed shows, and a number of false positive no-shows to generate predictions at a 90.8% precision with an 82.6% recall from false negatives. 
     Flowcharts representative of example machine readable instructions for implementing and/or executing in conjunction with the example systems, algorithms, and interfaces of  FIGS. 1-3 and 6-9  are shown in  FIGS. 4-5 . In these examples, the machine readable instructions comprise a program for execution by a processor such as the processor  1012  shown in the example processor platform  1000  discussed below in connection with  FIG. 10 . The program can be embodied in software stored on a tangible computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a BLU-RAY™ disk, or a memory associated with the processor  1012 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  1012  and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowchart and/or process(es) illustrated in  FIGS. 4-5 , many other methods of implementing the examples disclosed and described here can alternatively be used. For example, the order of execution of the blocks can be changed, and/or some of the blocks described can be changed, eliminated, or combined. 
     As mentioned above, the example process(es) of  FIGS. 4-5  can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a tangible computer readable storage medium such as a hard disk drive, a flash memory, a read-only memory (ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, a random-access memory (RAM) and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term tangible computer readable storage medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, “tangible computer readable storage medium” and “tangible machine readable storage medium” are used interchangeably. Additionally or alternatively, the example process(es) of  FIGS. 4-5  can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium such as a hard disk drive, a flash memory, a read-only memory, a compact disk, a digital versatile disk, a cache, a random-access memory and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, when the phrase “at least” is used as the transition term in a preamble of a claim, it is open-ended in the same manner as the term “comprising” is open ended. 
     The subject matter of this description may be implemented as stand-alone system or for execution as an application capable of execution by one or more computing devices. The application (e.g., webpage, downloadable applet or other mobile executable) can generate the various displays or graphic/visual representations described herein as graphic user interfaces (GUIs) or other visual illustrations, which may be generated as webpages or the like, in a manner to facilitate interfacing (receiving input/instructions, generating graphic illustrations) with users via the computing device(s). 
     Memory and processor as referred to herein can be stand-alone or integrally constructed as part of various programmable devices, including for example a desktop computer or laptop computer hard-drive, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), programmable logic devices (PLDs), etc. or the like or as part of a Computing Device, and any combination thereof operable to execute the instructions associated with implementing the method of the subject matter described herein. 
     Computing device as referenced herein can include: a mobile telephone; a computer such as a desktop or laptop type; a Personal Digital Assistant (PDA) or mobile phone; a notebook, tablet or other mobile computing device; or the like and any combination thereof. 
     Computer readable storage medium or computer program product as referenced herein is tangible (and alternatively as non-transitory, defined above) and can include volatile and non-volatile, removable and non-removable media for storage of electronic-formatted information such as computer readable program instructions or modules of instructions, data, etc. that may be stand-alone or as part of a computing device. Examples of computer readable storage medium or computer program products can include, but are not limited to, RAM, ROM, EEPROM, Flash memory, CD-ROM, DVD-ROM or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired electronic format of information and which can be accessed by the processor or at least a portion of the computing device. 
     The terms module and component as referenced herein generally represent program code or instructions that causes specified tasks when executed on a processor. The program code can be stored in one or more computer readable mediums. 
     Network as referenced herein can include, but is not limited to, a wide area network (WAN); a local area network (LAN); the Internet; wired or wireless (e.g., optical, Bluetooth, radio frequency (RF)) network; a cloud-based computing infrastructure of computers, routers, servers, gateways, etc.; or any combination thereof associated therewith that allows the system or portion thereof to communicate with one or more computing devices. 
     The term user and/or the plural form of this term is used to generally refer to those persons capable of accessing, using, or benefiting from the present disclosure. 
       FIG. 10  is a block diagram of an example processor platform  1000  capable of executing instructions to implement the example systems and methods disclosed and described herein. The processor platform  1000  can be, for example, a server, a personal computer, a mobile device (e.g., a cell phone, a smart phone, a tablet such as an IPAD™), a personal digital assistant (PDA), an Internet appliance, or any other type of computing device. 
     The processor platform  1000  of the illustrated example includes a processor  1012 . The processor  1012  of the illustrated example is hardware. For example, the processor  1012  can be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer. 
     The processor  1012  of the illustrated example includes a local memory  1013  (e.g., a cache). The processor  1012  of the illustrated example is in communication with a main memory including a volatile memory  1014  and a non-volatile memory  1016  via a bus  1018 . The volatile memory  1014  can be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory  1016  can be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  1014 ,  1016  is controlled by a memory controller. 
     The processor platform  1000  of the illustrated example also includes an interface circuit  1020 . The interface circuit  1020  can be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface. 
     In the illustrated example, one or more input devices  1022  are connected to the interface circuit  1020 . The input device(s)  1022  permit(s) a user to enter data and commands into the processor  1012 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system. 
     One or more output devices  1024  are also connected to the interface circuit  1020  of the illustrated example. The output devices  1024  can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touchscreen, a tactile output device, a light emitting diode (LED), a printer and/or speakers). The interface circuit  1020  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip or a graphics driver processor. 
     The interface circuit  1020  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem and/or network interface card to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  1026  (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     The processor platform  1000  of the illustrated example also includes one or more mass storage devices  1028  for storing software and/or data. Examples of such mass storage devices  1028  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives. 
     The coded instructions  1032  can be stored in the mass storage device  1028 , in the volatile memory  1014 , in the non-volatile memory  1016 , and/or on a removable tangible computer readable storage medium such as a CD or DVD. The instructions  1032  can be executed by the processor  1012  to implement the example system  100 , etc., as disclosed and described above. 
     From the foregoing, it will be appreciated that example methods, apparatus and articles of manufacture have been disclosed that improve processing of data and associated documents. The disclosed methods, apparatus and articles of manufacture improve the efficiency of using a computing device and an interface being driven by the computing device by providing relevant documents in the context of a particular patient and exam order for display and interaction via a single interface. In certain examples, access to the larger set of documents is also maintained. Certain examples improve a computer system and its process and user interface display through the ability to apply filters in a manner previously unavailable. While prior approaches did not provide such matching and filtering and suffered from lack of granularity which results in loss of relevant data, computing performance issues, impact on patient safety, etc., certain examples alter the operation of the computing device and provide a new interface and document interaction. The disclosed methods, apparatus and articles of manufacture are accordingly directed to one or more improvement(s) in the functioning of a computer, as well as a new matching methodology and user interface layout, structure, and interaction for patient and exam information. 
     Although certain example methods, apparatus and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the claims of this patent.