DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The office action is in response to the claims filed on January 8, 2026 for the application filed January 5, 2023 which claims priority to a provisional application filed on July 13, 2020. Claims 1-5 and 7-21 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5 and 7-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Eligibility Step 1:
Under step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, claims 1-5 and 7-21 are directed towards a method (i.e. a process), which is a statutory category. Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea). In the instant application, the claims are directed towards an abstract idea.
Eligibility Step 2A, Prong One:
Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, independent claim 1 is determined to be directed to an judicial exception because an abstract idea is recited in the claims which fall within the subject matter groupings of abstract ideas. The abstract idea (identified in bold) recited in the claim 1 is identified as:
A method to support an antimicrobial stewardship program (ASP) within a heahthcare setting, the method being implemented by one or more computer processors and comprising:
in response to a first trigger, retrieving, from a plurality of databases, medical data of a plurality of patients triaged by an ASP management system, antibiogram information that indicate resistance of pathogens to a plurality of antibiotics within a healthcare setting, and a plurality of guidelines related to treatment of certain diseases using the plurality of antibiotics, wherein the triaging of the plurality of patients is based on a risk of an antibiotic-related adverse event, wherein the first trigger comprises: a selection of a first patient based on a priority assigned to the first patient among the plurality of patients and at least one of: detection of a new prescription of an antibiotic for the selected first patient or a timer of the ASP management system indicating that a review of the selected first patient's antibiotic use is due;
generating a recommendation for at least one of a prescription of the first antibiotic of the plurality of antibiotics or an order of a first diagnostic test for a first patient of the plurality of patients, the recommendation being generated based on applying one or more rules to the plurality of guidelines, the antibiogram information, and the medical data for the first patient; and
providing, via an interface of the ASP management system accessible by a first member of a treating team, access to the medical data of the first patient, a subset of the antibiogram information relevant to a medical condition of the first patient, and the recommendation to facilitate a first clinical decision by the first member, the first clinical decision including at least one of prescribing a first dosage of the first antibiotic or ordering the first diagnostic test to the first patient.
The identified generating limitation of claim 1 fall within the subject matter grouping of mental processes. Generating recommendations by applying rules to guidelines and based on antibiogram information and medical data for a patient can be performed in the human mind using observations, evaluations, judgements and opinions. If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.
The identified limitations of claim 1 also fall within the subject matter grouping of certain methods of organizing human activities and the sub grouping of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). The claims recite triaging of the plurality of patients is based on a risk of an antibiotic-related adverse event; selecting a first patient based on based on a priority assigned to the first patient among the plurality of patients and at least one of: detection of a new prescription of an antibiotic for the selected first patient or a timer indicating that a review of the selected first patient's antibiotic use is due; and generating a recommendation for at least one of a prescription of the first antibiotic of the plurality of antibiotics or an order of a first diagnostic test for a first patient of the plurality of patients, the recommendation being generated based on applying one or more rules to the plurality of guidelines, the antibiogram information, and the medical data for the first patient, which are human activities performed healthcare professionals and antimicrobial stewardship team members. The details of how the triaging, selecting and recommending are merely rules or instructions a healthcare professionals and antimicrobial stewardship team members should follow as part of their antimicrobial stewardship program. Furthermore, utilizing a selection of a patient and detection of a new prescription or a timer indicating review is due to retrieve data is a human activity, such as the human activity determining when to perform an antimicrobial review of patient data and also a certain activity between a person and a computer, such as selecting a patient in order to retrieve data for the patient.
Accordingly, claim 1 recites an abstract idea under step 2A, prong one.
Eligibility Step 2A, Prong Two:
Under step 2A, prong two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the identified abstract ideas are integrated into a practical application. After evaluation, there is no indication that any additional elements or combination of elements integrate the abstract idea into a practical application, such as through: an additional element that reflects an improvement to the functioning of a computer, or an improvements to any other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element that implements the judicial exception with, or uses the judicial exception in connection with, a particular machine or manufacture that is integral to the claim; an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. As shown below, the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than a recitation of: generally linking the abstract idea to a particular technological environment or field of use; insignificant extra-solution activity to the judicial exception; and/or adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea as evidenced below.
The additional elements recited in claim 1 are identified in italics as:
A method to support an antimicrobial stewardship program (ASP) within a heahthcare setting, the method being implemented by one or more computer processors and comprising:
in response to a first trigger, retrieving, from a plurality of databases, medical data of a plurality of patients triaged by an ASP management system, antibiogram information that indicate resistance of pathogens to a plurality of antibiotics within a healthcare setting, and a plurality of guidelines related to treatment of certain diseases using the plurality of antibiotics, wherein the triaging of the plurality of patients is based on a risk of an antibiotic-related adverse event, wherein the first trigger comprises: a selection of a first patient based on a priority assigned to the first patient among the plurality of patients and at least one of: detection of a new prescription of an antibiotic for the selected first patient or a timer of the ASP management system indicating that a review of the selected first patient's antibiotic use is due;
generating a recommendation for at least one of a prescription of the first antibiotic of the plurality of antibiotics or an order of a first diagnostic test for a first patient of the plurality of patients, the recommendation being generated based on applying one or more rules to the plurality of guidelines, the antibiogram information, and the medical data for the first patient; and
providing, via an interface of the ASP management system accessible by a first member of a treating team, access to the medical data of the first patient, a subset of the antibiogram information relevant to a medical condition of the first patient, and the recommendation to facilitate a first clinical decision by the first member, the first clinical decision including at least one of prescribing a first dosage of the first antibiotic or ordering the first diagnostic test to the first patient.
The additional limitations of “being implemented by one or more computer processors”, “by an ASP management system” and “via an interface of the ASP management system” are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). The computer processors, interface and ASP system are recited at a high level generality and are used in their ordinary capacity to perform the abstract idea. Therefore, these additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or no more than mere instructions to implement an abstract idea or other exception on a computer or no more than merely using a computer as a tool to perform an abstract idea.
The additional limitations of “retrieving…” and “providing…” are determined to be no more than insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g). Retrieving the data and information required for generating the recommendations and providing the retrieved data and recommendations amounts to necessary data gathering and outputting and do not meaningfully limit the claim.
Accordingly, claim 1 does not recite additional elements which integrate the abstract idea into a practical application.
Eligibility Step 2B:
Under step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of “being implemented by one or more computer processors”, “by a ASP management system” and “via an interface of the ASP management system” are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f) and “retrieving…” and “providing…” are determined to be no more than insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g), which do not amount to significantly more than the abstract idea. Evidence that retrieving data and providing data are well-understood, routine and conventional is provided by MPEP §2106.05(d), subsection II.
Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements amounts to an inventive concept.
Dependent Claims:
The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. None of these limitations are deemed to integrate the claims into a practical application or to amount to significantly more than the abstract idea as detailed below:
Regarding claim 2: the claim recites the mental process of generating the recommendation responsive to receiving the second trigger and the additional elements of receiving a trigger, transmitting a query to retrieve information based on the trigger and receiving a second trigger to generate the recommendation are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f) and the insignificant extra-solution activity of to the judicial exception of mere data gathering under MPEP §2106.05(g) which is well-understood, routine and conventional as evidenced by MPEP §2106.05(d), subsection II.
Regarding claim 3: the traversing, identifying, determining, ranking, selecting and generating steps are directed to recite a mental process and the additional limitations of retrieving are determined to be the insignificant extra-solution activity of to the judicial exception of mere data gathering under MPEP §2106.05(g) which is well-understood, routine and conventional as evidenced by MPEP §2106.05(d), subsection II.
Regarding claim 4: enabling real-time or asynchronous communication between members of a team is directed to the abstract idea falling under the subject matter grouping of a certain methods of organizing human activity and the sub grouping of managing personal behavior or relationships or interactions between people. The communication interface, data access interface and providing the interfaces concurrently are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f) and the insignificant extra-solution activity of to the judicial exception of mere data outputting under MPEP §2106.05(g) which is well-understood, routine and conventional as evidenced by MPEP §2106.05(d), subsection II.
Regarding claim 5: enabling sending information to members of a team is directed to the abstract idea falling under the subject matter grouping of a certain methods of organizing human activity and the sub grouping of managing personal behavior or relationships or interactions between people. The communication interface for enabling the sending is determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f).
Regarding claim 7: the receiving and displaying using interfaces are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f) and the insignificant extra-solution activity of to the judicial exception of mere data gathering under MPEP §2106.05(g) which is well-understood, routine and conventional as evidenced by MPEP §2106.05(d), subsection II.
Regarding claim 8: Defining the plurality of databases to be and EMR, MPI, HIE, DICOM, PACS, LIS, RIS, antibiograms or hospital guidelines are determined to be no more than generally linking the use of a judicial exception to a particular technological environment or field of use under MPEP §2106.05(h).
Regarding claim 9: The defined medical data is encompassed by the abstract idea of claim 1 and does not add meaningful limits to the claims.
Regarding claim 10: selecting data based on conditions is determined to be directed to a mental process and displaying the subset of data is determined to be the insignificant extra-solution activity of to the judicial exception of mere data outputting under MPEP §2106.05(g) which is well-understood, routine and conventional as evidenced by MPEP §2106.05(d), subsection II.
Regarding claim 11: the receiving and displaying using interfaces and systems are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f) and the insignificant extra-solution activity of to the judicial exception of mere data gathering under MPEP §2106.05(g) which is well-understood, routine and conventional as evidenced by MPEP §2106.05(d), subsection II.
Regarding claim 12: selecting guidelines based disease state is determined to be directed to a mental process and providing access to the guidelines is determined to be the insignificant extra-solution activity of to the judicial exception of mere data outputting under MPEP §2106.05(g) which is well-understood, routine and conventional as evidenced by MPEP §2106.05(d), subsection II.
Regarding claim 13: The defined recommendations are encompassed by the abstract idea of claim 1 as being directed to a mental process.
Regarding claim 14: The defined information upon which the recommendations are based on are encompassed by the abstract idea of claim 1 as being directed to a mental process.
Regarding claim 15: the determining and ranking are determined to be mental processes and the providing is determined the insignificant extra-solution activity of to the judicial exception of mere data gathering under MPEP §2106.05(g) which is well-understood, routine and conventional as evidenced by MPEP §2106.05(d), subsection II.
Regarding claim 16: The defined recommendations and the defined information upon which the recommendations are based on are encompassed by the abstract idea of claim 1 as being directed to a mental process.
Regarding claim 17: The defined information upon which the recommendations are based on are encompassed by the abstract idea of claim 1 as being directed to a mental process.
Regarding claim 18: The defined information upon which the recommendations are based on are encompassed by the abstract idea of claim 1 as being directed to a mental process.
Regarding claim 19: The defined information upon which the recommendations are based on are encompassed by the abstract idea of claim 1 as being directed to a mental process and the editing of rules or guidelines is directed to the abstract idea falling under the subject matter grouping of a certain methods of organizing human activity and the sub grouping of managing personal behavior.
Regarding claim 20: determining a triage ranking of patients based on medical data and antibiogram information is determined to be directed to a mental process and displaying a ranking patient list include retrieved medical data and based on the triage ranking is determined the insignificant extra-solution activity of to the judicial exception of mere data outputting under MPEP §2106.05(g) which is well-understood, routine and conventional as evidenced by MPEP §2106.05(d), subsection II.
Regarding claim 21: determining a triage ranking based on one or more of: availability of the most recent lab test result, lab/drug mismatch, restricted drug prescription, and history of prior interventions is determined to be directed to a mental process and a certain method of organizing human activity.
Therefore, whether taken individually or as an ordered combination, 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 8-10, 13-14, 17 and 19-21 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Kuper et al. (The role of electronic health record and "add-on" clinical decision support systems to enhance antimicrobial stewardship programs) in view of Gupta et al. (U.S. Pub. No. 2021/0287809).
Regarding claim 1, Kuper discloses a method to support an antimicrobial stewardship program (ASP) within a heahthcare setting, the method being implemented by one or more computer processors and comprising (Abstract and pages 505-506, System evaluation):
healthcare setting, and a plurality of guidelines related to treatment of certain diseases using the plurality of antibiotics, wherein the triaging of the plurality of patients is based on a risk of an antibiotic-related adverse event (Page 503, Electronic health record–based systems, EHRs are digital versions of a patient’s record of car. pull in information from the EHR data repository to build reports or alerts, such as prior diagnosis (eg, International Classification of Disease, Tenth Revision, ICD10) codes, procedure codes, and other billing information that may not always be available with an addon CDSS. Page 503, Add-on clinical decision support systems, Add-on CDSSs run in parallel with the EHR and are dependent on the data they can extract from the institution’s native data sources. data flows from the EHR into the add-on CDSS. Page 503, Empiric antimicrobial selection, guidelines embedded in order sets within the EHR can improve initial antimicrobial selection, optimize dosing, and duration of therapy. Page 503, Empiric antimicrobial selection, historical search for previous diagnoses and culture and susceptibility results. Page 504, Syndrome-specific management, facility-specific clinical practice guidelines. Active and passive stewardship interventions that rely on an EHR or CDSS exist for a variety of syndromes, including bacteremia, pneumonia, skin and soft-tissue infections, C. difficile colitis, and intra-abdominal infections. Page 509, table 4, local sensitivities, developed using CLSI antibiogram recommendations, and ID specific guide lines easily available in the system(s) as part of the decision process. Predictive analytics should be built into rules and alerts that includes both patient specific information from previous visits and/or from a population dataset. Page 504, Postprescription antimicrobial review, Another advantage of both EHR and add-on systems is their ability to help reduce the workload and time required to conduct postprescription antimicrobial review. In some hospitals, hundreds of patients may be receiving antimicrobials at any given time, making it difficult to review every patient. EHRs and add-on CDSSs have developed methods to triage patients, based on pre-built and customizable algorithms that identify patients at highest risk for adverse events or inappropriate therapy. Typically, this is accompanied by a corresponding visual cue such as a red flag or a high numerical score that allows any user of the system to quickly determine which patients should be reviewed.),
generating a recommendation for at least one of a prescription of a first antibiotic of the plurality of antibiotics or an order of a first diagnostic test for the first patient of the plurality of patients, the recommendation being generated based on applying one or more rules to the plurality of guidelines, the antibiogram information, and the medical data for the first patient (Page 503, Table 3, Promoting initial appropriate empiric therapy based on suspected infection type through order sets. Selecting appropriate dose, indication, and/ or duration upon order entry. Page 503, Empiric antimicrobial selection, provide readily available tools to guide appropriate empiric antimicrobial prescribing for clinicians. Utilization of guidelines embedded in order sets within the EHR can improve initial antimicrobial selection, optimize dosing, and duration of therapy. Page 504, Empiric antimicrobial selection, add-on CDSS that can take the prescriber through a series of questions about the patient and do a historical search for previous diagnoses and culture and susceptibility results. They provide sophisticated guidance on which antimicrobial is most likely to cover the suspected pathogen causing the infection. Page 504, Diagnostic testing stewardship, The clinical laboratory is another area in which these systems, specifically EHRs, can intersect with the ASP by guiding the appropriate ordering of clinical diagnostic testing via laboratory test order interfaces. Default settings built in the EHR can positively and negatively impact provider test selection. An obvious positive example of the laboratory and ASP collaborating is in the effort to reduce inappropriate testing for C. difficile. The HER can be optimized to guide providers toward the appropriate situations for testing for C. difficile as well as away from inappropriate use of other stool diagnostic assays such as cultures and parasitology studies. Page 509, Table 4, machine learning to prompt appropriate selection of tests and medications. Ability to identify syndrome-specific interventions. Local sensitivities ,developed using CLSI antibiogram recommendations, and ID specific guidelines easily available in the system(s) as part of the decision process. Predictive analytics should be built into rules and alerts that includes both patient specific information from previous visits and/or from a population dataset. Also see page 505, Syndrome-specific management.); and
providing, via an interface of the ASP management system accessible by a first member of a treating team, access to the medical data of the first patient, a subset of the antibiogram information relevant to a medical condition of the first patient, and the recommendation to facilitate a first clinical decision by the first member, the first clinical decision including at least one of prescribing a first dosage of the first antibiotic or ordering the first diagnostic test to the first patient (Page 503, Electronic health record–based systems, EHRs are digital versions of a patient’s record of care. They have the capabilities of an electronic medical record but are accessible to all clinicians involved in a patient’s care and can share information with other areas of the same healthcare system (and ideally across multiple healthcare organizations. Page 505, A key reporting feature imperative to ASPs is the ability to produce antibiograms on request. These could be produced for a selected time, a specific unit or service, or for selected organisms. Some add-on systems and EHR-based systems have this capability. Page 504, Empiric antimicrobial selection, provide sophisticated guidance on which antimicrobial is most likely to cover the suspected pathogen. Page 504, Empiric antimicrobial selection, guiding the appropriate ordering of clinical diagnostic testing via laboratory test order interfaces. Page 509, Table 4, prompt appropriate selection of tests and medications. Systems should have interfaces with “smart” phones and tablets to allow for mobile access and limit need to have access to a desktop computer.).
Kuper further discloses reviewing patients based on a priority assigned to the first patient among the plurality of patients and at least one of: detection of a new prescription of an antibiotic for the selected first patient or a timer of the ASP management system indicating that a review of the selected first patient's antibiotic use is due (Page 504, Postprescription antimicrobial review, discusses performing postprescription antimicrobial review for patients identified as at highest risk for adverse events or inappropriate therapy and for identified antibiotic time-outs (eg, patient on vancomycin at 72 hours with no positive cultures) after empiric antimicrobial prescribing.).
However, Kuper does not appear to explicitly disclose that the retrieving of medical data of a plurality of patients, antibiogram information, and a plurality of guidelines is in response to a first trigger, wherein the first trigger comprises: a selection of a first patient based on a priority assigned to the first patient among the plurality of patients and at least one of: detection of a new prescription of an antibiotic for the selected first patient or a timer of the ASP management system indicating that a review of the selected first patient's antibiotic use is due.
Gupta teaches that it was old and well known in the art of antimicrobial stewardship at the time of the filing to retrieving of medical data of a plurality of patients, antibiogram information, and a plurality of guidelines in response to a first trigger, wherein the first trigger comprises: a selection of a first patient (Gupta, paragraph [0100], The dynamic system 103 may receive the user request 304 and generate one or more requests for insights for a target based on the user request 304. For example, the request for insights for the target may include a request for insights for a particular patient, a particular facility, and/or a particular geography. Paragraph [0101], Based on the received request for insights 306, the data stores 104 and 108 may obtain and/or access data. For example, first and second data stores 104 and 108 may obtain or access patient records; pathogen information; antibiotics information; heatmaps; drug resistance studies, information, and evidence; and the like. Also see paragraph [0055], [0098] and [0103].) to conduct processing, merging, filtering, aggregation, and/or alerting in a time efficient manner while data is added to the database and/or the individual entity requests may be different (Gupta, paragraph [0068]).
Therefore, it would have been obvious to one of ordinary skill in the art of antimicrobial stewardship at the time of the filing to modify method of Kuper such that the retrieving of medical data of a plurality of patients, antibiogram information, and a plurality of guidelines is in response to a first trigger, wherein the first trigger comprises: a selection of a first patient, as taught by Gupta and such that the selection is based on a priority assigned to the first patient among the plurality of patients and at least one of: detection of a new prescription of an antibiotic for the selected first patient or a timer of the ASP management system indicating that a review of the selected first patient's antibiotic use is due, as taught by Kuper, in order to conduct processing, merging, filtering, aggregation, and/or alerting in a time efficient manner while data is added to the database and/or the individual entity requests may be different.
Regarding claim 2, Kuper further discloses method of claim 1, further comprising:
receiving a second trigger to generate a recommendation, the second trigger comprising at least one of: receiving an indication that the medical data of the first patient are displayed in the interface, receiving a second command via the interface to generate the recommendation, or receiving a network message from the plurality of databases indicating that new medical data of first patient is available (Page 506, Syndrome-specific management, Electronically generated alerts can facilitate real-time notification of potential interventions (eg, patients with newly positive blood cultures). Page 504, Postprescription antimicrobial review, These systems excel at creating alerts to identify patients that require antimicrobial modifications. Alerts can be created for various care scenarios, but common examples include notifications of prolonged therapy past a certain time point (eg, patient on vancomycin at 72 hours with no positive cultures), alerting when positive cultures and/or rapid diagnostic tests are available and de-escalation of antimicrobial is possible, alerting to MDROs, and alerting for changing renal function requiring antimicrobial dose adjustments.); and
generating the recommendation responsive to receiving the second trigger (Page 506, Syndrome-specific management, Electronically generated alerts can facilitate real-time notification of potential interventions (eg, patients with newly positive blood cultures).
Kuper does not appear to explicitly disclose, but Gupta teaches that it was old and well known in the art of antimicrobial stewardship at the time of the filing: responsive to receiving the first trigger, transmitting a query including identifiers of the plurality of patients to the plurality of databases to retrieve the medical data of the plurality of patients (Gupta, paragraph [0100], The dynamic system 103 may receive the user request 304 and generate one or more requests for insights for a target based on the user request 304. For example, the request for insights for the target may include a request for insights for a particular patient. Paragraph [0101], Based on the received request for insights 306, the data stores 104 and 108 may obtain and/or access data. For example, first and second data stores 104 and 108 may obtain or access patient records; pathogen information; antibiotics information; heatmaps; drug resistance studies, information, and evidence; and the like. The data stores 104 and 108 may provide the obtained or accessed data to the dynamic system 103 at 308. Also see paragraph [0082].) to conduct processing, merging, filtering, aggregation, and/or alerting in a time efficient manner while data is added to the database and/or the individual entity requests may be different (Gupta, paragraph [0068]).
Therefore, it would have been obvious to one of ordinary skill in the art of antimicrobial stewardship at the time of the filing to modify method of Kuper to include: responsive to receiving the first trigger, transmitting a query including identifiers of the plurality of patients to the plurality of databases to retrieve the medical data of the plurality of patients, as taught by Gupta, in order to conduct processing, merging, filtering, aggregation, and/or alerting in a time efficient manner while data is added to the database and/or the individual entity requests may be different.
Regarding claim 8, Kuper further discloses the method of claim 1, wherein the plurality of databases comprise at least one of: an electronic medical record (EMR) database, a master patient index (MPI) services database, a health information exchange (HIE) server, a storage that stores image files in the format of Digital Imaging and Communications in Medicine (DICOM), a picture archiving and communication system (PACS), a laboratory information system (LIS) including genomic data, a radiology information system (RIS), an antibiogram database, or a hospital guideline database (Page 503, Add-on clinical decision support systems, that the data flows from the EHR into the add-on CDSS. data extracted from other primary health information systems (eg, laboratory, pharmacy, etc). Page 506, Unintended consequences, data transmission between the EHR/LIS and an add-on CDSS.).
Regarding claim 9, Kuper further discloses the method of claim 1, wherein the medical data include at least one of: a medical history, a body temperature, a blood pressure, or a lab result at different time points (Page 503, Electronic health record–based systems, EHRs are digital versions of a patient’s record of care. Page 504, Empiric antimicrobial selection, a historical search for previous diagnoses and culture and susceptibility results).
Regarding claim 10, Kuper further discloses the method of claim 1, wherein providing access to the medical data of the first patient comprises:
selecting a subset of the medical data (Page 504, Post prescription antimicrobial review alerting when positive cultures and/or rapid diagnostic tests are available and de-escalation of antimicrobial is possible, alerting to MDROs, and alerting for changing renal function requiring antimicrobial dose adjustments. In addition, these systems can identify intravenous to oral conversions and highlight significant drug–drug interactions when therapy is changed.); and
displaying the subset of the medical data in the interface (Page 504, Post prescription antimicrobial review alerting when positive cultures and/or rapid diagnostic tests are available and de-escalation of antimicrobial is possible, alerting to MDROs, and alerting for changing renal function requiring antimicrobial dose adjustments. In addition, these systems can identify intravenous to oral conversions and highlight significant drug–drug interactions when therapy is changed.);
wherein the subset of the medical data is selected based on at least one of: an input by the first member of the treating team, the subset of the medical data containing new test result for the first patient that has not been retrieved, or the subset of the medical data including laboratory measurement results that are relevant to the first clinical decision (Page 504, Post prescription antimicrobial review alerting when positive cultures and/or rapid diagnostic tests are available and de-escalation of antimicrobial is possible, alerting to MDROs, and alerting for changing renal function requiring antimicrobial dose adjustments. In addition, these systems can identify intravenous to oral conversions and highlight significant drug–drug interactions when therapy is changed.).
Regarding claim 13, Kuper further discloses the method of claim 1, wherein the recommendation for the prescription of the first antibiotic further includes a recommendation of at least one of: a route of administration of the first antibiotic, or a duration of a treatment course in which the first dosage of the first antibiotic is to be administered (Page 503, Empiric antimicrobial selection, Utilization of guidelines embedded in order sets within the EHR can improve initial antimicrobial selection, optimize dosing, and duration of therapy, Utilization of guidelines embedded in order sets within the EHR can improve initial antimicrobial selection, optimize dosing, and duration of therapy. Page 503, Table 3, Selecting appropriate dose, indication, and/ or duration upon order entry.).
Regarding claim 14, Kuper further discloses the method of claim 1, wherein the recommendation is generated based on at least one of: a medical history of the first patient, a suspected diagnosis of the first patient, suspected pathogens causing a disease of the first patient, a risk of drug resistance of the first patient, or laboratory test results of the first patient (Page 504, Empiric antimicrobial selection, the add-on CDSS that can take the prescriber through a series of questions about the patient and do a historical search for previous diagnoses and culture and susceptibility results. They provide sophisticated guidance on which antimicrobial is most likely to cover the suspected pathogen causing the infection. Page 505, Syndrome-specific management, Electronically generated alerts can facilitate real-time notification of potential interventions (eg, patients with newly positive blood cultures).).
Regarding claim 17, Kuper further discloses the method of claim 1, wherein the recommendation is generated based on at least one of: an antibiotic inventory of a hospital or of a clinic that is treating the first patient, or on a list of restricted drugs (Page 504, Empiric antimicrobial selection, Empiric antimicrobial selection. triaging antimicrobial drug shortages. Page 504, Table 3, Managing prior authorizations, restrictions, and nonformulary agents.)
Regarding claim 19, Kuper further discloses the method of claim 1, wherein the recommendation is generated based on one or more rules (Page 503, Empiric antimicrobial selection, Utilization of guidelines embedded in order sets within the EHR can improve initial antimicrobial selection, optimize dosing, and duration of therapy. Also see page 505, Syndrome-specific management. Page 509, Table 4, local sensitivities, developed using CLSI antibiogram recommendations, and ID specific guidelines easily available in the system(s) as part of the decision process.); and
wherein at least one or more rules or the plurality of guidelines are editable by administrators of a hospital or a clinic (Page 509, Table 4, The systems should have the ability to be easily customized by the end user. Page 505, Syndrome-specific management, facility-specific clinical practice guidelines for common infectious disease syndromes together with strategies for dissemination and implementation. Page 509, Table 4, local sensitivities, developed using CLSI antibiogram recommendations, and ID specific guidelines easily available in the system(s) as part of the decision process.).
Regarding claim 20, Kuper further discloses the method of claim 1 comprising: determining a triage ranking for each of the plurality of patients based on the medical data and the antibiogram information (Page 504, Postprescription antimicrobial review, EHRs and add-on CDSSs have developed methods to triage patients, based on pre-built and customizable algorithms that identify patients at highest risk for adverse events or inappropriate therapy. Typically, this is accompanied by a corresponding visual cue such as a red flag or a high numerical score that allows any user of the system to quickly determine which patients should be reviewed. Pages 509-510, The path forward: Future needs from a systems perspective, The usefulness of these stewardship systems to prospectively identify interventions or to identify patients at greatest risk for an infection or sepsis will be greatly improved if they can incorporate data from previous admissions, clinic visits, or population health datasets into the decision process. Page 509, Table 4, local sensitivities, developed using CLSI antibiogram recommendations, and ID specific guidelines easily available in the system(s) as part of the decision process.); and
displaying, via an interface accessible by a first member of an ASP team, a ranked patient list representing the plurality of patients and including at least a part of the medical data of the plurality of patients, the patient list being ranked based on the triage rankings of the plurality of patients, to facilitate a first clinical decision by the first member of the ASP team to intervene a prescription of a first antibiotic to the first patient of the plurality of patients (Page 504, Postprescription antimicrobial review, EHRs and add-on CDSSs have developed methods to triage patients, based on pre-built and customizable algorithms that identify patients at highest risk for adverse events or inappropriate therapy. Typically, this is accompanied by a corresponding visual cue such as a red flag or a high numerical score that allows any user of the system to quickly determine which patients should be reviewed. Pages 509-510, The path forward: Future needs from a systems perspective, The usefulness of these stewardship systems to prospectively identify interventions or to identify patients at greatest risk for an infection or sepsis will be greatly improved if they can incorporate data from previous admissions, clinic visits, or population health datasets into the decision process.).
Regarding claim 21, Kuper does not appear to explicitly disclose wherein the triage ranking is based on one or more of: availability of the most recent lab test result, lab/drug mismatch, restricted drug prescription, and history of prior interventions.
Gupta teaches that it was old and well known in the art of antimicrobial stewardship at the time of the filing to determine patient risk of an antibiotic-related adverse event based on one or more of: availability of the most recent lab test result, lab/drug mismatch, restricted drug prescription, and history of prior interventions (Gupta, paragraphs [0145]-[0147]) to ensure lifesaving medications are effective and delivered in a timely manner, improving patient outcomes (Gupta, paragraph [0003]).
Therefore, it would have been obvious to one of ordinary skill in the art of antimicrobial stewardship at the time of the filing to modify the triage ranking of Kuper to be based on availability of the most recent lab test result, lab/drug mismatch, restricted drug prescription, and history of prior interventions, as taught by Gupta, in order to ensure lifesaving medications are effective and delivered in a timely manner, improving patient outcomes
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (The role of electronic health record and "add-on" clinical decision support systems to enhance antimicrobial stewardship programs) in view of Gupta et al. (U.S. Pub. No. 2021/0287809) and Yancey (U.S. Pub. No. 2005/0182303).
Regarding claim 3, Kuper further discloses the method of claim 1, wherein the recommendation is generated by:
retrieving an antibiogram table of the antibiogram information from the plurality of databases based on a location identifier of a location where the first patient is to receive a antibiotics treatment, the antibiogram table comprising multiple sections, each section being associated with an antibiotic and listing degrees of resistances of different pathogens to the associated antibiotic (Page 509, table 4, local sensitivities, developed using CLSI antibiogram recommendations, and ID specific guide lines easily available in the system(s) as part of the decision process. CLSI compliant antibiograms can be produced on demand and stratified by location, age, specimen type, or disease state. Systems should be able to produce setting-specific antibiograms for use for their ED and ambulatory sectors. Page 505, Reporting capabilities, A key reporting feature imperative to ASPs is the ability to produce antibiograms on request. These could be produced for a selected time, a specific unit or service, or for selected organisms. Some add-on systems and EHR-based systems have this capability. Page 504, Empiric antimicrobial selection, CDSS that can take the prescriber through a series of questions about the patient and do a historical search for previous diagnoses and culture and susceptibility results. They provide sophisticated guidance on which antimicrobial is most likely to cover the suspected pathogen causing the infection. Producing and utilizing antibiograms of a specific unit in order to identify microbials for a patient is construed as retrieving based on a location identifier of a location where the first patient is to receive a antibiotics treatment, such as a unit identifier.;
identifying sections of the antibiogram table associated with the list of candidate antibiotics (Page 504, Empiric antimicrobial selection, CDSS that can take the prescriber through a series of questions about the patient and do a historical search for previous diagnoses and culture and susceptibility results. They provide sophisticated guidance on which antimicrobial is most likely to cover the suspected pathogen causing the infection. Page 509, table 4, local sensitivities, developed using CLSI antibiogram recommendations ,and ID specific guidelines easily available in the system(s) as part of the decision process. CLSI compliant antibiograms can be produced on demand and stratified by location, age, specimen type, or disease state.);
determining, based on the identified sections of the antibiogram, degrees of resistances of the list of candidate antibiotics to one or more pathogens that cause the patient’s disease, the one or more pathogens being indicated in the medical data of the first patient (Page 504, Empiric antimicrobial selection, CDSS that can take the prescriber through a series of questions about the patient and do a historical search for previous diagnoses and culture and susceptibility results. They provide sophisticated guidance on which antimicrobial is most likely to cover the suspected pathogen causing the infection.;
generating the recommendation including the recommended antibiotic and the associated dosage (Page 503, Table 3, Promoting initial appropriate empiric therapy based on suspected infection type through order sets. Selecting appropriate dose, indication, and/ or duration upon order entry. Page 504, Empiric antimicrobial selection, provide sophisticated guidance on which antimicrobial is most likely to cover the suspected pathogen causing the infection. Page 509, Table 4, prompt appropriate selection of tests and medications. Ability to identify syndrome-specific interventions.).
Kuper does not appear to explicitly disclose retrieving, from the plurality of databases and based on a disease of the first patient indicated in the medical data of the first patient, a graph representing a guideline of the plurality of guidelines; traversing the graph based on a status of the disease of the first patient to obtain a list of candidate antibiotics ranking the list of candidate antibiotics based on the degrees of resistances; or selecting a recommended antibiotic from the list of candidate antibiotics based on the ranking.
Yancey teaches that it was old and well known in the art of antibiotic administration systems at the time of the filing to include retrieving, from the plurality of databases and based on a disease of the first patient indicated in the medical data of the first patient, a graph representing a guideline of the plurality of guidelines; traversing the graph based on a status of the disease of the first patient to obtain a list of candidate antibiotics ranking the list of candidate antibiotics based on the degrees of resistances; and selecting a recommended antibiotic from the list of candidate antibiotics based on the ranking (Yancey, paragraph [0037], A program storage device, such as a Sepsis Tree decision-support program 2, is the means by which data input by the physician 3 and/or clinician 5 or MATT team member 1 are collated and analyzed to generate recommended antimicrobial regiments 4, preferably in order of most preferred to lease preferred (but acceptable) regimens. In certain embodiments, the Sepsis Tree decision-support program can analyze data regarding patient renal function, patient allergies, hospital antibiotic/antimicrobial resistance patterns and provide recommended antimicrobial regimens based on empiric sepsis category and appropriate standards for antibiotic rotation. Paragraph [0047], The output reports of the computerized systems and methods of the invention can include information regarding: (a) antibiotic regimen recommendations; (b) 1-5 potential antibiotic regimens in order of preference; (c) brief clinical summary of patient and parameters that effect antibiotic selection; (d) rationale for the suggested regimen; and (e) references from the medical literature.) to achieve improved antimicrobial/antibiotic administration and usage (Yancey, paragraph [0013]).
Therefore, it would have been obvious to one of ordinary skill in the art of medication stewardship systems at the time of the filing to modify the generation of the recommendation of Kuper to include retrieving, from the plurality of databases and based on a disease of the first patient indicated in the medical data of the first patient, a graph representing a guideline of the plurality of guidelines; traversing the graph based on a status of the disease of the first patient to obtain a list of candidate antibiotics ranking the list of candidate antibiotics based on the degrees of resistances; and selecting a recommended antibiotic from the list of candidate antibiotics based on the ranking, as taught by Yancey, in order to achieve improved antimicrobial/antibiotic administration and usage.
Regarding claim 15, Kuper does not appear to explicitly disclose, but Yancey teaches that it was old and well known in the art of medication stewardship systems at the time of the filing wherein the recommendation is generated based on:
determining a benefit-over-risk score for each of a plurality of alternative antimicrobial regimes, the risk being determined based on a likelihood of developing resistance to an antibiotics (Yancey, paragraph [0037], A program storage device, such as a Sepsis Tree decision-support program 2, is the means by which data input by the physician 3 and/or clinician 5 or MATT team member 1 are collated and analyzed to generate recommended antimicrobial regiments 4, preferably in order of most preferred to lease preferred (but acceptable) regimens. In certain embodiments, the Sepsis Tree decision-support program can analyze data regarding patient renal function, patient allergies, hospital antibiotic/antimicrobial resistance patterns and provide recommended antimicrobial regimens based on empiric sepsis category and appropriate standards for antibiotic rotation. Paragraph [0039], the report contains clinical data and a listing of preferred antibiotic/antimicrobial regimens along with the rationale behind why the regimens are preferred. Paragraph [0048], random or nonrandom assignment of order of preference of equally efficacious antimicrobial regimens based on hospital epidemiological considerations. Preference of antimicrobials based on efficaciousness and epidemiological considerations such as antimicrobial resistance patterns is construed as a benefit-over-risk score based on likelihood of developing resistance to an antibiotics.);
ranking the plurality of alternative antimicrobial regimes based on the benefit-over-risk scores (Yancey, A program storage device, such as a Sepsis Tree decision-support program 2, is the means by which data input by the physician 3 and/or clinician 5 or MATT team member 1 are collated and analyzed to generate recommended antimicrobial regiments 4, preferably in order of most preferred to lease preferred (but acceptable) regimens. Paragraph [0047], 1-5 potential antibiotic regimens in order of preference.; and
providing the recommended empirical antimicrobial regime based on the ranking (Yancey, paragraph [0047], The output reports of the computerized systems and methods of the invention can include information regarding: (a) antibiotic regimen recommendations; (b) 1-5 potential antibiotic regimens in order of preference.) to achieve improved antimicrobial/antibiotic administration and usage (Yancey, paragraph [0013]).
Therefore, it would have been obvious to one of ordinary skill in the art of medication stewardship systems at the time of the filing to modify the generation of the recommendation of Kuper to include the limitations above, as taught by Yancey, in order to achieve improved antimicrobial/antibiotic administration and usage.
Claims 4-5, 7 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (The role of electronic health record and "add-on" clinical decision support systems to enhance antimicrobial stewardship programs) in view of Gupta et al. (U.S. Pub. No. 2021/0287809) and Lamb et al. (U.S. Pub. No. 2019/0355481).
Regarding claim 4, Kuper further discloses the method of claim 1, wherein the interface comprises a communication interface to enable real-time or asynchronous communication between the first member and other members of the treating team or between the first member and members of another team (Page 505, Communication, Both add-on CDSSs and EHRs can facilitate communication among antimicrobial stewardship team members, intradepartmental communication (eg, pharmacist to pharmacist), and interdepartmental communication (eg, pharmacists to nurse, or nurse to physician) by creating a central communication location for patient care since each access to the system. These systems can improve communication between ASP team members regarding cultures, radiologic test results, and other information that could impact antimicrobial prescribing or duration. An example of intradepartmental communication is pharmacists utilizing the IT system to record notes about antibiotic therapy or drug levels from shift to shift or upon transfer to another unit. Interdepartmental communication and alerting could be utilized to facilitate infectious diseases consultation for high-risk infections such as Staphylococcus aureus bacteremia. Page 509, table 4, Systems need to interface with consumer-friendly formats such as patient portals, emailing, texting, and telemedicine.);
wherein the interface further comprises a data access interface configured to display data comprising at least one of the medical data of the first patient, the antibiogram information, or one or more recommended antibiotics (Page 503, Electronic health record–based systems, EHRs are digital versions of a patient’s record of care. They have the capabilities of an electronic medical record but are accessible to all clinicians involved in a patient’s care and can share information with other areas of the same healthcare system (and ideally across multiple healthcare organizations. Page 505, A key reporting feature imperative to ASPs is the ability to produce antibiograms on request. These could be produced for a selected time, a specific unit or service, or for selected organisms. Some add-on systems and EHR-based systems have this capability. Page 504, Empiric antimicrobial selection, provide sophisticated guidance on which antimicrobial is most likely to cover the suspected pathogen. Page 504, Empiric antimicrobial selection, guiding the appropriate ordering of clinical diagnostic testing via laboratory test order interfaces. Page 509, Table 4, prompt appropriate selection of tests and medications. Systems should have interfaces with “smart” phones and tablets to allow for mobile access and limit need to have access to a desktop computer.); and
Kuper does not appear to explicitly disclose wherein the communication interface and the data access interface are provided concurrently to the first member.
Lamb teaches that it was old and well known in the art of antibiotic administration systems at the time of the filing wherein the communication interface and the data access interface are provided concurrently to the first member (Lamb, figs. 4-5 and paragraph [0103] the communication thread and the dashboard may both be displayed simultaneously on the display device.) to make coordination of patient care among all the care providers less complicated or time-consuming (Lamb, paragraph [0003]).
Therefore, it would have been obvious to one of ordinary skill in the art of medication stewardship systems at the time of the filing to modify the generation of the recommendation of Kuper such that the communication interface and the data access interface are provided concurrently to the first member, as taught by Lamb, in order to make coordination of patient care among all the care providers less complicated or time-consuming.
Regarding claim 5, Kuper further discloses the method of claim 4, wherein the communication interface is configured to send, across a computer network, the generated recommendation including at least one of a prescription of the first dosage of the first antibiotic or an order of the first diagnostic test to a second member of the treating team (Page 505, Communication, Both add-on CDSSs and EHRs can facilitate communication among antimicrobial stewardship team members, intradepartmental communication (eg, pharmacist to pharmacist), and interdepartmental communication (eg, pharmacists to nurse, or nurse to physician) by creating a central communication location for patient care since each access to the system. These systems can improve communication between ASP team members regarding cultures, radiologic test results, and other information that could impact antimicrobial prescribing or duration. An example of intradepartmental communication is pharmacists utilizing the IT system to record notes about antibiotic therapy or drug levels from shift to shift or upon transfer to another unit. Interdepartmental communication and alerting could be utilized to facilitate infectious diseases consultation for high-risk infections such as Staphylococcus aureus bacteremia. Page 504, send alerts to pharmacy or the ASP team when the nonformulary drug is ordered, communicate the authorization process through an alert or notes embedded within the order (EHR).).
Regarding claim 7, Kuper does not appear to explicitly disclose, but Lamb teaches that it was old and well known in the art of antibiotic administration systems at the time of the filing wherein the interface is a first interface; and
wherein the method further comprises:
receiving the intervention recommendation from the second system (Lamb, paragraph [0081], Collaborative system interface 1000 may include a notification section whereby the user viewing collaborative system interface 1000 may be notified of urgent patient conditions, active communication channel discussions, lab test results, and other information. Paragraph [0044], the lab VHA may determine that multiple patients at the medical facility are harboring antibiotic resistant strains of bacteria. The lab VHA may then inform the care providers, via the communication thread, so that alternate therapeutic approaches may be applied.);
displaying, in a second interface, a notification including the intervention recommendation (Lamb, paragraph [0081], Collaborative system interface 1000 may include a notification section whereby the user viewing collaborative system interface 1000 may be notified of urgent patient conditions, active communication channel discussions, lab test results, and other information. Paragraph [0044], the lab VHA may determine that multiple patients at the medical facility are harboring antibiotic resistant strains of bacteria. The lab VHA may then inform the care providers, via the communication thread, so that alternate therapeutic approaches may be applied. Also se paragraph [0082].); and
in response to receiving an input from the first member to respond to the notification, displaying the first interface including the communication interface and the data access interface to the first member (Paragraph [0082], Collaborative system interface 1000 further includes links to patient communication thread-dashboard pairs. Each patient link may include notifications where relevant. Paragraph [0083], Selection of a patient link may launch the communication thread or dashboard for that patient. Paragraph [0103], the communication thread and the dashboard may both be displayed simultaneously on the display device.) to make coordination of patient care among all the care providers less complicated or time-consuming (Lamb, paragraph [0003]).
Therefore, it would have been obvious to one of ordinary skill in the art of medication stewardship systems at the time of the filing to modify Kuper to include the limitations above, as taught by Lamb, in order to make coordination of patient care among all the care providers less complicated or time-consuming.
Regarding claim 11, Kuper does not appear to explicitly disclose, but Lamb teaches that it was old and well known in the art of antibiotic administration systems at the time of the filing wherein the interface is a first interface; and
wherein the method further comprises:
receiving an indication that the new test result for the first member is stored at the plurality of databases (Lamb, paragraph [0081], Collaborative system interface 1000 may include a notification section whereby the user viewing collaborative system interface 1000 may be notified of urgent patient conditions, active communication channel discussions, lab test results, and other information. Paragraph [0082], In some examples, when a lab test result for a patient is available, the link for that patient may include a notification of an available lab test result, such as the notification displayed in the link for patient ID 1235.);
displaying, in a second interface, a notification including the indication (Lamb, paragraph [0081], Collaborative system interface 1000 may include a notification section whereby the user viewing collaborative system interface 1000 may be notified of urgent patient conditions, active communication channel discussions, lab test results, and other information. Paragraph [0082], In some examples, when a lab test result for a patient is available, the link for that patient may include a notification of an available lab test result, such as the notification displayed in the link for patient ID 1235.); and
in response to receiving an input from the first member to respond to the notification, displaying the subset of the medical data via the first interface to the first member (Paragraph [0082], Collaborative system interface 1000 further includes links to patient communication thread-dashboard pairs. Each patient link may include notifications where relevant. Paragraph [0083], Selection of a patient link may launch the communication thread or dashboard for that patient. Paragraph [0103], the communication thread and the dashboard may both be displayed simultaneously on the display device.) to make coordination of patient care among all the care providers less complicated or time-consuming (Lamb, paragraph [0003]).
Therefore, it would have been obvious to one of ordinary skill in the art of medication stewardship systems at the time of the filing to modify Kuper to include the limitations above, as taught by Lamb, in order to make coordination of patient care among all the care providers less complicated or time-consuming.
Regarding claim 12, Kuper further discloses the method of claim 11, further comprising: selecting, based on a state of disease indicated in the medical data for the first patient, a subset of the plurality of guidelines (Page 505, Active and passive stewardship interventions that rely on an EHR or CDSS exist for a variety of syndromes, including bacteremia, pneumonia, skin and soft-tissue infections, C. difficile colitis, and intra-abdominal infections. Electronically generated alerts can facilitate real-time notification of potential interventions (eg, patients with newly positive blood cultures), provide best-practice alerts to optimize antimicrobial therapy or minimize unnecessary therapy, and/or document compliance with institutional treatment guidelines. Page 503, Empiric antimicrobial selection, Utilization of guidelines embedded in order sets within the EHR can improve initial antimicrobial selection, optimize dosing, and duration of therapy. These order sets can also serve as an educational reference for trainees and providers. Page 504, Empiric antimicrobial selection, add-on CDSS that can take the prescriber through a series of questions about the patient and do a historical search for previous diagnoses and culture and susceptibility results. They provide sophisticated guidance on which antimicrobial is most likely to cover the suspected pathogen causing the infection before culture data for the current episode of care become available. In both scenarios, the prescriber can be given tailored guidance on choice by syndrome rather than having to abide by static guidelines.); and
providing, via the interface, access to the subset of the plurality of guidelines (Page 509, Table 4, Providers should have appropriate local sensitivities, developed using CLSI antibiogram recommendations, and ID specific guidelines easily available in the system(s) as part of the decision process and for documentation. Page 503, Empiric antimicrobial selection, Utilization of guidelines embedded in order sets within the EHR can improve initial antimicrobial selection, optimize dosing, and duration of therapy. These order sets can also serve as an educational reference for trainees and providers. Page 504, Empiric antimicrobial selection, They can be programmed to guide providers with alternative therapies, and, hyperlinks to institutional or national guidelines can be embedded within numerous places within these systems. These hyperlinks can be utilized to help promote optimal empiric therapy selection that complies with local formulary policies.)
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (The role of electronic health record and "add-on" clinical decision support systems to enhance antimicrobial stewardship programs) in view of Gupta et al. (U.S. Pub. No. 2021/0287809), Lamb et al. (U.S. Pub. No. 2019/0355481) and Citrome et al. (When does a difference make a difference? Interpretation of number needed to treat, number needed to harm, and likelihood to be helped or harmed).
Regarding claim 16, Kuper further discloses the method of claim 15, wherein the recommendation for the empirical antimicrobial regime further includes a recommendation of when the first dosage of the first antibiotic is to be administered (Page 504, Postprescription antimicrobial review, creating alerts to identify patients that require antimicrobial modifications. Page 509, The path forward: Future needs from a systems perspective, prospectively identify interventions or to identify patients at greatest risk for an infection or sepsis. Page 505, Syndrome-specific management, Electronically generated alerts can facilitate real-time notification of potential interventions (eg, patients with newly positive blood cultures), provide best-practice alerts to optimize antimicrobial therapy.); and
Kuper does not appear to explicitly disclose, but Citrome teaches that it was old and well known in the art of medication stewardship systems at the time of the filing wherein the recommendation is generated based on a ratio based on a first number needed to treat and a second number needed to harm, the first number indicating how many patients need to be treated with the first antibiotic in order to benefit one patient, the second number indicating how many patients can be treated with the first antibiotic before one experiences a treatment harm (Citrome, Page 407, Summary, The concept of likelihood to be helped or harmed(LHH), calculated as the ratio of NNH to NNT, is used to illustrate trade-offs between benefits and harms. Page 409, right column, 1st paragraph, A LHH much greater than 1 is the norm when comparing a desired outcome, for example remission, with a very severe adverse event. A LHH a little greater than 1is usually observed for acceptable interventions when comparing a desired outcome with an adverse event that is usually mild or moderate, but that may still lead to discontinuation. LHH less than or equal to1 is usually only acceptable when comparing a desired outcome with an adverse event that is usually mild or moderate, but that is usually temporary and does not lead to discontinuation, or there is a particularly urgent need for benefit (efficacy) that mitigates an otherwise prohibitive risk of harm (side effects).) to quantify benefit:harm trade-offs that are crucial in real-world clinical decision-making (Citrome, Page 411, Conclusions).
Therefore, it would have been obvious to one of ordinary skill in the art of medication stewardship systems at the time of the filing to modify recommendations of Kuper such that it is generated based on a ratio based on a first number needed to treat and a second number needed to harm, the first number indicating how many patients need to be treated with the first antibiotic in order to benefit one patient, the second number indicating how many patients can be treated with the first antibiotic before one experiences a treatment harm, as taught by Citrome, in order to quantify benefit:harm trade-offs that are crucial in real-world clinical decision-making.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (The role of electronic health record and "add-on" clinical decision support systems to enhance antimicrobial stewardship programs) in view of Gupta et al. (U.S. Pub. No. 2021/0287809) and Robicsek et al. (U.S. Pub. No. 2013/0325502).
Regarding claim 18, Kuper does not appear to explicitly disclose, but Robicsek teaches that it was old and well known in the art of treatment recommendation at the time of the filing wherein the recommendation is generated based on a treatment history of a second patient of the plurality of patients and based on a comparison of symptoms between the first patient and the second patient (Paragraph [0031], This illustrative method 20 begins at the step 22 of inputting historical incidence data. In this inputting step 22, data is gathered from a selected locality regarding all available recorded incidences of a selected syndrome within that locality. Paragraph [0034], the next step 24 in the illustrative method 20 is filtering irrelevant incidence information from the data collected in step 22. In this step, historical data from incidences of the syndrome or infection of interest (e.g., ABI) is filtered to cull records that are unnecessary, undesirable, and/or inappropriate for purposes of comparison to the current patient and that patient's specific syndrome or infection. Paragraph [0042], the next step 32 in the illustrative method is to input clinical data for each incidence of the given syndrome of interest, and associate such data, by patient, with the outcomes determined in step 30. The type of clinical data collected may include many common patient characteristics and factors relevant to medical diagnoses, such as age, sex, other demographics, prior surgical procedures, recent prescription history, prior lab results, diagnoses of long-term immuno-compromising conditions like HIV, co-morbidities, admission history, and prior related diagnoses. Paragraph [0045], the next step 34 in the illustrative method is to statistically correlate the treatment regimen outcomes determined in step 30 for each incidence of the syndrome of interest with the patient demographic and clinical data obtained in step 32 of the patients that presented with the incidences. Paragraph [0085], At this point, a clinician or other user can input a current patient's relevant characteristics and the guidance engine, using the models determined in step 34, will plug the characteristics into the models determined in step 34 and provide probabilities via a therapeutic recommendation tool that a given regimen would cover that particular patient's syndrome.) to provide guidance to clinicians in selecting treatment regimens in a way that overcomes the drawbacks of systems that provide guidance based on antibiograms do not reflect any patient-specific characteristics (Robicsek, paragraphs [0009]-[0010]).
Therefore, it would have been obvious to one of ordinary skill in the art of medication stewardship systems at the time of the filing to modify recommendations of Kuper such that it is generated based on a treatment history of a second patient of the plurality of patients and based on a comparison of symptoms between the first patient and the second patient, as taught by Robicsek, in order to provide guidance to clinicians in selecting treatment regimens in a way that overcomes the drawbacks of systems that provide guidance based on antibiograms do not reflect any patient-specific characteristics.
Response to Arguments
Applicant's arguments filed January 8, 2026 regarding claims 1-5 and 7-21 being rejected under 35 U.S.C. §101 have been fully considered but they are not persuasive.
Applicant argues that the claims are rooted in or directed to an improvement of a particular technology, including real-time computer-based patient intervention systems and providing computer graphical interface presenting.
In response, the Applicant has not provided any evidence as to a technical problem or technical solution in the fields of real-time computer-based patient intervention systems or computer graphical interface presenting. Rather, the claims recite t performing antimicrobial review of triaged patients (i.e. reviewing patient data, antibiogram data and guidelines) and recommending antibiotics or diagnostic tests, which is considered both a mental process and certain method of organizing human activity. The claims do not recite any improvements or details as to how data is retrieved or presented and do not provide any details as to how the recommended antibiotics or diagnostic tests are determined, which could be considered an improvement to real-time computer-based patient intervention systems and providing computer graphical interface presenting.
Applicant argues that the claimed ASP system and methods cannot be practically performed in the human mind.
In response, it is maintained that “generating a recommendation for at least one of a prescription of a first antibiotic of the plurality of antibiotics or an order of a first diagnostic test for the first patient of the plurality of patients, the recommendation being generated based on applying one or more rules to the plurality of guidelines, the antibiogram information, and the medical data for the first patient” can be performed in the human mind using observations, evaluations, judgments and opinions.
Applicant argues that the claims are integrated into a practical application of facilitating antimicrobial intervention of potentially many patients in a care setting and presenting selectively relevant data to care team clinicians for enabling them to make time-sensitive decisions in the care of patients.
In response, the Applicant has not provided details as to which additional limitations in the claim integrate the abstract idea into the practical application or why. As discussed above, the retrieving and presenting of the data is determined to be the insignificant extra-solution activity of data gathering and data outputting which is well-understood, routine and conventional.
Applicant argues that the combination of elements of the claims represent significantly more than an abstract idea and are not merely routine or conventional.
In response, the Applicant has not provided details as to which additional limitations in the claim amount to significantly more or why. As discussed above, the retrieving and presenting of the data is determined to be the insignificant extra-solution activity of data gathering and data outputting which is well-understood, routine and conventional.
Applicant's arguments filed January 8, 2026 regarding claims 1-5 and 7-21 being rejected under 35 U.S.C. §102/103 have been fully considered but they are moot in view of the new grounds of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devin C. Hein whose telephone number is (303)297-4305. The examiner can normally be reached 9:00 AM - 5:00 PM M-F MDT.
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/DEVIN C HEIN/Examiner, Art Unit 3686