Patent Application
20170124281
Medication non-adherence is one of the most expensive problems in healthcare. Nearly 300 billion of the $750 billion the United States spends annually on healthcare could be avoided with improved medication adherence which equates to around $2,000 per patient. Moreover, medication non-adherence causes 30-50% of treatment failures and results in 125,000 deaths annually. It has been determined that medication adherence is the number one thing that patients lie to their doctors about. Out of every 100 prescriptions written, 50-70% are filled, 48-66% are picked up, 25-30% are taken as directed, and only 15-20% are refilled as prescribed.
Medication non-adherence is a multi-faceted problem. The majority of solutions have focused on the most common reason for non-adherence—forgetfulness. However, this only accounts for about 25% of non-adherence. Other commonly reported risk factors for non-adherence include side effects of the medication, cost of the medication, the patient's impression that the medication is not necessary, and/or the inability of the patient to obtain transportation to access a pharmacy.
There is a need for a system that will enhance communication between venues of care, decrease the risk of medication reconciliation errors, and provide the clinician with an accurate picture of which medications the patient is filling in an outpatient setting. There is a need for a system that can recognize patterns of medication non-adherence in order to provide interventions to improve medication adherence and prevent adverse events from occurring.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The present invention is defined by the claims.
In brief and at a high level, this disclosure describes, among other things, methods, systems, and computer-readable media for tracking prescription fills and determining medication adherence patterns for patients. As previously mentioned, patients often fail to fill and use their prescription medications as prescribed. By tracking a patient's medication fill history, a physician can determine whether a patient is taking medications as directed. If the patient is not adhering to prescribed instructions for taking medications, a physician can determine if the patient's non-adherence puts the patient at risk for additional health problems. Barriers to adherence can be determined and interventions can be applied to address those barriers. Tracking a patient's medication adherence can prevent adverse health events from occurring due to non-adherence.
In one embodiment, computer-storage media having computer-executable instructions embodied thereon that when executed, performs a method of tracking medication adherence is provided. A selection of a patient is received. Data sources are queried to find prescription data for the patient. The prescription data is extracted from the data sources and compiled into a medication refill history for the patient. The medication refill history is then analyzed to determine medication adherence patterns for the patient. Finally, a medication adherence graphic is automatically generated to represent the medication adherence patterns for the patient.
In another embodiment, a computerized method is carried out by at least one server having at least one processor for determining medication adherence patterns for a patient. A patient is selected then medication information related to the patient is retrieved from various sources. A prescription order and refill history is assembled based on the medical information and a level of medication adherence for the patient is determined by analyzing the prescription refill history. The prescription refill history is then displayed, indicating patterns of medication adherence for the patient. The patterns of medication adherence are analyzed to identify barriers to adherence and then clinical interventions are recommended to address those barriers.
In yet another embodiment, a computer-implemented system is designed to track medication adherence. A computer having at least one processor performs a number of steps beginning with receiving a selection of a patient. Medication data for the patient is then extracted from prescription data sources. A prescription refill history is constructed for the patient. The percentage of days covered for each medication prescribed is calculated, as is a medication adherence score for the patient. A base adherence pattern label and one or more add-on adherence pattern labels are assigned to the patients and the patient's medications. The patient's medication adherence patterns are then displayed and analyzed to identify barriers to medication adherence. Finally, intervention strategies are automatically generated to address the barriers to medication adherence.
Embodiments are described in detail below with reference to the attached drawings figures, wherein:
The subject matter of the present invention is described with specificity herein to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different steps or combinations of steps similar to the ones described in this document, in conjunction with other present or future technologies. Moreover, although the terms “step” and/or “block” may be used herein to connote different elements of methods employed, the terms should not be interpreted as implying any particular order among or between various steps herein disclosed unless and except when the order of individual steps is explicitly described.
Embodiments of the present invention are directed to methods, systems, and computer-readable media for determining and tracking medication adherence patterns for a patient. Medication adherence refers to whether a patient is taking medications correctly as prescribed. As previously mentioned, lack of medication adherence is a serious problem within the healthcare industry. While clinicians may not be able to monitor a patient's daily intake of medications, they are able to use the present invention to access patient information regarding fills and refills of the patient's prescriptions. These records provide an indication of the patient's adherence levels by comparing actual prescription fills and refills with what is prescribed by a clinician and presenting adherence patterns to the clinician in an easy to read visual format.
An exemplary computing environment suitable for use in implementing embodiments of the present invention is described below.
The present invention might be operational with numerous other purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that might be suitable for use with the present invention include personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above-mentioned systems or devices, and the like.
The present invention might be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Exemplary program modules comprise routines, programs, objects, components, and data structures that perform particular tasks or implement particular abstract data types. The present invention might be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules might be located in association with local and/or remote computer storage media (e.g., memory storage devices).
With continued reference to
The control server 102 typically includes therein, or has access to, a variety of non-transitory computer-readable media. Computer-readable media can be any available media that might be accessed by control server 102, and includes volatile and nonvolatile media, as well as, removable and nonremovable media. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk 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 information and which can be accessed by control server 102. Communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.
The control server 102 might operate in a computer network 106 using logical connections to one or more remote computers 108. Remote computers 108 might be located at a variety of locations in a medical or research environment, including clinical laboratories (e.g., molecular diagnostic laboratories), hospitals and other inpatient settings, veterinary environments, ambulatory settings, medical billing and financial offices, hospital administration settings, pharmacies, and clinicians' offices. Clinicians may comprise a treating physician or physicians; specialists such as surgeons, radiologists, cardiologists, and oncologists; emergency medical technicians; physicians' assistants; nurse practitioners; nurses; nurses' aides; pharmacists; dieticians; microbiologists; laboratory experts; laboratory technologists; genetic counselors; researchers; veterinarians; students; and the like. The remote computers 108 might also be physically located in nontraditional medical care environments so that the entire healthcare community might be capable of integration on the network. The remote computers 108 might be personal computers, servers, routers, network PCs, peer devices, other common network nodes, or the like and might comprise some or all of the elements described above in relation to the control server 102. The devices can be personal digital assistants or other like devices.
Computer networks 106 comprise local area networks (LANs) and/or wide area networks (WANs). Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets, and the Internet. When utilized in a WAN networking environment, the control server 102 might comprise a modem or other means for establishing communications over the WAN, such as the Internet. In a networking environment, program modules or portions thereof might be stored in association with the control server 102, the data store 104, or any of the remote computers 108. For example, various application programs may reside on the memory associated with any one or more of the remote computers 108. It will be appreciated by those of ordinary skill in the art that the network connections shown are exemplary and other means of establishing a communications link between the computers (e.g., control server 102 and remote computers 108) might be utilized.
In operation, an organization might enter commands and information into the control server 102 or convey the commands and information to the control server 102 via one or more of the remote computers 108 through input devices, such as a keyboard, a microphone (e.g., voice inputs), a touch screen, a pointing device (commonly referred to as a mouse), a trackball, or a touch pad. Other input devices comprise satellite dishes, scanners, or the like. Commands and information might also be sent directly from a remote healthcare device to the control server 102. In addition to a monitor, the control server 102 and/or remote computers 108 might comprise other peripheral output devices, such as speakers and a printer.
Although many other internal components of the control server 102 and the remote computers 108 are not shown, such components and their interconnection are well known. Accordingly, additional details concerning the internal construction of the control server 102 and the remote computers 108 are not further disclosed herein.
Turning now to
The computing system 200 includes a processor 210, an extraction component 212, a displaying component 214, an analyzing component 216, a generating component 218, a calculating component 220, and an assigning component 222 within a medication adherence pattern service 204. The computing system 200 may also include one or more end-user display devices 206 useable to view the information provided by the medication adherence pattern service 204 and an input device 202 to receive selections and/or inputs from a user. The medication adherence pattern service 204 receives information from one or more prescription data sources 208.
In some embodiments, one or more of the illustrated components/modules may be implemented as stand-alone applications. In other embodiments, one or more of the illustrated components/modules may be integrated directly into the operating system of the medication adherence pattern service. The components/modules described are exemplary in nature and in number and should not be construed as limiting. Any number of components/modules may be employed to achieve the desired functionality within the scope of embodiments hereof. Further, components/modules may be located on any number of servers.
The input device 202 functions to receive inputs from a user. The input device 202 may be a keyboard, a microphone, a touch screen, a mouse, a trackball, or a touch pad. The input device 202 is used by a user to select a patient in order to see that patient's medication information. The processor 210 within the medication adherence pattern service 204 receives the selection of the patient.
In response to the selection of a patient, the extraction component 212 is configured to extract medication or prescription data from various prescription data sources 208. The prescription data sources 208 may include electronic medical records, outpatient pharmacy records, long-term care facility records, e-prescriptions, insurance claims, prescription benefit manager records, and the like. The medication data may include drug names, strength of prescriptions, dosages, routes of administration, fill dates, and/or days' supply of each drug.
The generating component 218 is configured to use the medication information extracted from the prescription data sources 208 to construct or generate a prescription refill history for the selected patient. The prescription order and refill history includes timelines representing prescription fill events for each medication the patient is prescribed. The prescription refill history may be updated in real time in response to udpates in the patient's prescription information.
Each time the patient fills a prescription, the fill or refill event is depicted with a solid circle 312. For example, for “FILL 1” 310, the fill occurs on day 30. Each circle is followed by a line 314 extending laterally from the circle in the direction of the timeline 302. For “FILL 1” 310, the line 314 extends until day 60. This line 314 represents the duration of the prescription. This particular medication has a duration of 30 days.
“FILL 2” 316 shows a dotted circle 318, which represents when a refill was filled early. Here, the medication was filled a few days before the next date that the patient would need additional medication. The fill date is then adjusted on the chart to show a solid circle 312 at day 60, indicating the time at which the patient would begin taking the medication from that refill.
There is a gap between “FILL 2” 316 and “FILL 3” 320, indicating that the patient was not taking the medication during that time because the patient did not refill the medication again until after day 120. Another gap in time is shown between “FILL 3” 320 and “FILL 4” 322.
“FILL 4” 322 has a truncated line ending in an “X” 324 to indicate that the amount in the prescription would have lasted longer than the end of the study. The overall display 300 of the patient's prescription refill history provides information that can be used to analyze the patient's medication adherence patterns.
A patient's prescription refill history may be represented in a number of ways. The display of
Returning to
The assigning component 222 is configured to assign a base adherence pattern label to the patient. The base adherence pattern labels may be “High,” “Moderate,” “Low,” or “Mixed” depending on the patient's medication adherence score. The assigning component 222 also assigns one or more add-on adherence pattern labels to each of the medications the patient is prescribed. The add-on adherence pattern labels include outlier, end gap, sync gap, and overpossession. The add-on adherence pattern labels may indicate to a clinician that the patient is at risk for medication non-adherence.
The generating component 218 is configured to then generate a display of the patient's medication adherence patterns in the form of a patient medication graphic. A patient medication graphic may include a list of under-utilized medications, a list of over-utilized medications, an overall adherence rating for the patient, a percentage of days covered table for eligible medications, a percentage of days covered table for adherence by disease state, a list of potential and/or previous recognized barriers to adherence for the patient, and a list of intervention strategies for the patient. The patient medication graphic may display the medications prescribed to the patient in groups organized by class of medication.
The analyzing component 216 analyzes the patient's medication adherence patterns and the patient's electronic medical record (EMR) to identify barriers to medication adherence for patients at risk for non-adherence. Barriers to adherence may include, for example, language barriers, forgetting to refill prescriptions, cost of medication, side effects of medication, and lack of transportation to a pharmacy. Based on the identified barriers, then the generating component 218 automatically generates one or more intervention strategies to address the barriers to medication adherence. These intervention strategies may be displayed in the medication adherence graphic. A clinician may view the medication adherence patterns, barriers to adherence, and recommended intervention strategies to formulate a course of treatment for the patient in order to address known or potential medication non-adherence.
The displays generated by the computer 204 are displayed on a display device 206. The end-user computing device may include a display screen. Embodiments are not intended to be limited to visual display but rather may also include audio presentation, combined audio/visual presentation, and the like. The end-user computing device may be any type of display device suitable for presenting a graphical user interface. Such computing devices may include, without limitation, a computer, such as, for example, any of the remote computers 108 described above with reference to
In the first example medication adherence graphic 400 of
A timeline 410 in days is presented at the bottom of the graphic 400. As was described above with respect to
The medication adherence graphic 400 is labeled with a base adherence pattern label 420 and three add-on adherence pattern labels 418. The pattern labels are assigned to the medication adherence graphic 400 by an assigning component, such as the assigning component 222 of
An outlier is any drug that falls within a PDC range two or more quarters away from the patient's base adherence pattern label 420. Because Pantaprazole is in Q1 408 and the patient's base adherence pattern label 420 is “High,” the “outlier” add-on adherence pattern label 418 applies.
A “sync gap” add-on adherence pattern label 418 applies whenever there are three or more medications prescribed to a patient that are filled with synchronized gaps. Here, Atenolol, Furosemide, Simvastatin, Citalopram, and Olmesartan were all refilled a few days after the supply of the first fill ran out. This is indicated with a space between the lines and the next circle on the timeline. The spaces occur all at the same time, indicating that this pattern is patient-specific, rather than medication specific.
An “end gap” add-on adherence pattern label 418 applies whenever there is no coverage for a medication within 30 days of the end date. In short, this add-on adherence pattern label 418 indicates that the patient has discontinued use of a drug prematurely.
An “overpossession” add-on adherence pattern label (not shown) applies when a patient has possession of more of a medication than has been prescribed.
The example in
The add-on adherence pattern labels 418 “sync gap” and “end gap” have been assigned to this patient's medications. The “sync gap” add-on adherence pattern label 418 applies to Atenolol, Amlodiprine, Lisonopril, Topiramate, and Celecoxib in Q3 404. As with the medications in
A third exemplary medication adherence graphic 424 is shown in
The list of under-utilized medications 502 is included to highlight medications that the patient is prescribed which the patient has not filled often enough. Conversely, the list of over-utilized medications 504 is included to highlight medications that the patient is filling too often, resulting in the patient having a greater supply than is prescribed. By highlighting these medications, a physician can quickly identify the medications that are not being taken properly by the patient and determine if interventional measures are necessary.
The overall medication adherence score 506 is based on the PDC for each medication the patient is prescribed. The overall medication adherence score 506 may be calculated by a calculating component, such as the calculating component 220 of
The eligible medications PDC 508 shows the percentage of days covered for a particular medication. In the example of
The display of barriers to adherence 512 may include a list of reasons that a patient is not taking prescribed medications properly. This list may include barriers that are known to the physician or potential barriers identified by the patterns of the patient's medication adherence. In addition, a display of intervention strategies 514 may be provided. The intervention strategies are automatically generated in response to the list of barriers to adherence. The intervention strategies may be generated by a generating component, such as the generating component 218 of
The GUI 500 also includes a display of the patient's refill history 516. This includes a list of the medications 518 prescribed to the patient. These medications may be displayed in order of level of adherence 520. Medication adherence patterns 524 for the patient are displayed in reference to a timeline 522. The medication adherence patterns 524 may be the same or similar to those described in
The prescription data is then compiled into a medication refill history at a step 608. The medication refill history may be generated with a generating component, such as generating component 218 of
The analysis of the patient's medication refill history may indicate that a patient has not refilled a prescription. If the patient fails to fill or refill a prescription within a set period of time, an alert may be communicated to a clinician. For example, an alert may be sent to a physician if a patient has not refilled a prescription within 10 days of the previous prescription running out.
A medication adherence graphic representing the medication adherence patterns for the patient is automatically generated at a step 612. The medication adherence graphic may be generated by a generating component, such as the generating component 218 of
A prescription refill history based on the patient's medical information is assembled at a step 706. The prescription refill history may be assembled with a generating component, such as generating component 218 of
At a step 710 a display of the prescription refill history is generated, indicating one or more patterns of medication adherence for the patient. The display of the prescription refill history may be generated by a generating component, such as the generating component 218 of
The patterns of medication adherence are analyzed to identify barriers to adherence at a step 712. The patterns of medication adherence may be analyzed by an analyzing component, such as the analyzing component 216 of
An alert may be generated when the patient's medication adherence patterns indicate that the patient has failed to fill a prescription within a set period of time. This alert is communicated to a clinician. The clinician may then employ clinical interventions to ensure that the patient is properly taking medications as prescribed.
The present invention has been described in relation to particular embodiments, which are intended in all respects to be illustrative rather than restrictive. Further, the present invention is not limited to these embodiments, but variations and modifications may be made without departing from the scope of the present invention.
