Abstract: A record linking platform having a computer system with a processor, multiple databases each having records, such as private health information. Each of the multiple databases are not in direct communication with one another. A control center is in communication with the different entities, the control center configured to: generate a site configuration file, a key configuration file, and a broker configuration file. A keymaster configured to: receive the key configuration file, generate a hash key, and transmit the hash key to the databases. The databases configured to: receive the site configuration file and the hash key; process the records to generate evaluation records data that conform to a data model specified in the site configuration file; and encrypt the evaluation records using the hash key. An honest broker configured to: receive the broker configuration file and the encrypted evaluation records; and link the encrypted evaluation records without decrypting.

Abstract: A record linking platform having a computer system with a processor, multiple databases each having records, such as private health information. Each of the multiple databases are not in direct communication with one another. A control center is in communication with the different entities, the control center configured to: generate a site configuration file, a key configuration file, and a broker configuration file. A keymaster configured to: receive the key configuration file, generate a hash key, and transmit the hash key to the databases. The databases configured to: receive the site configuration file and the hash key; process the records to generate evaluation records data that conform to a data model specified in the site configuration file; and encrypt the evaluation records using the hash key. An honest broker configured to: receive the broker configuration file and the encrypted evaluation records; and link the encrypted evaluation records without decrypting.

Abstract: System and method of managing a clinical trial in accordance with a protocol are provided. In accordance therewith, a workflow associated with the protocol is accessed from a first data structure. The workflow includes a plurality of workflow tasks to be performed in connection with treatment of a plurality of patients according to the protocol. Instruction is provided to clinical personnel to perform one or more workflow tasks according to the workflow in connection with a visit of a patient out of a plurality of visits. Progress of the patient through the workflow is recorded to a second data structure as performance or non-performance of the one or more workflow tasks in connection with the visit on a per-visit basis.

Abstract: Protocol meta-models are made available to a protocol designer. Each protocol meta-model includes a list of preliminary patient eligibility attributes appropriate for a particular disease category. The protocol designer chooses the appropriate meta-model, and encodes the clinical trial protocol, including eligibility and patient workflow, within the selected meta-model. The resulting protocol database is stored together with databases of other protocols in a library of protocol databases. Once a patient is enrolled into a study, the protocol database indicates to a clinician what tasks are to be performed at each patient visit. These tasks can include both patient management tasks and data management tasks.

Abstract: Protocol meta-models are made available to a protocol designer. Each protocol meta-model includes a list of preliminary patient eligibility attributes appropriate for a particular disease category. The protocol designer chooses the appropriate meta-model, and encodes the clinical trial protocol, including eligibility and patient workflow, within the selected meta-model. The resulting protocol database is stored together with databases of other protocols in a library of protocol databases. Once a patient is enrolled into a study, the protocol database indicates to a clinician what tasks are to be performed at each patient visit. These tasks can include both patient management tasks and data management tasks.

Abstract: Meta-models are made available to clinical trial protocol designers. A meta-model includes a list of preliminary patient eligibility attributes appropriate for a particular disease category. The protocol designer chooses the appropriate meta-model, and encodes the clinical trial protocol, including eligibility and patient workflow, within the selected meta-model in a protocol database. The resulting protocol database is stored together with databases of other protocols in a library of protocol databases. Sponsors and individual clinical sites have controlled access to the protocols. Study sites make reference to the protocol database in order to perform patient eligibility screening. Once a patient is enrolled into a study, the protocol database indicates to the clinician what workflow tasks are to be performed at each patient visit.

Abstract: Roughly described, a user instantiates protocol elements in a structured clinical trial protocol database and then draws from them in the development of one or more protocol related documents. The system helps the user select tasks to be performed during the study by reference to a historical database of tasks previously associated with similar protocols. The system automatically generates complex content from protocol elements in the database, and can render overlapping sets of protocol elements differently at different locations in the document. The system can automatically provide advisories indicating aspects of the document that still require completion or highlighting other issues that a sponsoring authority deems important for the document type. Alter all protocol elements are instantiated in the protocol database, it can then be used to drive the operation of most downstream aspects of the study.

Abstract: Clinical trials are defined, managed and evaluated according to an overall end-to-end system. The central authority creates protocol meta-models and makes them available to clinical trial protocol designers. Each meta-model includes a short list of preliminary patient eligibility attributes which are appropriate for a particular disease category. The protocol designer chooses the appropriate meta-model, and encodes the clinical trial protocol, including eligibility and patient workflow, within the selected meta-model. The resulting protocol database is stored together with databases of other protocols in a library of protocol databases. Sponsors and individual clinical sites have controlled access to the protocols. Study sites make reference to the pertinent protocol databases to which they have access in the protocol database library in order to perform patient eligibility screening.

Abstract: Structure and methodology for reducing risk of protocol ambiguities that could lead to operational failures in the conduct of clinical trials. Roughly described, an analyst encodes the features of a protocol into a highly structured, formal model created specifically to capture issues that tend to cause operational difficulties. The process forces the analyst to look for specific parameters in the text version of the protocol. The system creates a database of the protocol as encoded, and can display the protocol schema as a graphical network of protocol events and temporal links. In one aspect, the database includes an object class into which an analyst encodes descriptions of operational uncertainties. The system can display the network in graphical-visual form, with a human-perceptible indication of objects that have operational uncertainties associated therewith. Advantageously, the formal protocol model includes slots for encoding temporal constraints among protocol events.

Abstract: Roughly described, a user instantiates protocol elements in a structured clinical trial protocol database and then draws from them in the development of one or more protocol related documents. The system helps the user select tasks to be performed during the study by reference to a historical database of tasks previously associated with similar protocols. The system automatically generates complex content from protocol elements m the database, and can render overlapping sets of protocol elements differently at different locations in the document. The system can automatically provide advisories indicating aspects of the document that still require completion or highlighting other issues that a sponsoring authority deems important for the document type. After all protocol elements are instantiated in the protocol database, it can then be used to drive the operation of most downstream aspects of the study.

Abstract: Roughly described, a machine-readable protocol database identifies a sequence of workflow tasks for a clinical trial protocol. The sequence of workflow tasks is organized as a graph whose nodes can contain or represent patient contact event objects, with one or more of the tasks assigned to each patient contact event object. The graph also indicates preferred or expected times for a patient to transition from one node to the next, and optionally also indicates a predicted likelihood that different alternative paths will be taken to a common destination node. A problem-solving method automatically extracts the time duration expected or predicted for a patient to traverse each separate phase of the protocol. Such durations are provided to a simulation engine which automatically generates timeline forecasts of patient progress through at least part of the workflow tasks prescribed by the protocol.

Abstract: A clinical decision support application screens drug orders for drug dosing errors based on patient-specific information. The support application verifies that medication dosages are appropriately adjusted for renal function based upon calculated creatinine clearance (CrCI). In addition to looking for overdosing errors, other important features are its ability to detect under-dosing, and dosages and intervals that are not consistent with institution policy. The clinical decision support application also detects potentially dangerous drug combinations and serves as a safety net by providing a drug-drug interaction alert to a healthcare professional as well as reminders and escalations.

Abstract: An automated diabetes data interpretation method is provided which combines symbolic and numeric computing approaches in order to identify and highlight key clinical findings in the patient's self-recorded diabetes data. The patient data, including blood glucose levels and insulin dosage levels, recorded by a diabetic patient over a period of time by means of a glucose meter or the like, is initially downloaded into a central processing system such as a personal computer.