Patent Grant
10489717
This disclosure relates generally to the field of health care management and, more specifically, to a system and method for pharmacovigilance.
Pharmacovigilance is the science of collecting, monitoring, researching, assessing, and evaluating information from healthcare providers and patients on the adverse effects of medications with a view towards identifying hazards associated with the medications and preventing harm to patients.
A typical health care system includes a variety of participants, including doctors, hospitals, insurance carriers, and patients, among others. These participants frequently rely on each other for the information necessary to perform their respective roles because individual care is delivered and paid for in numerous locations by individuals and organizations that are typically unrelated. As a result, a plethora of health care information storage and retrieval systems are required to support the heavy flow of information between these participants related to patient care. Critical patient data is stored across many different locations using legacy mainframe and client-server systems that may be incompatible and/or may store information in non-standardized formats. To ensure proper patient diagnosis and treatment, health care providers often request patient information by phone or fax from hospitals, laboratories, or other providers. Therefore, disparate systems and information delivery procedures maintained by a number of independent health care system constituents lead to gaps in timely delivery of critical information and compromise the overall quality of clinical care. Since a typical health care practice is concentrated within a given specialty, an average patient may be using services of a number of different specialists, each potentially having only a partial view of the patient's medical status.
Moreover, pharmacovigilance is facing increased pressure from regulators and academics who are mining real-world databases for safety signals. Some factors affecting the pharmacovigilance landscape include: an increasing use of real-world data by regulators; heightened expectations of manufacturers from the FDA (Food and Drug Administration), public, and academics/investigators; externalization of safety data (e.g., EMR (electronic medical records); and emergence of pharmacovigilance as an applied science.
There are certain limitations to the way in which pharmacovigilance is currently being implemented. Firstly, pharmacovigilance, or drug surveillance, is typically done by “ad hoc” reporting, where a physician independently identifies patients that have a problem with a certain drug and report this singular instance to the FDA. The FDA then accumulates this information and communicates with pharmaceutical manufacturers. This process is inefficient and ineffective. To overcome some of the drawbacks of the ad hoc approach, the FDA has implemented the “Sentinel” and “Mini Sentinel” initiatives. However, these initiatives look at retrospective and/or historical data to perform drug surveillance.
Accordingly, there remains a need in the art for a system and method for pharmacovigilance that overcomes the drawbacks and limitations of current approaches.
Some embodiments provide systems, methods, and computer-readable storage media for displaying a graphical representation of relationships between a plurality of agents and a plurality of clinical outcomes. A method includes: receiving, by a processor included in a computing device, a selection of a plurality of agents; receiving, by the processor, a selection of a plurality of clinical outcomes; analyzing, by the processor, clinical data stored in a database to determine a number of occurrences for each clinical outcome when one or more agents are administered to a plurality of patients having a first clinical condition; calculating, by the processor, for each agent-clinical outcome pairing, a count for a number of patients having the first clinical condition, that were administered the agent of the agent-clinical outcome pairing, and had the clinical outcome of the agent-clinical outcome pairing; calculating, by the processor, for each agent-clinical outcome pairing, a relative risk score for patients having the first clinical condition, that were administered the agent of the agent-clinical outcome pairing, and had the clinical outcome of the agent-clinical outcome pairing; calculating, by the processor, for each agent-clinical outcome pairing, a statistical significance value for the relative risk score corresponding to the agent-clinical outcome pairing; and, displaying, in a graphical user interface on the display device, a two-dimensional grid in which one or more agents are displayed in a first axis and one or more clinical outcomes are displayed in a second axis, wherein a given agent-clinical outcome pairing is displayed in the graphical user interface if the count for the agent-clinical outcome pairing exceeds a first threshold, the relative risk score for the agent-clinical outcome pairing exceeds a second threshold, and the statistical significance value for the relative risk score for the agent-clinical outcome pairing exceeds a third threshold.
Some embodiments provide systems, methods, and computer-readable storage media for analyzing a relationship between an agent and a clinical outcome. A method includes: receiving, by a processor included in a computing device, a selection of a first agent; receiving, by the processor, a selection of a first clinical outcome; categorizing, by the processor and based on one or more stratification factors, a plurality of patients into a plurality of stratification categories, wherein each patient in the plurality of patients is associated with a first clinical condition and is administered the first agent; analyzing, by the processor, clinical data stored in a database to determine a number of occurrences of the first clinical outcome when the first agent is administered to the plurality of patients; calculating, by the processor, for the first agent and the first clinical outcome, a first set of risk scores, wherein a separate risk score corresponds to each of the plurality of stratification categories, and wherein calculating the risk score for a given stratification category includes measuring a statistical significance of a relationship between the first agent and the clinical outcome for the patients included in the given stratification category; displaying, in a graphical user interface on the display device, a two-dimensional grid in which the first clinical outcome is displayed in a first axis and the plurality of stratification categories are displayed in a second axis; and, displaying, in the graphical user interface, for each stratification category in which the first clinical outcome is observed, a graphical element corresponding to a relative risk score for the combination of first agent and the first clinical outcome for the stratification category.
Embodiments of the disclosure provide a system and method for pharmacovigilance. According to some embodiments, health related clinical or other data is stored in one or more databases. The clinical data may include, for each patient, demographic data, diagnostic codes (e.g., ICD (International Statistical Classification of Diseases and Related Health Problems) 9 and/or ICD 10), procedure codes (e.g., CPT (Current Procedural Terminology) codes, HCPCS (The Healthcare Common Procedure Coding System) codes), medication and prescription data (e.g., NDC (National Drug Code) and GPI (Generic Product Identifier)), and lab data (e.g., LOINC (Logical Observation Identifiers Names and Codes), among others. Clinical data may also include data from electronic medical records (EMRs) and/or publicly available database (e.g., Medwatch). Other data may include medical cost data, including pharmaceutical costs, care and treatment costs, and the like. Other data may include genetic information data and/or data from patient devices, such as computers, smart phones, and wearable devices (e.g., fitness trackers, heart rate monitors, and the like. Further, other data may include data from consumer activity databases such as credit card transaction databases, online search activity databases and social media activity databases (e.g., Facebook, Twitter). Further still, other data which may be considered, in an adverse event surveillance embodiment of the present disclosure, includes demographic data, geographic data (e.g., zip code), employment data, and/or family relationship data.
A processor in a computer system is configured to receive a selection of one or more agents (e.g., drugs) and one or more clinical outcomes. Examples of clinical outcomes include, for example, adverse events, productivity of a workforce, violent crimes in a population, etc. The processor is configured to calculate a risk score for the one or more clinical outcomes in relation to the one or more agents. In one or more embodiments, the processor is configured to calculate a risk score for the one or more clinical outcomes based on a prioritized, weighted list of data sources (e.g., where clinical data is weighted relatively higher than consumer activity data). According to various embodiments, the risk score may be an absolute risk or a relative risk. In some embodiments, one or more of a Chi-squared statistical analysis and a P-value statistical analysis may also be performed to confirm or reject the observed calculations.
Accordingly, some embodiments provide a proactive, prospective, and ongoing approach to pharmacovigilance. The database from which the analysis is performed is continuously being updated with new clinical data. For example, medical claims data may be entered into the database within 48 hours of an insurance carrier receiving information about the treatment. In some embodiments, the database from which the analysis is performed is continuously (or substantially continuously) updated from claims databases of a plurality of healthcare organizations and/or insurance carriers.
Some embodiments disclosed herein provide a proactive and automated signal detection, surveillance, and reporting system with standardized reporting. Examples of reporting systems used with embodiments of the disclosure include providing reporting interfaces that report information to drug manufacturers, the FDA (Food and Drug Administration), to the public (e.g., label warning updates), to product liability insurers, and/or to individual patient patients (e.g., Patient A is on drug X, but the system detected that there are side effects when Drug X is taken with apples). In some embodiments, notices may be sent directly to registered patient devices (e.g., mobile devices, etc.). Advantageously, reporting on information may be helpful to drug manufacturers or health plan organizations for: performing second-level confirmatory analytics, in reapplying for additional off-label uses (e.g., different patient populations (e.g., by gender, ethnicity, age band, etc.), in exonerating a drug for broader use within the population (e.g., by narrowing the risk to particular genders, ethnicity, age bands, etc.), in applying for an unanticipated use (e.g., where an unanticipated benefit or harm has been identified), for re-pricing (e.g., reports can be used by health plans to inform negotiations for “value based” pricing of drugs; can inform drug manufacturers on higher value for drugs with new/expanded uses), for refining criteria for plan benefit eligibility, and for remarketing a drug, among other uses.
Some embodiments provide real-time monitoring due to rapid adjudication and incorporation of claims data into analytic database, and a signal validation system that can exonerate or stratify risk in near real-time and identify potential benefits, versus an industry average of six to nine months.
Turning to the figures,
When the patient 102 utilizes the services of one or more health care providers 110, a medical insurance carrier 112 collects the associated clinical data 114 in order to administer the health insurance coverage for the patient 102. Additionally, a health care provider 110, such as a physician or nurse, enters clinical data 114 into one or more health care provider applications pursuant to a patient-health care provider interaction during an office visit or a disease management interaction. Clinical data 114 originates from medical services claims, pharmacy data, as well as from lab results, and includes information associated with the patient-health care provider interaction, including information related to the patient's diagnosis and treatment, medical procedures, drug prescription information, in-patient information, and health care provider notes, among other things. The medical insurance carrier 112 and the health care provider 110, in turn, provide the clinical data 114 to the health care organization 100, via one or more networks 116, for storage in one or more medical databases 118. The medical databases 118 are administered by one or more server-based computers associated with the health care provider 100 and comprise one or more medical data files located on a computer-readable medium, such as a hard disk drive, a CD-ROM, a tape drive, or the like. The medical databases 118 may include a commercially available database software application capable of interfacing with other applications, running on the same or different server based computer, via a structured query language (SQL). In an embodiment, the network 116 is a dedicated medical records network. Alternatively, or in addition, the network 116 includes an Internet connection that comprises all or part of the network.
In some embodiments, an on-staff team of medical professionals within the health care organization 100 consults various sources of health reference information 122, including evidence-based preventive health data, to establish and continuously or periodically revise a set of clinical rules 120 that reflect best evidence-based medical standards of care for a plurality of conditions. The clinical rules 120 are stored in the medical database 118.
To supplement the clinical data 114 received from the insurance carrier 112, the PHR 108 allows patient entry of additional pertinent medical information that is likely to be within the realm of patient's knowledge. Examples of patient-entered data include additional clinical data, such as patient's family history, use of non-prescription drugs, known allergies, unreported and/or untreated conditions (e.g., chronic low back pain, migraines, etc.), as well as results of self-administered medical tests (e.g., periodic blood pressure and/or blood sugar readings). Preferably, the PHR 108 facilitates the patient's task of creating a complete health record by automatically populating the data fields corresponding to the information derived from the medical claims, pharmacy data and lab result-based clinical data 114. In one embodiment, patient-entered data also includes non-clinical data, such as upcoming doctor's appointments. In some embodiments, the PHR 108 gathers at least some of the patient-entered data via a health risk assessment tool (HRA) 130 that requests information regarding lifestyle, behaviors, family history, known chronic conditions (e.g., chronic back pain, migraines, etc.), and other medical data, to flag individuals at risk for one or more predetermined medical conditions (e.g., cancer, heart disease, diabetes, risk of stroke, etc.) pursuant to the processing by a calculation engine 126. Preferably, the HRA 130 presents the patient 102 with questions that are relevant to his or her medical history and currently presented conditions. The risk assessment logic branches dynamically to relevant and/or critical questions, thereby saving the patient time and providing targeted results. The data entered by the patient 102 into the HRA 130 also populates the corresponding data fields within other areas of PHR 108. The health care organization 100 aggregates the clinical data 114 and the patient-entered data, as well as the health reference and medical news information 122, into the medical database 118 for subsequent processing via the calculation engine 126.
The health care organization 100 includes a multi-dimensional analytical software application including a calculation engine 126 comprising computer-readable instructions executed by one or more processors for performing statistical analysis on the contents of the medical databases 118 in order to analyze a relationship between one or more agents and one or more clinical outcomes. The relationships identified by the calculation engine 126 can be presented in a graphical display 104, e.g., to the healthcare provider 110 and/or medical insurance carrier 112 and/or to the government (e.g., FDA) and/or to the patient 102.
After collecting the relevant data, the calculation engine 126 receives a selection of one or more agents. In one example implementation, the agents are prescription drugs. The calculation engine calculates a risk of occurrence of one or more clinical outcomes for each of the one or more agents. In some embodiments, the calculation engine 126 also receives a selection of an “indication” (e.g., a medical or clinical condition, disease, etc.) experienced by a portion of the population of patients. In one implementation, a drug may be exonerated from causing a clinical outcome for specific subgroups of a population (e.g., those that also have the “indication”) or possibly overall (e.g., entire population). In some implementations, a drug may be exonerated when taken in combination with other criteria present; for example, when taken with other drugs, when taken with certain foods, or when exercise is detected, among others. In another example implementation, the calculation engine 126 may determine that certain adverse events occur mostly in off-label use. “Off-label” use refers to non-recommended uses of a drug, such as non-FDA approved uses. In another implementation, calculation engine 126 may determine how a drug's safety profile compares to other drugs within the same class of drugs. Other use cases are also within the scope of embodiments of the disclosure, as described in greater detail herein. In further embodiments, the calculation engine 126 may determine that a drug is “protective” relative to a certain clinical outcome for a certain sub-population of patients, as described in greater detail herein.
For example, embodiments disclosed herein can provide “comparative effectiveness” information by directly comparing multiple pharmacologically similar agents against varied and multiple health outcomes of interest, allowing for inferences to be made about the comparative risks and benefits of these agents. In some embodiments, a comparison process may include the steps of identifying a subgroup based on age, gender, race, ethnicity, geography or other categories, and comparing the subgroup to other subgroup(s) based on the same categories to determine if a given agent or intervention is more or less effective (or harmful) as compared to another agent or intervention within a given subgroup. In another embodiment, a comparison process may be employed to simultaneously determine risks and benefits associated with a given agent in a given subgroup, and output a graphical summary which can be used to inform a risk/benefit determination by individual patients and/or healthcare providers.
While the entity relationships described above are representative, those skilled in the art will realize that alternate arrangements are possible. In one embodiment, for example, the health care organization 100 and the medical insurance carrier 112 is the same entity. Alternatively, the health care organization 100 is an independent service provider engaged in collecting, aggregating, and processing medical care data from a plurality of sources to provide a personal health record (PHR) service for one or more medical insurance carriers 112. In yet another embodiment, the health care organization 100 provides PHR services to one or more employers by collecting data from one or more medical insurance carriers 112. In one embodiment, an insurance carrier computer system executes the calculation engine 126. In yet another embodiment, a third party computer system receives medical care (and other) data from a plurality of sources, including multiple medical insurance carriers and health care organizations, and executes the calculation engine 126.
At step 206, the processor analyzes clinical data in a database to determine a number of occurrences of the adverse event when the agent is administered. As described, the clinical data can come from many sources, including demographic data, claims data, procedure codes, diagnostic codes, pharmacy/prescription data, patient-entered data, among others. The processor analyzes the data to identify a number of patients that have exhibited the adverse event when taking the drug for a predetermined minimum amount of time (for example, 6 months).
At step 208, the processor applies one or more filters. The clinical data can be filtered according to certain parameters, such as patient age, gender, demographic info, clinical stratification scores and identified conditions, and whether the use of the drug was “on-label” or “off-label” (i.e., “on-label” refers to use in the recommended or FDA approved manner; “off-label” refers to use in a non-recommended or non-FDA approved manner), among others. The analysis performed at step 206 can, therefore, be applied only to the data that satisfies the filters. In some embodiments, step 208 is performed before step 206. Also, in some embodiments, step 208 is optional and is omitted. In such a case, no filter is applied, and all the clinical data is analyzed.
At step 210, the processor calculates a risk score corresponding to the adverse event and the agent. According to some embodiments, the risk score can be an absolute risk or a relative risk. Table 1 below illustrates occurrences of the adverse event when a particular drug is administered, a total number of patients that suffered the adverse event, a total number of patients to whom the drug was administered, and a total number of patients to whom the drug was not administered.
In Table 1, “IAO” refers to the occurrence of the adverse event when the drug is administered, “IO” refers to the total number of patients that suffered the adverse event, “IA” refers to the total number of patients to whom the drug was administered, and “I” refers to the total number of patients to whom the drug was not administered.
According to one embodiment, an “ON agent risk,” “NO agent risk,” “Absolute Risk,” and “Relative Risk” can be calculated using Equations 1 to 4, respectively:
A “chi-squared” analysis can also be performed to calculate a confidence level for the statistical analysis performed using Equation 5:
In some embodiments, a “Chi-squared value” or “P-value” may be calculated to test the statistical significance of the calculations.
Table 2, below, illustrates an example where the adverse event is congestive heart failure (CHF) and the drug is an ACE inhibitor.
As shown, a total of 179499 patients took the drug and 568 experienced the adverse effect. A total of 2433 patients experienced the adverse effect. A total of 656938 patients did not take the drug.
Using the Equations 1-4 above, the relative risk is calculated at 0.81. The Chi-squared value is calculated using Equation 5 as 19.49.
At step 212, the processor determines whether there are more adverse events to analyze for the selected agent/drug. If the processor determines that there are more adverse events to analyze for the selected agent/drug, then the method 200 returns to step 204, described above. If the processor determines that there are no more adverse events to analyze for the selected agent/drug, then the method 200 proceeds to step 214.
At step 214, the processor determines whether there are more agents/drugs to analyze against adverse events. If the processor determines that there are more agents/drugs to analyze, then the method 200 returns to step 202, described above. If the processor determines that there are no more agents/drugs to analyze, then the method 200 proceeds to step 216.
At step 216, the processor outputs results (i.e., risk scores) to a graphical display. In some embodiments, the results may be graphically represented as a “heat map,” where a circle corresponds to the average relative risk of the drug-adverse event combination, and where a greater size of the circle corresponds to a greater average relative risk. Examples are provided below in
As described above, a processor can calculate a risk score, such as average relative risk, for each combination of agent and outcome. In the example shown in
In addition, in some embodiments, a user can click on or hover a cursor over one of the circles, which causes a dialog box 510 to be displayed. The dialog box 510 includes various counts and statistics for the particular agent-outcome pair.
In
In
In
In the additional embodiment illustrated in
In one embodiment, the real-time transfer module 758 comprises web services 762, 764 that interface with external applications for transporting the real-time data via a Simple Object Access Protocol (SOAP) over HTTP (Hypertext Transfer Protocol). The message ingest web service 762, for example, receives real-time data that is subsequently processed in real-time by the calculation engine 126. The message ingest web service 762 synchronously collects clinical data 114 from the medical insurance carrier 112, patient-entered data 128, including patient-entered clinical data 128, from the patient's PHR 108 and HRA 130, as well as health reference information 122 and medical news information 124. In an embodiment, the message ingest web service 762 also receives clinical data 114 in real-time from one or more health care provider applications 756, such as an electronic medical record (EMR) application and a disease management application. In yet another embodiment, the message ingest service 762 receives at least some of the patient-entered data 128 pursuant to the patient's interaction with a nurse in disease management or an integrated voice response (IVR) system. Incoming real-time data is optionally stored in the medical database 118. Furthermore, incoming real-time data associated with a given patient 102, in conjunction with previously stored data at the database 118 and the clinical rules 120, defines a rules engine run to be processed by the calculation engine 126. Hence, the real-time transfer module 758 collects incoming real-time data from multiple sources and defines a plurality of rules engine runs associated with one or more agents (e.g., drugs) and one or more outcomes (e.g., adverse events) for real-time processing.
The real-time transfer module 758 forwards the rules engine runs to the calculation engine 126 to instantiate a plurality of real-time rule processing sessions 772. The processing of the rule processing sessions 772 by the calculation engine 126 can be load-balanced across multiple logical and physical servers to facilitate multiple and simultaneous requests for real-time calculation of risk scores for one or more pairs of agents and outcomes. In one embodiment, the load-balancing of sessions 772 is accomplished in accordance with a J2EE (Java) specification. Each rule processing session 772 makes calls to the medical database 118 by referring to a unique agent ID field for a corresponding agent (e.g., drug) to receive data related to that agent for processing of incoming real-time data. The results 1000 of the real-time processing of the calculation engine may then be output to the real-time transfer module 758 for distribution to one or more health care provider applications 756 and/or to other servers and/or services via message output service 764.
Thus, embodiments of the disclosure provide user interface elements in the graphical user interface to scale and/or filter the results. As shown, user interface element 1104 may correspond to statistical significance of the data (e.g., Chi-squared or P-value), user interface element 1106 may correspond to count of “IAO,” i.e., where the Indication (“I”), Agent (“A”), and Outcome (“O”) are each present, and user interface element 1108 may correspond to average relative risk. In some implementations, “statistically significant” results corresponding to the user interface element 1104 may comprise results with a Chi-squared value of five (5.0) or more. In the example shown in
A user may interact with one or more of the user interface elements 1104, 1106, 1108 to filter the data in an effort to reduce the number of agent-outcome results shown in the user interface. In the example in
As described herein, a plurality of agents can be analyzed against a plurality of outcomes. In
As shown in
In the example shown in
In the example in
Further, as shown in
Based on the stratified data shown in
In some embodiments, when an agent has been determined to be harmful, the processor may further send a notification to one or more entities to inform them of this potential risk. For example, the processor may notify the FDA (Food and Drug Administration), the public (e.g., label warning updates), product liability insurers, and/or individual patient patients. In some embodiments, notices may be sent directly to registered patient devices (e.g., smart phones, etc.). In some embodiments, reporting on information may be helpful to drug manufacturers or health plan organizations for: performing second-level confirmatory analytics, in reapplying for additional off-label uses (e.g., different patient populations (e.g., by gender, ethnicity, age band, etc.), in exonerating a drug for broader use within the population (e.g., by narrowing the risk to particular genders, ethnicity, age bands, etc.), in applying for an unanticipated use (e.g., where an unanticipated benefit or harm has been identified), for re-pricing (e.g., reports can be used by health plans to inform negotiations for “value based” pricing of drugs; can inform drug manufacturers on higher value for drugs with new/expanded uses), for refining criteria for plan benefit eligibility, and for remarketing a drug, among other uses. In some embodiments, the notification is sent only if the agent-outcome pairing indicates a relative risk greater than 1, but also greater than a certain threshold.
At step 1506, the processor analyzes clinical data stored in a database to determine a number of occurrences for each clinical outcome when one or more agents are administered to a plurality of patients having a first clinical condition. The clinical data stored in the database may include demographic data, lab data, pharmacy data, claims data, diagnostic codes, procedure codes, heath reference information, medical news, standards-of-care, and/or patient-entered data.
At step 1508, the processor calculates, for each agent-clinical outcome pairing, a count for a number of patients having the first clinical condition, that were administered the agent of the agent-clinical outcome pairing, and had the clinical outcome of the agent-clinical outcome pairing. At step 1510, the processor calculates, for each agent-clinical outcome pairing, a relative risk score for patients having the first clinical condition, that were administered the agent of the agent-clinical outcome pairing, and had the clinical outcome of the agent-clinical outcome pairing. At step 1512, the processor calculates, for each agent-clinical outcome pairing, a statistical significance value for the relative risk score corresponding to the agent-clinical outcome pairing. Calculating the statistical significance value comprises calculating one or more of a Chi-squared value and a P-value.
At step 1514, the processor displays, in a graphical user interface on the display device, a two-dimensional grid in which one or more agents are displayed in a first axis and one or more clinical outcomes are displayed in a second axis, where a given agent-clinical outcome pairing is displayed in the graphical user interface if the count for the agent-clinical outcome pairing exceeds a first threshold, the relative risk score for the agent-clinical outcome pairing exceeds a second threshold, and the statistical significance value for the relative risk score for the agent-clinical outcome pairing exceeds a third threshold.
The graphical user interface may further include a first slider graphical user interface element corresponding to the first threshold value, where adjusting the first slider causes the first threshold value to be adjusted. The graphical user interface may further include a second slider graphical user interface element corresponding to the second threshold value, wherein adjusting the second slider causes the second threshold value to be adjusted. The graphical user interface may further include a third slider graphical user interface element corresponding to the third threshold value, wherein adjusting the third slider causes the third threshold value to be adjusted.
At step 1606, the processor categorizes, based on one or more stratification factors, a plurality of patients into a plurality of stratification categories, wherein each patient in the plurality of patients is associated with a first clinical condition and is administered the first agent. In some embodiments, the one or more stratification factors include one or more of: a gender stratification factor, an age category stratification factor, a risk category stratification factor, and a geographic location stratification factor.
At step 1608, the processor analyzes clinical data stored in a database to determine a number of occurrences of the first clinical outcome when the first agent is administered to the plurality of patients. At step 1610, the processor calculates, for the first agent and the first clinical outcome, a first set of risk scores, wherein a separate risk score corresponds to each of the plurality of stratification categories, and wherein calculating the risk score for a given stratification category includes measuring a statistical significance of a relationship between the first agent and the clinical outcome for the patients included in the given stratification category.
At step 1612, the processor displays, in a graphical user interface on the display device, a two-dimensional grid in which the first clinical outcome is displayed in a first axis and the plurality of stratification categories are displayed in a second axis.
At step 1614, the processor displays, in the graphical user interface, for each stratification category in which the first clinical outcome is observed, a graphical element corresponding to a relative risk score for the combination of first agent and the first clinical outcome for the stratification category. In one embodiment, a relative risk score less than 1.0 is displayed in a first color, and a relative risk score greater than 1.0 is displayed in a second color. In some embodiments, each graphical element displayed in the graphical user interface comprises a circle, where for a relative risk greater than 1.0 a larger circle corresponds to a greater relative risk.
In some embodiments, based on determining that the first relative risk score is less than 1.0 and less than a first threshold value, the processor determines that the first agent is protective with respect to the first clinical outcome for patients in the first stratification category and associated with the first clinical condition. In some embodiments, based on determining that the first relative risk score is greater than 1.0 and greater than the first threshold value, the processor determines that the first agent is harmful with respect to the first clinical outcome for patients in the first stratification category and associated with the first clinical condition. In some embodiments, based on determining that the first clinical outcome is not observed, the processor determines that the first agent is exonerated from causing the first clinical outcome for patients in the first stratification category and associated with the first clinical condition. In some embodiments, absence of a first stratification category in the second axis corresponds to the first clinical outcome not being observed for the patients administered the first agent in the first stratification category.
In sum, embodiments described herein provide a system and method for pharmacovigilance, i.e., drug surveillance. The systems and methods described herein may, in some implementations, be used by drug companies or others (such as, for example, the FDA) to monitor and test the safety and efficacy of drugs with respect to certain outcomes. The systems and methods could be customized by applying certain filters to analyze the data at finer granularity.
Some embodiments compute the clinical context of a health outcome or adverse event, rather than simply pairing a drug to a health outcome of interest. In various implementations, this includes analyzing the existence of an FDA-labeled indication for the drug (i.e., on-label use versus off-label use), the relative frequency of the symptoms for the outcome of interest (e.g., dizziness or palpitations may be symptoms of an arrhythmia), the relative frequency of testing for the outcome of interest (e.g., Holter EKG monitoring may be used to detect arrhythmias) to calibrate whether frequency of the outcome of interest (e.g., there may appear to be more liver abnormalities just because more liver function testing was being done), the relative frequency of the outcome itself, and the relative frequency of “rescue treatments” related to the outcome, e.g. for a drug that causes diarrhea, the frequency of anti-diarrheal treatments (as opposed to episodes of the diarrhea itself).
Embodiments aggregate this data in a manner not only to detect new signals of drug-adverse event relationships, but can be configured in a way to “exonerate” drugs or confirm drug effects by providing data to suggest that (i) no agent-outcome relationships were detected, (ii) limited agent-outcome relationships were detected (e.g., in a subset of a population previously believed to be at risk of a negative effect), or (iii) a previous agent-outcome relationship is affirmed. Data resulting from such a determination may be used in assessing liability associated with the manufacture, marketing and/or sale of pharmaceuticals. In this way, drugs that may appear to be generating signals in the FDA AERS (Adverse Event Reporting System) may be compared against the signal confirmation versus exoneration findings calculated using embodiments of the disclosure. For example, using the embodiments disclosed herein, which are capable of updating on a near-real-time basis by running analysis on a frequent repeated basis (e.g., weekly, monthly), signals are detected earlier and trend analysis for emerging and/or fading signals can be performed more quickly.
In another embodiment, adverse events or benefits associated with a given drug may be detected across multiple related individuals including teratogenic effects on children. For example, where a mother takes a given drug during pregnancy, the child may be exposed to and suffer consequences during fetal development, with observable long-term consequences which can be detected through the present invention.
In another embodiment, an association between an event and an agent or intervention may be evaluated to determine if a causal relationship among the two potentially exists (or, if the association is a spurious correlation) by evaluating healthcare claim data to determine the sequential relationship between the event and the agent.
All references, including publications, patent applications and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
The use of the terms “a” and “an” and “the” and similar referents in the context of describing the disclosure (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the disclosure and does not pose a limitation on the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the disclosure.
Preferred embodiments of this disclosure are described herein, including the best mode known to the inventors for carrying out the disclosure. Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the disclosure to be practiced otherwise than as specifically described herein. Accordingly, this disclosure includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the disclosure unless otherwise indicated herein or otherwise clearly contradicted by context.
This patent application is a continuation-in-part of U.S. patent application Ser. No. 14/286,102 filed on May 23, 2014, which is a continuation of U.S. patent application Ser. No. 13/733,791 filed on Jan. 3, 2013 (now issued as U.S. Pat. No. 8,744,872), both of which are hereby incorporated by reference in their entireties.
| Number | Name | Date | Kind |
|---|---|---|---|
| 6461245 | Morgan | Oct 2002 | B1 |
| D521017 | Jewitt et al. | May 2006 | S |
| D602945 | Watanabe et al. | Oct 2009 | S |
| D693846 | Daniel | Nov 2013 | S |
| D696285 | Hally | Dec 2013 | S |
| D728611 | Takano | May 2015 | S |
| D733181 | Manfredo et al. | Jun 2015 | S |
| D746832 | Pearcy et al. | Jan 2016 | S |
| 9235686 | Jackson et al. | Jan 2016 | B2 |
| D760248 | Suarez | Jun 2016 | S |
| 9413807 | Sherman et al. | Aug 2016 | B1 |
| D765678 | Goux | Sep 2016 | S |
| D769899 | Salazar Cardozo et al. | Oct 2016 | S |
| D769918 | Kim et al. | Oct 2016 | S |
| D771676 | Binder et al. | Nov 2016 | S |
| D772245 | Mandawat et al. | Nov 2016 | S |
| D772250 | Kohan et al. | Nov 2016 | S |
| D775652 | Hoang et al. | Jan 2017 | S |
| D779545 | Chen et al. | Feb 2017 | S |
| D781303 | Lukanuski et al. | Mar 2017 | S |
| D786900 | Parmar et al. | May 2017 | S |
| D788126 | Evnin et al. | May 2017 | S |
| D789952 | Maitlen et al. | Jun 2017 | S |
| D789984 | Sun | Jun 2017 | S |
| D791163 | Millares | Jul 2017 | S |
| D791796 | Baudisch et al. | Jul 2017 | S |
| D792904 | Nakaguchi et al. | Jul 2017 | S |
| D794054 | Chaudhri | Aug 2017 | S |
| D795891 | Kohan et al. | Aug 2017 | S |
| D799500 | Selden et al. | Oct 2017 | S |
| D799505 | Park et al. | Oct 2017 | S |
| D802622 | Clymer et al. | Nov 2017 | S |
| D804500 | Fischer et al. | Dec 2017 | S |
| 20030046110 | Gogolak | Mar 2003 | A1 |
| 20060206512 | Hanrahan et al. | Sep 2006 | A1 |
| 20070061611 | Mackinlay et al. | Mar 2007 | A1 |
| 20070250523 | Beers et al. | Oct 2007 | A1 |
| 20070294112 | Settimi | Dec 2007 | A1 |
| 20090319556 | Stolte et al. | Dec 2009 | A1 |
| 20090319891 | MacKinlay et al. | Dec 2009 | A1 |
| 20100223068 | Von Schweber et al. | Sep 2010 | A1 |
| 20110027291 | Schoeberl et al. | Feb 2011 | A1 |
| 20110302110 | Beers et al. | Dec 2011 | A1 |
| 20120084064 | Dzenis et al. | Apr 2012 | A1 |
| 20120179482 | Garms et al. | Jul 2012 | A1 |
| 20120271612 | Barsoum et al. | Oct 2012 | A1 |
| 20130016106 | Yip et al. | Jan 2013 | A1 |
| 20130054621 | Kretz et al. | Feb 2013 | A1 |
| 20130132444 | Chen et al. | May 2013 | A1 |
| 20140282147 | Kim et al. | Sep 2014 | A1 |
| 20140282163 | MacKinlay et al. | Sep 2014 | A1 |
| 20140282187 | Mackinlay et al. | Sep 2014 | A1 |
| 20150278213 | Anand et al. | Oct 2015 | A1 |
| 20150278214 | Anand et al. | Oct 2015 | A1 |
| 20150278371 | Anand et al. | Oct 2015 | A1 |
| 20160070430 | Kim et al. | Mar 2016 | A1 |
| 20160070451 | Kim et al. | Mar 2016 | A1 |
| 20160274750 | Stewart | Sep 2016 | A1 |
| 20160342292 | Stewart | Nov 2016 | A1 |
| 20160343154 | Stewart | Nov 2016 | A1 |
| 20170010776 | Stewart | Jan 2017 | A1 |
| 20170010785 | Stewart | Jan 2017 | A1 |
| 20170010786 | Stewart | Jan 2017 | A1 |
| 20170010792 | Stewart | Jan 2017 | A1 |
| Entry |
|---|
| “The Sentinel Initiative—National Strategy for Monitoring medical Product Safety”; May (2008) FDA. |
| Number | Date | Country | |
|---|---|---|---|
| 20170004275 A1 | Jan 2017 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13733791 | Jan 2013 | US |
| Child | 14286102 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 14286102 | May 2014 | US |
| Child | 15252026 | US |
