METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR DETERMINING A NARCOTICS USE INDICATOR
A method, system, and computer program product for determining a controlled substance use indicator to enable a physician, or other prescriber, to quickly review a numerical score that reflects a patient's past drug use and is indicative of proper, or improper, future drug use. This score analyzes many aspects of a patient's past activities to determine multiple individual indicator values that may be selectively weighted to create a final controlled substance use indicator. Such individual indicator values may include a usage related indicator factoring in the patient's past drug use, particularly the type of controlled substances used; an instruction related indicator that may consider the patient's past use of prescribers, quantity of prescriptions, or the number of open prescriptions from different prescribers; a dispensing related indicator that examines a patient's use of pharmacies, in filling prescriptions; or even an auxiliary indicator that may reflect the patient's number of active prescriptions.
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This application is a continuation application of U.S. application Ser. No. 13/234,777, filed Sep. 16, 2011, the disclosure of which is hereby incorporated herein by reference in its entirety for all purposes.
TECHNICAL FIELDThe present invention relates to predicting proper narcotic usage; particularly, to a method and system for creating numerous controlled substance use indicators to predict the likelihood of a patient correctly using a prescription drug of interest.
BACKGROUNDPrescription drug abuse is one of the leading forms of drug abuse in the US. The types of drugs most commonly abused today are narcotics, sedatives, and stimulants. There are other categories of drugs that can be abused besides these three types, one such example being anabolic steroids, and collectively these drugs are classified as controlled substances by the DEA. Narcotics have risen to the top of all controlled substances in terms of the number of people who abuse them. Approximately 3% of 12 year old children in the US admit to using Vicodin in the previous year, while about 15% of 18-25 year old men and women admit to the same. It is estimated that approximately 15 million people in the US abuse prescription drugs. Emergency Departments have seen a 111% increase in the number of visits from people who are seeking narcotics for their addiction. Prescription drug abuse is the number one drug abuse problem in the US.
Healthcare entities have to deal with this problem every day (pharmacists, hospitals, providers), as do law enforcement officials and educators. One of the tools physicians, physician assistants, pharmacists, and law enforcement can use is a state based Prescription Monitoring Program, or PMP. One such example is available at ohioPMP.org. All 50 states now have, or are developing, these programs and they are usually funded at the Federal level. These programs require that pharmacists and providers who dispense medications directly report every narcotic distribution to the state PMP. The state PMP maintains a database of these “transactions.” Approved providers can log into the state PMP website and retrieve a patient's narcotic use information in PDF format. This document may be 1-10 pages long and annotates very specific details about prescription usage (who, where, when, what, how much, when written, when filled, new or refill, etc.). Presumably, a provider, such as a physician, physician's assistant, or pharmacist would utilize this site whenever they were concerned about the potential for prescription drug abuse. However, providers use this service at a relatively low rate because it is a somewhat arduous process to navigate to the site, login, enter demographic data, wait for the report search, download the PDF and then read all of the data. Ohio reports that only 17% of prescribers in the state have even applied for access to the PMP and fewer than that use the system regularly.
SUMMARYIn an embodiment, a computer-implemented method for determining the likelihood of proper prescription drug use by a patient comprises obtaining a record from a prescription database, the record indicative of a plurality of prescriptions corresponding to the patient, and generating, with one or more computer processors, a usage related indicator by comparing at least one of a prescription drug type corresponding to two or more of the plurality of prescriptions or a prescription drug quantity corresponding to the two or more of the plurality of prescriptions with a plurality of general population prescription drug use data. The method further comprises generating, with the one or more computer processors, an instruction related indicator by comparing a prescriber corresponding to at least one of the plurality of prescriptions to a plurality of general population prescription drug instruction data, and combining, with the one or more computer processors, the usage related indicator and the instruction related indicator to produce a prescription drug use indicator for display on a visual medium.
In another embodiment, a computer-implemented method for determining the likelihood of proper prescription drug use by a patient comprises obtaining a record from a prescription database on a server, the record indicative of a plurality of prescriptions corresponding to the patient, a morphine equivalents unit percentile for a give morphine equivalents unit period by comparing at least one of a narcotic type of two or more of the plurality of prescriptions or a narcotic quantity of two or more of the plurality of prescriptions with a plurality of general population prescription drug use data. The method further comprises generating, with the one or more computer processors, an instruction related indicator including: a) identifying a potential prescription overlap situation of at least two prescribers during a prescription overlap period to produce a prescription overlap percentile; and b) creating of a prescriber indicator by comparing a prescriber quantity with the plurality of general population prescription drug use data to determine a prescriber percentile for a given prescriber period. Still further the method comprises combining, with the one or more computer processors, the prescription overlap percentile, the prescriber percentile, and the morphine equivalents unit percentile to produce a narcotics use indicator for display on a visual medium.
Without limiting the scope of the present method, system, and program, referring now to the drawings and figures:
These drawings are provided to assist in the understanding of exemplary embodiments as described in more detail below and should not be construed as unduly limiting. In particular, the relative spacing, positioning, sizing and dimensions of the various elements illustrated in the drawings are not drawn to scale and may have been exaggerated, reduced or otherwise modified for the purpose of improved clarity. Those of ordinary skill in the art will also appreciate that a range of alternative configurations have been omitted simply to improve the clarity and reduce the number of drawings.
DETAILED DESCRIPTIONThe claimed method, system, and computer program product for determining a controlled substance use indicator enables a significant advance in the state of the art. Of note, when the controlled substances of interest are narcotics, the result is a narcotics use indicator. Likewise, when sedatives or stimulants are the focus they result in a sedative use indicator and a stimulant use indicator.
As previously touched upon, a prescription database (6000) may reside on a state PMP server, however one skilled in the art will appreciate that the prescription database (6000) described herein is not limited to a statewide system or a federal system, as it may be a hospital specific prescription database, a commercial prescription database, or community specific prescription database (6000). Similarly, the prescription database (6000) need not reside on a server but rather may reside on a local memory device in a standalone manner, and further, in anticipation of advances in health care IT infrastructure, the prescription database (6000) may be created for an individual patient broadly electronically querying a network of health care providers and aggregating the collected data, which may be completed in virtually real-time. Regardless of the scope, location, or creation of the prescription database (6000), it contains at least one of record (6100) indicative of the prescription drug use by the patient. One illustrative record (6100) is seen in
The record (6100) may also contain data pertaining to the prescriptions that have been filled for a particular patient, whether they are for narcotics or other controlled substances. Therefore, the record (6100) may contain data about a prescribed narcotic such as a narcotic type (6125N), a narcotic strength (6130N), a narcotic form (6135N), and a narcotic quantity (6140N). The record (6100) may contain similar information regarding other prescribed controlled substances such as a controlled substance type (6125C), a controlled substance strength (6130C), a controlled substance form (6135C), and a controlled substance quantity (6140C). Some controlled substance types potentiate each other and become more dangerous when taken together. Two such examples are narcotics and benzodiazepines. For example, the act of consuming a narcotic like demerol can become more dangerous by combining it with a benzodiazepine such as Lorazepam. Thus, in these examples the element numbers for narcotics end with the letter “N” and those for other related controlled substances end in the letter “C”, while sharing the same numerical references. These are simply examples of the data that may be contained within a record (6100) and are not all required, nor are these the only types of data that may reside in a record (6100).
The present method, system, and computer program product retrieve patient specific data from a record (6100) and transforms the data into at least one indicator by comparing the patient specific data with a plurality of general population prescription drug use data. The indicator, or indicators, are then transformed into a controlled substance use indicator (10) via the application of at least one adjustment factor. A diagram of one embodiment of the procedure is seen in
In one embodiment patient specific data including at least a prescriber (6145), a distributor (6155), a narcotic type (6125N), a narcotic strength (6130N), and a narcotic quantity (6140N) is retrieved from the record (6100). Next, at least one prescription drug use processor receives this data and transforms it into at least two indicators; namely, a usage related indicator (1000) and an instruction related indicator (2000). The usage related indicator (1000) is created by comparing at least the patient information concerning the narcotic type (6125N), the narcotic strength (6130N), and the narcotic quantity (6140N) with a plurality of general population prescription drug use data; while the instruction related indicator (2000) is created by comparing at least the patient information concerning the prescriber (6145) with the plurality of general population prescription drug use data.
The act of comparing patient specific data with the plurality of general population prescription drug use data can mean a number of things, as will be explained in greater detail later. In the big picture the comparison simply results in at least an indication of where the patient data ranks when compared to similar data that is representative of a larger population of patients. For example, one embodiment may simply identify whether the patient data is in a below normal range, a normal range, or an above normal range when compared to a larger population of patients. Alternatively, another embodiment may determine a percentile ranking of the patient data compared to the larger population of patients.
Finally, at least one prescription drug use processor applies an adjustment factor (5000) to at least one of the usage related indicator (1000) and the instruction related indicator (2000) to create an adjusted indicator, and transforms the adjusted indicator into a controlled substance use indicator (10) to display on a visual media. The controlled substance use indicator (10) is created within 5-10 seconds of the request.
The embodiment above utilized only a usage related indicator (1000) and an instruction related indicator (2000). However, an example will be explained with respect to
The upper table in
The upper table in
With reference again to
Another possible usage related indicator (1000) is an associated controlled substance unit indicator (1300). The associated controlled substance unit indicator (1300) is created in part by comparing the associated controlled substance quantity (6140C) with the plurality of general population prescription drug use data to determine a controlled substance unit percentile (1340) for a given controlled substance unit period (1310). Thus, in the upper table of
The upper table in
Another possible instruction related indicator (2000) is a prescriber indicator (2200). The creation of a prescriber indicator (2200) is created in part by comparing a prescriber quantity (2220) with the plurality of general population prescription drug use data to determine a prescriber percentile (2240) for a given prescriber period (2210). Thus, in the upper table of
The upper table in
In addition to the usage related indicator (1000) and the instruction related indicator (2000), the method may incorporate a dispensing related indicator (3000). The dispensing related indicator (3000) is created by comparing at least the patient information concerning the distributor (6155) with the plurality of general population prescription drug use data, and in this embodiment the adjustment factor (5000) is then applied to at least one of the usage related indicator (1000), the instruction related indicator (2000), and the dispensing related indicator (3000).
In one particular embodiment the dispensing related indicator (3000) is a distribution source indicator (3100). The creation of a distribution source indicator (3100) is created in part by comparing a distribution source quantity (3120) with the plurality of general population prescription drug use data to determine a distribution source percentile (3140) for a given distribution source period (3110). Thus, in the upper table of
The upper table in
Now that the first data column associated with the five rows of data in the upper tables of
With specific reference to the embodiment of
Referring now to
While the example above focused on a single period of time,
A benefit of incorporating multiple periods is that because all the periods may have the same start date, i.e. the reference date in
In another embodiment any of the adjustment factors may be automatically adjusted if preset criteria are met concerning data that highly correlates with improper prescription drug use. For example, as previously discussed with respect to
As seen in
Further, as seen in
As seen in
As seen in
Even further, another embodiment includes an auxiliary indicator (4000) that is a controlled substance rate of change indicator (4200). The controlled substance rate of change indicator (4200) is created in part by comparing how the patient's narcotics use indicator (10) has changed over a period, or periods, of time to the rate of change associated with the plurality of general population prescription drug use data. For example, a request for a narcotics use indicator (10) may result in the determination of a first narcotics use indicator at a fixed time interval prior to the request date, and then the determination of a second narcotics risk indicator at a rate of change period (4210) prior to the fixed time interval. The difference between the first and second narcotics risk indicators, referred to as a controlled substance variation (4220), may then be used to adjust the presently requested narcotics use indicator if a threshold controlled substance variation (4220) is exceeded. The controlled substance variation (4220) may be compared with the plurality of general population prescription drug use data to determine a rate of change percentile (4240) for the given rate of change period (4210). Thus, one with skill in the art will recognize that this controlled substance rate of change indicator (4200) is yet another indicator that may be found in the upper tables of
Throughout this document there are multiple references to a step of comparing a quantity, whether it is the 1120, 1220, 1320, 2120, 2220, 2320, 3120, 3220, 4120, or 4220 quantity, “with the plurality of general population prescription drug use data” to determine an indicator, whether it be a usage related indicator (1000), an instruction related indicator (2000), or a dispensing related indicator (3000). In some of the many disclosed embodiments the determination of an indicator includes a determination of whether the quantity is within an acceptable range or an unacceptable range, however other embodiments determine approximate percentile rankings of the quantity compared to the general population data, such as the 1140, 1240, 1340, 2140, 2240, 2340, 3140, 3240, 4140, or 4240 percentiles.
The general population prescription drug use data referenced is data associated with at least 1000 patients over the period of interest. In one embodiment this general population data is present in the database (6000) and is extracted for use in arriving at the indicators, or in some embodiments the percentile(s). The general population data need not be extracted each time patient specific data is retrieved from the database (6000); rather the general population data may be extracted after extended intervals, which may be months or even years. The general population prescription drug use data may be from a statewide or federal prescription database, a hospital specific prescription database, a commercial prescription database, or community specific prescription database. Thus, in yet another embodiment the general population prescription drug use data referenced is data associated with at least 1,000,000 patients over the period of interest; while yet a further embodiment, such as data used in generating
Therefore, the act of comparing a quantity “with the plurality of general population prescription drug use data” to determine an indicator may include the step of previously acquiring the general population prescription drug use data, processing the data, converting the data into a quickly accessible electronic format, and storing the converted data on hardware for use in determining the final narcotics risk indicator (10) in less than 5 seconds, whether the general population prescription drug data is local or on a hardware device on the other side of the planet. Thus, in one embodiment a local prescription drug use processor securely retrieves and stores into memory patient specific data from a remote database (6000), the local prescription drug use processor securely retrieves and stores into memory previously compiled and transformed data representative of the general populations prescription drug use, the local prescription drug use processor retrieves portions of this stored data to form and store at least a usage related indicator (1000) and an instruction related indicator (2000), the local prescription drug use processor applies an adjustment factor (5000) to at least one of usage and instruction related indicators (1000, 2000) and transforms them into a numerical narcotics use indicator (10), and the local prescription drug use processor formats and transmits the narcotics use indicator (10) to display on a visual media. Further, in light of confidential patient data security, the local prescription drug use processor may then clear the patient specific data from the local memory, as well as leave a timestamp within the remote database (6000) to serve as an indicator of when a patient's data was accessed. The prescription drug use processor may further securely transmit the narcotics use indicator (10) back to the database (6000) for storage and retrieval during subsequent data requests in determining updated narcotics use indicators (10). Thus, a system for carrying out the determination of a narcotics use indicator (10) may consist of several securely connected pieces of hardware communicating with the specially programmed prescription drug use processor to determine the narcotics use indicator (10). As the local prescription drug use processor retrieves the patient specific data from the database (6000), it may create a local patient-specific database for temporarily storing and processing data. The local patient-specific database is cleared of patient specific data upon the creation of the narcotics use indicator (10) and any associated reports that are simultaneously created.
The analysis of large quantities of data is well known in the field of statistics to identify acceptable ranges, unacceptable ranges, and percentile rankings, and therefore will not be reviewed in detail. However, one of many embodiments will be discussed for illustrative purposes. For instance,
The prescription drug use processor is a specially programmed computer device such as a personal computer, a portable phone, a multimedia reproduction terminal, a tablet, a PDA (Personal Digital Assistant), or a dedicated portable terminal that can perform the secure retrieval and processing of input, output, storage and the like of information. It goes without saying that such a program can be distributed through a recording medium such as a CD-ROM and a transmission medium such as the Internet. Further, the present invention may be a computer-readable recording medium such as a flexible disk, a hard disk, a CD-ROM, an MO, a DVD, a DVD-ROM, a DVD-RAM, a BD (Blu-ray Disc), flash drives, thumb drives, and a semiconductor memory that records the computer program. Thus, the distributed program may be used to program a computer to create a prescription drug processor thereby becoming a special purpose computer to securely perform particular functions pursuant to instructions from program software.
Numerous alterations, modifications, and variations of the preferred embodiments disclosed herein will be apparent to those skilled in the art and they are all anticipated and contemplated to be within the spirit and scope of this application. For example, although specific embodiments have been described in detail, those with skill in the art will understand that the preceding embodiments and variations can be modified to incorporate various types of substitute and or additional or alternative steps, procedures, and the order for such steps and procedures. Accordingly, even though only few variations of the present methodology and system are described herein, it is to be understood that the practice of such additional modifications and variations and the equivalents thereof, are within the spirit and scope of this application. The corresponding structures, materials, acts, and equivalents of all methods, means, and step plus function elements in the claims below are intended to include any structure, material, or acts for performing the functions in combination with other claimed elements as specifically claimed.
Claims
1. A computer-implemented method for determining the likelihood of proper prescription drug use by a patient, the method comprising:
- obtaining a record from a prescription database, the record indicative of a plurality of prescriptions corresponding to the patient;
- generating, with one or more computer processors, a usage related indicator by comparing at least one of a prescription drug type corresponding to two or more of the plurality of prescriptions or a prescription drug quantity corresponding to the two or more of the plurality of prescriptions with a plurality of general population prescription drug use data;
- generating, with the one or more computer processors, an instruction related indicator by comparing a prescriber corresponding to at least one of the plurality of prescriptions to a plurality of general population prescription drug instruction data; and
- combining, with the one or more computer processors, the usage related indicator and the instruction related indicator to produce a prescription drug use indicator for display on a visual medium.
2. The method of claim 1,
- wherein the plurality of prescriptions includes at least one prescription for a narcotic;
- wherein, for the at least one prescription for a narcotic, the prescription drug type is a narcotic type and the prescription drug quantity is a narcotic quantity; and
- wherein generating the usage related indicator further includes the creation of a morphine equivalents unit indicator by transforming the narcotic type, a narcotic strength, and the narcotic quantity into a morphine equivalents unit quantity, and comparing the morphine equivalents unit quantity with the plurality of general population prescription drug use data to determine a morphine equivalents unit percentile for a given morphine equivalents unit period.
3. The method of claim 2, wherein combining the usage related indicator and the instruction related indicator includes applying a usage weighting factor that is a narcotic usage weighting factor.
4. The method of claim 3, wherein the morphine equivalents unit percentile is determined for at least two morphine equivalents unit periods and the narcotic usage weighting factor is applied to a multi period morphine equivalents unit percentile that is the average of the morphine equivalents unit percentiles.
5. The method of claim 1,
- wherein the plurality of prescriptions includes at least one prescription for a controlled substance;
- wherein, for the at least one prescription for a controlled substance, the prescription drug type is a controlled substance type and the prescription drug quantity is a controlled substance quantity; and
- wherein generating the usage related indicator further includes the creation of a controlled substance unit indicator by comparing the controlled substance quantity with the plurality of general population prescription drug use data to determine a controlled substance unit percentile for a given controlled substance unit period.
6. The method of claim 5, wherein combining the usage related indicator and the instruction related indicator includes applying a usage weighting factor that is a controlled substance usage weighting factor.
7. The method of claim 6, wherein the controlled substance unit percentile is determined for at least two controlled substance unit periods and the controlled substance usage weighting factor is applied to a multi period controlled substance unit percentile that is the average of the controlled substance unit percentiles.
8. The method of claim 1, wherein the record contains a prescription period and wherein generating the instruction related indicator includes identifying a potential prescription overlap situation when the record includes at least two prescribers during a prescription overlap period, and creating of a prescription overlap indicator by determining a prescription overlap quantity that is the total number of days that the prescription period of the each prescriber coincide and comparing the prescription overlap quantity with the plurality of general population prescription drug use data to determine a prescription overlap percentile.
9. The method of claim 8, wherein combining the usage related indicator and the instruction related indicator includes applying an instruction weighting factor that is an overlap weighting factor.
10. The method of claim 9, wherein the prescription overlap percentile is determined for at least two prescription overlap periods and the overlap weighting factor is applied to a multi period prescription overlap percentile that is the average of the prescription overlap percentiles.
11. The method of claim 1, wherein generating the instruction related indicator includes the creation of a prescriber indicator that indicating a ranking of a prescriber quantity compared to the plurality of general population prescription drug use data to determine a prescriber percentile for a given prescriber period.
12. The method of claim 11, wherein combining the usage related indicator and the instruction related indicator includes applying an instruction weighting factor that is a prescriber weighting factor.
13. The method of claim 12, wherein the prescriber percentile is determined for at least two prescriber periods and the prescriber weighting factor is applied to a multiperiod prescriber percentile that is the average of the prescriber percentiles.
14. The method of claim 1, wherein each of the plurality of prescriptions includes a distributor; the method further comprising:
- generating a dispensing related indicator that indicates a ranking of the distributor compared to the plurality of general population prescription drug use data, and wherein a weighting factor is applied to at least one of the usage related indicator, the instruction related indicator, and the dispensing related indicator.
15. The method of claim 14, wherein generating the dispensing related indicator further includes the creation of a distribution source indicator that indicates a ranking of a distribution source quantity compared to the plurality of general population prescription drug use data to determine a distribution source percentile for a given distribution source period.
16. The method of claim 15, wherein combining the usage related indicator and the instruction related indicator includes applying a dispensing weighting factor.
17. The method of claim 16, wherein the distribution source percentile is determined for at least two distribution source periods and the dispensing weighting factor is applied to a multi period distribution source percentile that is the average of the distribution source percentiles.
18. The method of claim 1, wherein each of the plurality of prescriptions includes a prescription drug type.
19. The method of claim 1, wherein the usage related quantity is based on at least the prescription drug type corresponding to two or more of the plurality of prescriptions, the prescription drug strength corresponding to two or more of the plurality of prescriptions, and the prescription drug quantity corresponding to the two or more of the plurality of prescriptions.
20. The method of claim 1, wherein combining the usage related indicator and the instruction related indicator to determine the prescription drug use indicator includes numerically weighting at least one of the usage related indicator or the instruction related indicator.
21. A computer-implemented method for determining the likelihood of proper prescription drug use by a patient, the method comprising:
- obtaining a record from a prescription database on a server, the record indicative of a plurality of prescriptions corresponding to the patient;
- generating, with one or more computer processors, a morphine equivalents unit percentile for a give morphine equivalents unit period by comparing at least one of a narcotic type of two or more of the plurality of prescriptions or a narcotic quantity of two or more of the plurality of prescriptions with a plurality of general population prescription drug use data;
- generating, with the one or more computer processors, an instruction related indicator including: a) identifying a potential prescription overlap situation of at least two prescribers during a prescription overlap period to produce a prescription overlap percentile; and b) creating of a prescriber indicator by comparing a prescriber quantity with the plurality of general population prescription drug use data to determine a prescriber percentile for a given prescriber period;
- combining, with the one or more computer processors, the prescription overlap percentile, the prescriber percentile, and the morphine equivalents unit percentile to produce a narcotics use indicator for display on a visual medium.
22. The method of claim 18, wherein the narcotics use indicator is at least a two digit number.
23. The method of claim 19, wherein the narcotics use indicator is at least a three digit number, wherein a last digit is an active prescription indicator and represents a number of active prescriptions.
24. The method of claim 18, wherein
- (A) the prescription overlap percentile is determined for at least two prescription overlap periods and the overlap weighting factor is applied to a multi period prescription overlap percentile that is the average of the prescription overlap percentiles,
- (B) the prescriber percentile is determined for at least two prescriber periods and the prescriber weighting factor is applied to a multi period prescriber percentile that is the average of the prescriber percentiles, and
- (C) the morphine equivalents unit percentile is determined for at least two morphine equivalents unit periods and the narcotic usage weighting factor is applied to a multi period morphine equivalents unit percentile that is the average of the morphine equivalents unit percentiles.
25. The method of claim 18, wherein the narcotic usage weighting factor is at least twice the overlap weighting factor, and the narcotic usage weighting factor is at least twice the prescriber weighting factor.
26. The method of claim 21, wherein at least one of the overlap weighting factor, the prescriber weighting factor, or the narcotic usage weighting factor is automatically increased when a preset criteria for at least one of the usage related indicator and the instruction related indicator, is retrieved from the database.
Type: Application
Filed: Feb 24, 2014
Publication Date: Jun 19, 2014
Applicant: NATIONAL ASSOC. OF BOARDS OF PHARMACY (Mount Prospect, IL)
Inventor: James Huizenga (Dayton, OH)
Application Number: 14/188,171
International Classification: G06F 19/00 (20060101);