WO2018084166A1

WO2018084166A1 - Method, computing system and medium for optimizing of healthcare institution resource utilisation

Info

Publication number: WO2018084166A1
Application number: PCT/JP2017/039475
Authority: WO
Inventors: Seng Khoon TEH
Original assignee: Nec Corporation
Priority date: 2016-11-02
Filing date: 2017-11-01
Publication date: 2018-05-11
Also published as: JP2019537121A; SG10201609191RA; JP7044111B2

Abstract

A method for optimizing healthcare resource utilisation is provided. The method includes receiving patient information for at least one patient, determining, from the patient information, a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient, and a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient, selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation based on at least the two constraints such as maximal nurse staffing strength and maximal hospital readmission rate target, and outputting a recommended healthcare institution resource allocation.

Description

METHOD, COMPUTING SYSTEM AND MEDIUM FOR OPTIMIZING OF HEALTHCARE INSTITUTION RESOURCE UTILISATION

The present invention relates broadly, but not exclusively, to methods and systems for optimizing healthcare institution resource utilisation.

Background

In hospital operation, it is becoming increasingly important to monitor and control the number of patients who readmit within 30 days of being discharged. Reducing readmission reduces the burden on healthcare institution resources (e.g. doctors, machinery, and beds), improves patient outcomes, as resources are available for needed attention, and reduces unnecessary public healthcare expenditure.

Currently, there are several methods for assessing hospital readmission risk. Most of them focus on development of a quantitative score indicative of either the probability of hospital readmission, or the probability of preventable hospital readmission. The intention is to provide actionable insights for healthcare providers to render appropriate intervention to minimize rehospitalisation.

Generally speaking, healthcare resources such as basic nursing care are critical to reducing readmission via intervention. Intervention includes, for example, discharge preparation, care coordination, and patient education. However, it is increasingly common that hospitals have inadequate nurse staff, which affects nurses’ efforts to carry out these processes of care to reduce hospital readmission. When supply of effective medical intervention to minimize rehospitalisation does not meet escalating patient demand, healthcare providers need to real-time prioritize allocation of scarce resources to minimise the impact of hospital readmission.

A need therefore exists to provide methods and systems for optimizing healthcare resource utilisation that seek to address the above-mentioned problems.

Summary

A method for optimizing healthcare resource utilisation is provided.

According to a first aspect of the present invention, a method for optimizing healthcare resource utilisation is disclosed. The method includes receiving patient information for at least one patient, determining, from the patient information, a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient, and a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient, selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation, and outputting a recommended healthcare institution resource allocation.

According to a second aspect of the present invention, a computing system for optimizing healthcare institute resource utilisation. The computing system includes a receiver means, determining means, selector means and outputting mans. The receiver means receives patient information for at least one patient. The determining means determines, from the patient information, a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient, and a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient. The selector means selects one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation. The outputting means outputs a recommended healthcare institution resource allocation.

According to a third aspect of the present invention, a computer readable medium including computer program code configured to, with at least one processor cause a computer at least to receive patient information for at least one patient, determine, from the patient information, a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient, and a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient, select one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation, and output a recommended healthcare institution resource allocation.

Embodiments of the invention will be better understood and readily apparent to one of ordinary skill in the art from the following written description, which provides examples only, and in conjunction with the drawings in which:

Figure 1 shows a flow chart illustrating a method for optimizing healthcare institution resource utilisation according to present teachings; Figure 2 shows a detailed workflow illustrating transactions between patient, healthcare institute and optimizing system according to present teachings; Figure 3 shows a detailed workflow illustrating transactions between patient, healthcare institute and optimizing system according to present teachings; Figure 4 shows a healthcare institute readmission risk impact assessment, according to present teachings; Figure 5A shows a healthcare institute readmission risk impact and cost assessment, according to present teachings; Figure 5B shows an exemplary targeted pool of patients to maximally reduce hospital readmission impact, according to present teachings; Figure 6 shows a flow chart illustrating a method for optimizing healthcare institution resource utilisation, according to present teachings; Figure 7 shows a detailed workflow illustrating transactions between multiple healthcare institutes and optimizing system, according to present teachings; Figure 8 shows a detailed workflow illustrating transactions between multiple healthcare institutes, patients/healthcare resource and optimizing system , according to present teachings; Figure 9 shows a schematic of a system for optimizing healthcare institute resource utilisation according to present teachings; and Figure 10 shows an exemplary computing device suitable for executing the method for optimizing healthcare institute resource utilisation according to present teachings.

Unless context dictates otherwise, the following terms will be given the meaning provided here:
“healthcare institute”, “healthcare institution” and similar includes a hospital, a clinic or any other institute in which healthcare services are provided;
“patient information” is any information relating to patient or patient’s condition, which may include a disease, injury or other condition. In one example, past clinical records for the patient’s condition may be patient information, as may be a diagnosis for a new patient or new patient condition;
“treatment”, “healthcare intervention” and similar refer to any action by a healthcare resource (e.g. a human or machine) for the purpose of reducing a likelihood of readmission of the patient. Counselling for a patient and prescribing medicine for a patient may also be included in this definition.
“healthcare institute resource” and similar will be understood to refer to any types of resource such as healthcare human resources, medical equipment, beds for patients and so forth. Nursing staff and doctors are included in the intended meaning of “healthcare human resources”.
“readmission” refers to a situation in which patient returns to a healthcare institute for admission within a specific period from previous admission - for example, 30 days.
“one or more databases” refers to any database or databases located within a computing system or remote server such as a computer in hospital or cloud server. The database or databases may each be a cloud database running on a cloud computing platform.

Embodiments of the present invention will be described, by way of example only, with reference to the drawings. Like reference numerals and characters in the drawings refer to like elements or equivalents.

Some portions of the description which follows are explicitly or implicitly presented in terms of algorithms and functional or symbolic representations of operations on data within a computer memory. These algorithmic descriptions and functional or symbolic representations are the means used by those skilled in the data processing arts to convey most effectively the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities, such as electrical, magnetic or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated.

Unless specifically stated otherwise, and as apparent from the following, it will be appreciated that throughout the present specification, discussions utilizing terms such as “receiving”, “determining”, “selecting”, “outputting” or the like, refer to the action and processes of a computer system, or similar electronic device, that manipulates and transforms data represented as physical quantities within the computer system into other data similarly represented as physical quantities within the computer system or other information storage, transmission or display devices.

The present specification also discloses apparatus for performing the operations of the methods. Such apparatus may be specially constructed for the required purposes, or may comprise a computer or other device selectively activated or reconfigured by a computer program stored in the computer. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various machines may be used with programs in accordance with the teachings herein. Alternatively, the construction of more specialized apparatus to perform the required method steps may be appropriate. The structure of a computer will appear from the description below.

In addition, the present specification also implicitly discloses a computer program, in that it would be apparent to the person skilled in the art that the individual steps of the method described herein may be put into effect by computer code. The computer program is not intended to be limited to any particular programming language and implementation thereof. It will be appreciated that a variety of programming languages and coding thereof may be used to implement the teachings of the disclosure contained herein. Moreover, the computer program is not intended to be limited to any particular control flow. There are many other variants of the computer program, which can use different control flows without departing from the spirit or scope of the invention.

Furthermore, one or more of the steps of the computer program may be performed in parallel rather than sequentially. Such a computer program may be stored on any computer readable medium. The computer readable medium may include storage devices such as magnetic or optical disks, memory chips, or other storage devices suitable for interfacing with a computer. The computer readable medium may also include a hard-wired medium such as exemplified in the Internet system, or wireless medium such as exemplified in the global system for mobile communication (GSM) mobile telephone system. The computer program when loaded and executed on such a computer effectively results in an apparatus that implements the steps of the preferred method.

Figure 1 shows a flow chart illustrating a method 100 for optimizing healthcare institute resource utilisation, according to an embodiment of the invention. The method 100 may be performed by a computer coupled to one or more databases. Furthermore, the method 100 may be performed by a computing device which may be a server system, mobile device (e.g. a smart phone or tablet computer) or a personal computer. Further details on the computer and databases will be provided below with reference to Figures 9 and 10.

The method 100 broadly comprises:
Step 102: receiving patient information for at least one patient - this may occur, for example, when a patient arrives at the healthcare institution (i.e. on admission), when the patient is to depart the healthcare institution (i.e. at discharge), during treatment of the patient at the healthcare institution or at another suitable time;
Step 104: determining, from the patient information:
a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient; and
a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient;
Step 106: selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation; and
Step 108: outputting a recommended healthcare institution resource allocation.

Without loss of generality, the description will largely focus on embodiments of the invention relating to hospitals rather than the more general “healthcare institution” case, though it will be understood to apply equally to clinics or other healthcare institutions.

Step 102 involves receiving patient information for at least one patient. The patient information may include information on an electronic medical record, such as severity of condition and type of condition, the patient’s age, origin and other information that can be used to identify the patient and assist in diagnosing diseases and conditions. In one example, the patient information may be collected from a patient and stored in a database of a medical institute.

The patient information may further include past clinical records for a condition similar to the condition from which the at least one patient is suffering. The past clinical records may be anonymized to protect privacy of patients and saved in database of the medical institute. The past clinical records may include whether or not patients with a relevant disease or condition were readmitted after treatment, for example within 30 days. The past clinical records may further include healthcare institution resource utilisation for administering treatment of patients with a particular condition.

The step 102 may be initiated at the time of admission of a patient. In one example, when the patient reaches a reception of a healthcare institute, a system of a healthcare institute initiates the step 102, i.e. the system receives patient information for the patient. The patient may be asked to fill out a medical inquiry form and the duly filled inquiry form together with past clinical records of the patient may be sent to the system of the healthcare institute for use in performance of the present methods.

This scenario can be applied to determine what treatment should be given to the patient in the healthcare institution.

The step 102 may instead be initiated at the time of discharge of a patient. In one example, the patient receives medical examination at the healthcare institute and then a system of a healthcare institute initiates the step 102, i.e. the system receives patient information for the patient. The result of medical examination may be sent to the system of the healthcare institute.

This scenario can be applied to determine what outpatient treatment, or inhome treatment, should be given to the patient outside of the physical premises of the healthcare institution. In other words, the method can be performed:
- before, or upon arrival, of the patient at the hospital,
- during treatment or assessment of the patient (e.g. where conditions or symptoms are deduced during treatment that were not evident to the patient or medical practitioner upon arrival) and/or
- at the end of, or after, treatment at the healthcare institution.

Thus the present methods can be used to allocate resources both in the hospital and out of the hospital, such as at nursing homes, in the home of the patient and at outpatient clinics.

Step 104 may include determining first and second anticipated institution resource requirements. The first anticipated institution requirement is a requirement for a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient. The requirement for the first current patient treatment is calculated based on received patient information which may include information of the patient’s condition and past clinical records for the condition. Based on the past clinical records, severity of condition and probability of readmission may be estimated and the healthcare institution resource requirements for the first current patient treatment may also be anticipated.

The second anticipated institution requirement is a requirement for a second current patient treatment and treatment upon readmission of each patient. The second current patient treatment may be less comprehensive than the first current patient treatment. In other words, under the second current patient treatment the patient may be more likely to be readmitted than under the first current patient treatment. The hospital can then determine whether it wishes to apply the first or second current patient treatment based on the impact on its resources over time.

Ideally speaking, all patients should be treated to reduce a likelihood of readmission. However, due to insufficiency of healthcare institution resources and the variation in availability of those resources, some patients may be required to be treated upon readmission. The requirement for the second current patient treatment and treatment upon readmission is calculated based on received patient information which may include information of condition and past clinical records for the condition. Similar to the requirement for the first current patient treatment, the requirement for the second current patient treatment and treatment upon readmission may be estimated from severity of disease and probability of readmission based on past clinical records.

Step 106 may include selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation. By taking into account severity of each patient’s condition and a likelihood of readmission estimated from the received patient information, one of the first anticipated institution resource requirement and the second anticipated institution resource requirement is selected for each patient to optimize healthcare institution resource utilisation. Of course, the ideal outcome for patient’s would be to apply the first current patient treatment in all cases. However, this requires hospitals to have the requisite resources for such treatment. In general, hospitals will not have such resources available.

Accordingly, there are several ways to optimize healthcare institution resource utilisation. The optimized healthcare institution resource utilisation is defined based on the preference or requirements of the healthcare institution. In one example, minimising total healthcare institution resource utilisation may be the priority of the healthcare institution. As such, the selection will be made to be that which provides the smallest total healthcare institution resources utilisation. In the other example, minimising the likelihood of readmission of patients may be the priority of the healthcare institution.

Simply selecting treatments to minimise readmission would likely exceed the available resources of the healthcare institution. In an alternative method, to minimise the likelihood of readmission of patients, maximum healthcare institution resource availability may be considered. At any point in time, a time-based anticipated healthcare institution resource utilisation should not exceed the maximum healthcare institution resource availability. In other words, the method ensures treatments cannot be scheduled where there are no resources to provide those treatments. If the timebased anticipated healthcare institution resource utilisation exceeds the maximum healthcare institution resource availability, the system may become out of control.

Step 108 may include outputting a recommended healthcare institution resource allocation. The recommended healthcare institution resource allocation may be output by means of any type of output such as information displayed on screen, or messages to healthcare institutes. The recommended healthcare institution resource allocation may include a list of patients to be attended to with high priority in view of severity and likelihood of readmission. Alternatively, the recommended healthcare institution resource allocation may include a request for temporal transferring of healthcare staff from one healthcare institute to another healthcare institute. Based on the selected institution resource requirement for each patient, a healthcare human resource may receive a recommended healthcare institution resource allocation, for example, a type of treatment required to be administered to one or more patients in view of the entire healthcare institution resource availability.

Figure 2 shows an exemplary workflow 200 illustrating transactions between patients 202, a healthcare institute 204 and an optimizing system 206, according to an embodiment of the invention. In one example, patient information 208 may be collected from each patient 202, and the healthcare institute 204 may forward the collected patient information 208 of several patients 202 to optimizing system. Alternatively, each patient 202 may have own personal healthcare records in a database of government or a third party healthcare record management company. Each patient 202 may allow the optimizing system 206 to retrieve personal healthcare records from the database. The patient information 208 may include diagnostic results of patients 202 stored in electronic medical records connected to or stored in a database in the healthcare institute 204. From the diagnostic results of patients 202, the condition suffered by each patient 202 may be identified. The patients 202 may include, separately, one or more present patients, one or more past patients or a combination or present and past patients as needed.

Based on the identified condition, past clinical records for the identified condition may be retrieved from database in the healthcare institute 204 or any other database for clinical information. The past clinical records may include information for severity of the condition, readmission rate of past patients suffering by the condition, a requirement (i.e. hospital resources) for treating the condition to reduce a likelihood of readmission, and a requirement for treating the condition upon readmission.

In one example, the healthcare institute 204 may gather patient information 208 for each patient 202 including past clinical records which are relevant to the identified condition of each patient 202. The gathered patient information 210 may be transferred to optimizing system 206. Thus, the patient information 208 may be transferred from patient 202 to the optimizing system 206 via the healthcare institute 204. However, each patient 202 may send own personal clinical records saved in database of government or a third party healthcare record management company to the optimizing system 206 without using healthcare institute 204. The optimizing system 206 may be any type of computing means including a system installed on a remote server, cloud computing environment, or a computing device in a healthcare institute.

The optimizing system 206 determines a first anticipated institution resource requirement and a second anticipated institution resource requirement as discussed with reference to Figure 1 based on the patient information 208 transferred as the gathered patient information 210. To determine the first anticipated institution resource requirement and the second anticipated institution resource requirement, past clinical records and medical knowledge databases included in the patient information may be used. Clinicians and other hospital staff or practitioners may also be consulted to estimate the treatment requirements for a particular treatment regime or patient condition. The past clinical records and medical knowledge database may include details of the condition and how the condition was attended in the past and whether or not patients with the condition readmitted in the past. The optimizing system 206 may retrieve relevant information for the condition of the patient 202 which is useful for determining first and second healthcare institute resource requirements. For example, average of required costs to attend the condition and a probability of readmission of patients with the condition may be retrieved.

After determining a first anticipated institution resource requirement and a second anticipated institution resource requirement, the optimizing system 206 selects one of the first anticipated institution requirement and the second anticipated institution resource requirement for each patient 202 to optimize healthcare institution resource utilisation. Where healthcare resource is insufficient, minimizing healthcare institution resource usage is prioritised. On the other hand, where a condition needs immediate treatment, such as infection disease, minimizing a likelihood of readmission of patient is prioritised.

Upon the first anticipated institution resource requirement or the second anticipated institution resource requirement being selected for each patient 202, a recommended healthcare institution resource allocation 212 is outputted to the healthcare institute 204. And then, the healthcare institute 204 administers treatment to each patient 202 in line with the recommended healthcare institution resource allocation 212.

Figure 3 shows another exemplary workflow 300 illustrating transactions between patients 302, a healthcare institute 304 and an optimizing system 306, according to present teachings. A healthcare institute 304 may provide the optimizing system 306 with patient information 310. In response, the optimizing system 306 may determine a first anticipated institution resource requirement and a second anticipated institution resource requirement based on patient information 310. And then, the optimizing system 306 may select the first anticipated institution resource requirement or the second anticipated institution requirement for each patient 302.

In one example, the first anticipated institution resource requirement is a current resource requirement to reduce a likelihood of readmission and the second anticipated institution resource requirement is a resource requirement upon readmission. In that situation, the optimizing system 306 may split patients 302 into two groups. A patient 302 in the first group 314 will be provided the first anticipated institution resource requirement, i.e. patient 302 in this group may receive a healthcare treatment to reduce a likelihood of readmission. On the other hand, patients 302 in second group 316 will be provided the second anticipated institution resource requirement, i.e. patient 302 in this group may receive healthcare treatment upon readmission.

The optimizing system 306 provides a recommended healthcare institution resource allocation 312 to the healthcare institute 304. The healthcare institute 304 may split patients 302 into two groups, i.e. a first group 314 and a second group 316 as suggested by the recommended healthcare institution resource allocation 312. In one example, patients 302 may be allocated to the first group 314 because of either of both of the severity of their condition (e.g. life threatening injuries, other conditions requiring detailed and immediate attention, and other conditions that could, if treated properly, avoid significant future hospital resource utilisation) and the otherwise high probability of readmission. On the other hand, patients 302 in the second group 316 may receive healthcare treatment upon readmission because either or both of the severity of their condition and the probability of readmission are low.

Allocation of each of the patients 302 to first group 314 or second group 316 is not fixed. Depending on condition of each of patients 302, of changes in that condition, the allocation for each of the patients may change. A patient 302 allocated to the first group 314 can be migrated to the second group 316 if condition of the patient 302 is improved after healthcare treatment. On the other hand, a patient 302 allocated to the second group 316 can be migrated to the first group 314 if condition of the patient 302 worsens. For example, due to the incubation period of a disease germ, condition of the patient 302 may be suddenly changed after a certain period of time.

Although only the first group 314 and the second group 316 are shown in FIG 3, each of the groups may comprise subgroups and those subgroups may each receive different resource allocation depending on the condition of the patient 302 of likelihood of readmission. For example, there may be a first subgroup of patients 302 suffering from an incurable disease. Such patients 302 are required to be readmitted to mitigate symptoms of the disease on a regular basis and still need care at home. In other words, the likelihood of readmission is 100%. There may be a second subgroup of patients 302 who are potentially going to be readmitted however the readmission may be preventable with a necessary healthcare resource allocation. There may also be a third subgroup of patients 302 who are unlikely to be, or will not be, readmitted. In this case, the first group of patients 302 may comprise the second subgroup of patients 302, and those patients 302 may receive a first resource allocation intended to reduce a likelihood of readmission. The second group of patients 302 may comprise the first and third subgroups of patients - in other words, those whose likelihood of readmission is unlikely to be affected by any particular treatment.

There may similarly be additional subgroups other than the three subgroups identified above.

Figure 4 shows a healthcare institute readmission risk impact assessment 400 according to an embodiment of the invention. In this context, readmission risk impact is defined as a negative effect for resource utilisation when readmission is occurred. When the readmission risk impact is assessed as high, expectation for resource utilisation is high. To minimise resource utilisation, it is important to reduce the readmission risk impact. In one example, selection for one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient is based on a score for each patient. The score may be based on two criteria, severity of condition 404 and healthcare institute readmission probability 402. The readmission probability may be measured before and after healthcare treatment or intervention. The score may be based on the readmission before healthcare treatment or intervention because the score is used to determine whether or not healthcare treatment is required. If the severity of condition 404 is high in terms of cost for treatment i.e. resource utilisation and the probability of readmission is high, there is high healthcare institute readmission risk impact so a high score is assigned to the patient in such a situation. On the other hand, if the severity of condition 404 is low in terms of cost for treatment and probability of readmission is low, there is low healthcare institute readmission risk impact so low score is assigned to such a situation.

In one example, severity of condition in terms of cost for treatment is divided into three categories, i.e. low, medium and high. Probability of healthcare institution readmission before intervention is also divided into three categories, i.e. low, medium and high. Thus, a patient may be assigned to one of nine categories in view of severity of disease and probability of readmission. Each of the nine categories is scored in view of condition and probability of readmission, e.g. a patient with low impact condition is scored as 1, a patient with high impact disease is scored as 9. For patients with condition of low severity, scores 1, 2, and 3 may be assigned to the patients with low, medium, and high probability of readmission respectively. For patients with condition of medium severity, scores 2, 4, and 6 may be assigned to patients with low, medium, and high probability of readmission respectively. For patients with condition of high severity, scores 3, 6, and 9 may be assigned to patients with low, medium, and high probability of readmission respectively.

In one example, there are 4 patients with 4 condition i.e. condition A: Incontinence after stroke 406, condition B: Incontinence after stroke 408, condition C: Recurrent Stroke (Haemorrhagic) 410, and condition D: Recurrent Stroke (Ischemic) 412. These patients with conditions may be assigned to one of nine categories in view of severity of condition and probability of readmission - note: where readmission is unavoidable (e.g. for periodic heart monitoring, dialysis or to check progression of an incurable disease), then the present methods will assume that the resources required to treat unavoidable readmission is deducted from the maximum healthcare institution resource availability. In other words, where readmission is unavoidable, there may be less, or no, flexibility in determining which resources to allocate to treatment of the readmitted patient. Those resources are excluded from present methods when assessing which treatment to administer (i.e. first and second current treatment). This method will instead focus on conditions for which the likelihood of readmission is not 100%. Severity of condition and probability of readmission are analysed based on patient information such as information on electronic medical records, past clinical information, and information on a medical knowledge database.

In accordance with the above discussed readmission risk assessment, the conditions A, B, C and D are assigned to one of nine categories in view of severity of condition and probability of readmission as shown in Figure 4. For example, the condition A 406 is considered as low severity but high probability of readmission, which is scored as 3. The condition B 408 is considered as low severity and medium probability of readmission, which is scored as 2. The condition C 410 is considered as high severity and medium probability of readmission, which is scored as 6. The condition D 412 is considered as high severity and high probability of readmission, which is scored as 9. In one example, the optimizing system may select the first anticipated institution resource requirement for a patient with a high score, e.g. a patient in the condition C 410 or the condition D 412. Also, optimizing system may select the second anticipated institution resource requirement for a patient with a low score, e.g. a patient in the condition A 406 or the condition B 408.

Figure 5A shows a healthcare institute readmission risk impact and cost assessment 500, according to present teachings. The vertical axis indicates hospital cost 504 which is proportional to manpower of healthcare institute. The horizontal axis indicates elapsed time 502 after admission of the healthcare institute. The more patients readmit the healthcare institute, the more hospital cost will be incurred. According to medical domain knowledge of condition and intervention, transitional care upon hospital discharge within 30 days is critical to ensure seamless transfer of patients from hospital to community (home). As a result, national health agencies such as the Centres for Medicare and Medicaid Service introduced readmissions reduction program to tightly monitor and regulate unplanned hospital readmission rate within 30 days. For illustration purpose, we provide a hypothetical cost curve that the healthcare institute cost or readmission at 30 days is the highest 506, which is unwanted revenue for the healthcare institute. To reduce the unwanted revenue for the healthcare institute, it is important to reduce the number of readmission within 30 days by healthcare intervention to reduce a likelihood of readmission.

To assess readmission risk impact and cost, effectiveness of hospital intervention may be assessed. By hospital intervention, likelihood of readmission may be reduced. Thus, effectiveness of hospital intervention may be assessed by comparing likelihood of readmission with and without intervention. These assessments of hospital intervention may be used to determine the first anticipated healthcare institute requirement and the second anticipated healthcare institute requirement as discussed with Figure 1.

Figure 5B shows an exemplary targeted pool of patients to maximally reduce hospital readmission impact 510. In this example, the pool of patients 512 consists of five patients, i.e. P1, P2, P3, P4 and P5. Also, a pool of nurses 514 consists of two nurses, i.e. N1 and N2.

Healthcare resource allocation to each of the patients P1, P2, P3, P4 and P5 in the pool of patients 512 is determined to maximally reduce hospital readmission impact in view of at least two constraints, i.e. hospital cost and maximum hospital readmission target rate.

The hospital cost is proportional to manpower effort such as a nurse. Thus, the hospital cost is proportional to Full Time Equivalent of Nurse (FTE) which can be described as 1×N. In this example, hospital cost is 2×N which is equal to Full Time Equivalent of two nurses N1 and N2 in the pool of nurses 514.

In this example, hospital cost 2×N is distributed to each of the five patients P1, P2, P3, P4 and P5 in view of hospital readmission probability for each of the patients. For example, 0.1×N is allocated to each of the patients P1 (516) and P2 (518). Similarly, 0.4×N is allocated to the patient P3 (520), 0.6×N is allocated to the patient P4 (522), and 0.8×N is allocated to the patient P5 (524).

The other constraint is the maximum hospital readmission target rate. The hospital cost and the hospital readmission probability are required to meet the following condition:
Y≧ a₁Z₁+a₂Z₂+a₃Z₃+ a₄Z₄+a₅Z₅+…
Where
Y=Maximum hospital readmission target rate (e.g. 10%)*Total number of patients;
a_i=Hospital cost allocated to a patient i (i=1, 2, 3, ...); and
Z_i=Hospital readmission probability for a patient i.

Figure 6 shows a flow chart illustrating a method 600 for optimizing healthcare institute resource utilisation, according to an embodiment of the invention. The method 600 may be performed by a computer coupled to one or more databases. Furthermore, the method 600 may be performed by a computing device which may be a server system, a mobile device (e.g. a smart phone or tablet computer) or a personal computer. Further details on the computer and databases will be provided below with reference to Figures 9 and 10.

The method 600 broadly comprises:
Step 602: receiving maximum healthcare institution resource availability;
Step 604: analysing combinations of the first anticipated institution resource requirements and the second anticipated institution resource requirements for respective patients;
Step 606: identifying at least one combination providing a time-based anticipated healthcare institution resource utilisation that, at no point in time, exceeds the maximum healthcare institution resource availability; and
Step 608: identifying, from the at least one combination, a combination that provides the minimum total healthcare institution resource utilisation.

Step 602 involves receiving the maximum healthcare institution resource availability. To optimize utilisation of healthcare institution resource, it is important to know the maximum healthcare institution resource availability. In one example, the availability may be calculated based on the number of nursing staff. Also, the availability may be calculated based on beds for patients and available medical equipment.

Step 604 involves analysing the combinations of the first anticipated institution resource requirements and the second anticipated institution resource requirements for respective patients. For example, the required healthcare institution resources for respective patients may be calculated for each combination of the first anticipated institution resource requirements and the second anticipated institution resource requirements.

Step 606 involves identifying at least one combination providing a timebased anticipated healthcare institution resource utilisation that, at no point in time, exceeds the maximum healthcare institution resource availability. If the required healthcare institution resource exceeds the maximum healthcare institution resource availability, the healthcare institution cannot provide proper healthcare service as required for each patient. Thus, even if a combination may reduce readmission of patients effectively, it is important to exclude combinations which require resources exceeding the maximum resource availability.

Step 608 involves identifying, from the at least one combination, a combination that provides the minimum total healthcare institution resource utilisation. After excluding the combinations which require resources exceeding the maximum resource availability, total healthcare institution resource utilisation is considered. In one example, a combination with minimum total healthcare institution resource utilisation is identified. Any other criterion such as minimizing readmission probability of patients may be applied.

Figure 7 shows a detailed workflow 700 illustrating transactions between multiple healthcare institutes 702 and an optimizing system 704 for optimizing healthcare institution resource utilisation among the multiple healthcare institutes, according to present teachings. Each of the multiple healthcare institutes 702 provides the optimizing system 704 with patient information and resource availability 706 for each of the healthcare institutes 702. The patient information may include the number of patients and the identified condition for each patient. Readmission risk score for each patient as discussed in the explanation for Figure 4 may be assigned in each of the healthcare institutes 702 or the optimizing system 704.

In one example, the optimizing system 704 determines the first and second healthcare institute resource requirements for each patient and selects one of the first and second healthcare institute resource requirements. In addition to healthcare institute resource utilisation within one healthcare institute, healthcare institute resource utilisation across the multiple healthcare institutes 702 may be considered. Such as temporary or permanently transfer nursing staffs from one institute to another institute may be useful to optimize total healthcare institute resource utilisation. Thus, a recommended healthcare institute resource utilisation 708 may include a recommendation within each institute and a recommendation across multiple institutes. Cooperation among multiple institutes may be required to conduct the recommended healthcare institute resource utilisation.

Figure 8 shows a detailed workflow 800 illustrating transactions between

multiple healthcare institutes

802 and 810 and an optimizing system 804 for optimizing healthcare institution resource utilisation among the

multiple healthcare institutes

802 and 810, according to present teachings. Each of the

multiple healthcare institutes

802 and 810 provides the optimizing system 804 with patient information and resource availability 806 for each of the healthcare institutes 802 and 810. Based on the patient information, a required healthcare institute resource may be calculated in the optimizing system 804.

In one example, the healthcare institute 810 may receive more patients than its capacity to handle in view of healthcare institute resource availability. On the other hand, the healthcare institute 802 may receive fewer patients than its capacity to handle in view of healthcare institute resource availability. The optimizing system 804 may gather such information 806 from the

healthcare institute

810 and 802 and provide a recommended healthcare institute resource allocation 808 with each of the healthcare institutes 810 and 802. In this scenario, the healthcare institute 810 is requested to transfer patient 812 to the healthcare institute 802 to optimize total healthcare institute resource utilisation. Alternatively, nursing staff in the healthcare institute 802 may be requested to transfer to the healthcare institute 810 to attend a patient in the healthcare institute 810. For transferring healthcare human resource, such as nursing staff, location of the healthcare institutes and years of experience of nursing staff may be considered in the optimizing system 804.

In accordance with present teaching, healthcare institute resource utilisation within a single healthcare institute or across multiple healthcare institutes may be optimized. Optimizing such a resource utilisation is advantageous for the situation suffered by insufficiency of healthcare institution resource. Especially in the situation suffered by unexpected disasters or terrorism, the number of patients may easily exceed the capacity of the nearest healthcare institute. Prioritize a patient in view of severity of condition and readmission likelihood will be useful to optimize resource utilisation. Also, transferring patients or healthcare human resource will be useful to achieve optimized total healthcare institute resource utilisation.

Figure 9 shows a schematic of a network-based system 900 for optimizing healthcare institute resource utilisation according to an embodiment of the invention. The system 900 comprises a computer 902, one or more databases 904a…904n, a user input module 906 and a user output module 908. Each of the one or more databases 904a…904n is communicably coupled with the computer 902. The user input module 906 and the user output module 908 may be separate and distinct modules communicably coupled with the computer 902. Alternatively, the user input module 906 and the user output module 908 may be integrated within a single mobile electronic device (e.g. a mobile phone, a tablet computer, etc.). The mobile electronic device may have appropriate communication modules for wireless communication with the computer 902 via existing communication protocols. The optimizing system 206, the optimizing system 306, the optimizing system 704 and the optimizing system 804 can be achieved by the computer 902.

The computer 902 may comprise: at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code configured to, with at least one processor, cause the computer at least to: (A) receive patient information for at least one patient; (B) determine, from the patient information: a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient and a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient; (C) select one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient to optimize healthcare institution resource utilisation; and (D) output a recommended healthcare institution resource allocation.

The various types of data, e.g. patient information, healthcare institute resource availability, past clinical records, medical domain knowledge of condition and intervention can be stored in a single database (e.g. 904a), or stored in multiple databases (e.g. patient information are stored on database 904a, healthcare institute resource availability are stored on database 904n, etc.). The databases 904a…904n may be realized using cloud computing storage modules and/or dedicated servers communicably coupled with the computer 902.

Figure 10 depicts an exemplary computer / computing device 1000, hereinafter interchangeably referred to as a computer system 1000, where one or more such computing devices 1000 may be used to facilitate execution of the above-described method for optimising healthcare institute resource utilisation. In addition, one or more components of the computer system 1000 may be used to realize the computer 902. The following description of the computing device 1000 is provided by way of example only and is not intended to be limiting.

As shown in Figure 10, the example computing device 1000 includes a processor 1004 for executing software routines. Although a single processor is shown for the sake of clarity, the computing device 1000 may also include a multi-processor system. The processor 1004 is connected to a communication infrastructure 1006 for communication with other components of the computing device 1000. The communication infrastructure 1006 may include, for example, a communications bus, a cross-bar, or a network.

The computing device 1000 further includes a main memory 1008, such as a random access memory (RAM), and a secondary memory 1010. The secondary memory 1010 may include, for example, a storage drive 1012, which may be a hard disk drive, a solid state drive or a hybrid drive and/or a removable storage drive 1014, which may include a magnetic tape drive, an optical disk drive, a solid state storage drive (such as a universal serial bus (USB) flash drive, a flash memory device, a solid state drive or a memory card), or the like. The removable storage drive 1014 reads from and/or writes to a removable storage medium 1044 in a well-known manner. The removable storage medium 1044 may include a magnetic tape, an optical disk, a non-volatile memory storage medium, or the like, which is read by and written to by removable storage drive 1014. As will be appreciated by persons skilled in the relevant art(s), the removable storage medium 1044 includes a computer readable storage medium having stored therein computer executable program code instructions and/or data.

In an alternative implementation, the secondary memory 1010 may additionally or alternatively include other similar means for allowing computer programs or other instructions to be loaded into the computing device 1000. Such means can include, for example, a removable storage unit 1022 and an interface 1040. Examples of a removable storage unit 1022 and interface 1040 include a program cartridge and cartridge interface (such as that found in video game console devices), a removable memory chip (such as an erasable programmable read only memory (EPROM) or programmable read only memory (PROM)) and associated socket, a removable solid state storage drive (such as a USB flash drive, a flash memory device, a solid state drive or a memory card), and other removable storage unit 1022 and interfaces 1040 which allow software and data to be transferred from the removable storage unit 1022 to the computer system 1000.

The computing device 1000 also includes at least one communication interface 1024. The communication interface 1024 allows software and data to be transferred between computing device 1000 and external devices via a communication path 1026. In various embodiments of the inventions, the communication interface 1024 permits data to be transferred between the computing device 1000 and a data communication network, such as a public data or private data communication network. The communication interface 1024 may be used to exchange data between different computing devices 1000 which partly form an interconnected computer network. Examples of a communication interface 1024 can include a modem, a network interface (such as an Ethernet card), a communication port (such as a serial port, a parallel port, a printer port, a general purpose interface bus (GPIB), an IEEE 1394 port, an RJ45 port, and a USB port), an antenna with associated circuitry and the like. The communication interface 1024 may be wired or may be wireless. Software and data transferred via the communication interface 1024 are in the form of signals which can be electronic, electromagnetic, optical or other signals capable of being received by communication interface 1024. These signals are provided to the communication interface 1024 via the communication path 1026.

As shown in Figure 10, the computing device 1000 further includes a display interface 1002 which performs operations for rendering images to an associated display 1030 and an audio interface 1032 for performing operations for playing audio content via associated speaker(s) 1034.

As used herein, the term "computer program product" may refer, in part, to a removable storage medium 1044, a removable storage unit 1022, a hard disk installed in the storage drive 1012, or a carrier wave carrying software over the communication path 1026 (wireless link or cable) to the communication interface 1024. The term "computer readable storage media" refers to any non-transitory, non-volatile tangible storage medium that provides recorded instructions and/or data to the computing device 1000 for execution and/or processing. Examples of such storage media include magnetic tape, a compact disc read only memory (CD-ROM), DVD, a Blu-ray(TM) Disc, a hard disk drive, a read only memory (ROM) or integrated circuit, a solid state storage drive (such as a USB flash drive, a flash memory device, a solid state drive or a memory card), a hybrid drive, a magneto-optical disk, or a computer readable card such as a secure digital (SD) card and the like, whether or not such devices are internal or external of the computing device 1200. Examples of transitory or non-tangible computer readable transmission media that may also participate in the provision of software, application programs, instructions and/or data to the computing device 1000 include radio or infrared transmission channels as well as a network connection to another computer or networked device, and the Internet or Intranets including e-mail transmissions and information recorded on Websites and the like.

The computer programs (also called computer program code) are stored in the main memory 1008 and/or the secondary memory 1010. The computer programs can also be received via the communication interface 1024. Such computer programs, when executed, enable the computing device 1000 to perform one or more features of embodiments discussed herein. In various embodiments, the computer programs, when executed, enable the processor 1004 to perform features of the above-described embodiments. Accordingly, such computer programs represent controllers of the computer system 1000.

Software may be stored in a computer program product and loaded into the computing device 1000 using the removable storage drive 1014, the storage drive 1012, or the interface 1040. Alternatively, the computer program product may be downloaded to the computer system 1000 over the communication path 1026. The software, when executed by the processor 1004, causes the computing device 1000 to perform functions of embodiments described herein.

It is to be understood that the embodiment of Figure 10 is presented merely by way of example. Therefore, in some embodiments one or more features of the computing device 1000 may be omitted. Also, in some embodiments, one or more features of the computing device 1000 may be combined together. Additionally, in some embodiments, one or more features of the computing device 1000 may be split into one or more component parts.

It will be appreciated by a person skilled in the art that numerous variations and/or modifications may be made to the present invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects to be illustrative and not restrictive.

The whole or part of the embodiments i.e. example embodiments disclosed above can be described as, but not limited to, the following supplementary notes.

<SUPPLEMENTARY NOTES>
<Supplementary Note 1>
A method for optimizing healthcare institution resource utilisation, comprising:
receiving patient information for at least one patient;
determining, from the patient information:
a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient; and
a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient;
selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation; and
outputting a recommended healthcare institution resource allocation.
<Supplementary Note 2>
The method in accordance with Supplementary Note 1,
wherein the selecting comprises selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement to minimise total healthcare institution resource utilisation.
<Supplementary Note 3>
The method in accordance with Supplementary Note 1, further comprising
receiving a maximum healthcare institution resource availability,
wherein the selecting comprises selecting the respective first anticipated institution resource requirement or the second anticipated institution resource requirement for each patient so that, at any point in time, a time-based anticipated healthcare institution resource utilisation will not to exceed the maximum healthcare institution resource availability.
<Supplementary Note 4>
The method in accordance with Supplementary Note 1,
wherein the one of the first anticipated institution resource requirement and the second anticipated institution resource requirement is selected for each patient so as to minimize the likelihood of readmission of the respective patient.
<Supplementary Note 5>
The method in accordance with Supplementary Note 3,
wherein the selecting comprises:
analysing combinations of first anticipated institution resource requirements and the second anticipated institution resource requirements for respective patients;
identifying at least one combination providing a time-based anticipated healthcare institution resource utilisation that, at no point in time, exceeds the maximum healthcare institution resource availability; and
identifying, from the at least one combination, a combination that provides the minimum total healthcare institution resource utilisation.
<Supplementary Note 6>
The method in accordance with any one of Supplementary Notes 1 to 5, further comprising
receiving a patient condition severity score,
wherein the one of the first anticipated institution resource requirement and the second anticipated institution resource requirement is selected based on the patient condition severity score.
<Supplementary Note 7>
The method in accordance with any one of Supplementary Notes 1 to 6,
wherein the receiving the patient information comprises receiving patient information of a plurality of patients across multiple healthcare institutions.
<Supplementary Note 8>
The method in accordance with Supplementary Note 7, further comprising
receiving a maximum healthcare institution resource availability for each of the plurality of healthcare institutions,
wherein the selecting comprises selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient so a time-based anticipated healthcare institution resource utilisation will not exceed the respective maximum healthcare institution resource availability of each respective healthcare institution.
<Supplementary Note 9>
The method in accordance with Supplementary Note 7, further comprising
receiving a maximum healthcare institution resource availability for each of the plurality of healthcare institutions,
wherein the selecting comprises selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient so a time-based anticipated healthcare institution resource utilisation for all patients will not exceed a total of all the maximum healthcare institution resource availabilities for all of the plurality of healthcare institution.
<Supplementary Note 10>
The method in accordance with Supplementary Note 9,
wherein the outputting the recommended healthcare institution resource allocation comprises identifying one or more patients at one of the plurality of healthcare institutions that are recommended to be moved to another of the plurality of healthcare institutions.
<Supplementary Note 11>
The method in accordance with Supplementary Note 9,
wherein the outputting the recommended healthcare institution resource allocation comprises identifying one or more healthcare institution resources at one of the plurality of healthcare institutions that are recommended to be moved to another of the plurality of healthcare institutions.
<Supplementary Note 12>
The method in accordance with Supplementary Note 11,
wherein the identifying one or more healthcare institution resources at one of the plurality of healthcare institutions that are recommended to be moved to another of the plurality of healthcare institutions comprises recommended at least one of temporary and permanent movement of the respective one or more healthcare institution resources.
<Supplementary Note 13>
A computing system for optimizing healthcare institute resource utilisation, comprising:
receiver means for receiving patient information for at least one patient;
determining means for determining, from the patient information:
a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient; and
a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient; and
selector means for selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation; and
outputting means for outputting a recommended healthcare institution resource allocation.
<Supplementary Note 14>
The computing system in accordance with Supplementary Note 13,
wherein the selector means selects one of the first anticipated institution resource requirement and the second anticipated institution resource requirement to minimise total healthcare institution resource utilisation.
<Supplementary Note 15>
The computing system in accordance with Supplementary Note 13,
wherein the receiver means receives a maximum healthcare institution resource availability, and
the selector means selects the respective first anticipated institution resource requirement or the second anticipated institution resource requirement for each patient so that, at any point in time, a time-based anticipated healthcare institution resource utilisation will not to exceed the maximum healthcare institution resource availability.
<Supplementary Note 16>
The computing system in accordance with Supplementary Note 13,
wherein the one of the first anticipated institution resource requirement and the second anticipated institution resource requirement is selected for each patient so as to minimize the likelihood of readmission of the respective patient.
<Supplementary Note 17>
The computing system in accordance with Supplementary Note 15,
wherein the selector means is configured to:
analyse combinations of first anticipated institution resource requirements and the second anticipated institution resource requirements for respective patients;
identify at least one combination providing a time-based anticipated healthcare institution resource utilisation that, at no point in time, exceeds the maximum healthcare institution resource availability; and
identify, from the at least one combination, a combination that provides the minimum total healthcare institution resource utilisation.
<Supplementary Note 18>
The computing system in accordance with any one of Supplementary Notes 13 to 17,
wherein the receiver means receives a patient condition severity score, and
the one of the first anticipated institution resource requirement and the second anticipated institution resource requirement is selected based on the patient condition severity score.
<Supplementary Note 19>
The computing system in accordance with any one of Supplementary Notes 13 to 18,
wherein the receiver means receives patient information of a plurality of patients across multiple healthcare institutions.
<Supplementary Note 20>
The computing system in accordance with Supplementary Note 19,
wherein the receiver means receives a maximum healthcare institution resource availability for each of the plurality of healthcare institutions, and
the selector means selects one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient so a time-based anticipated healthcare institution resource utilisation will not exceed the respective maximum healthcare institution resource availability of each respective healthcare institution.
<Supplementary Note 21>
The computing system in accordance with Supplementary Note 19,
wherein the receiver means receives a maximum healthcare institution resource availability for each of the plurality of healthcare institutions, and
the selector means selects one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient so a time-based anticipated healthcare institution resource utilisation for all patients will not exceed a total of all the maximum healthcare institution resource availabilities for all of the plurality of healthcare institution.
<Supplementary Note 22>
The computing system in accordance with Supplementary Note 21,
wherein the outputting means identifies one or more patients at one of the plurality of healthcare institutions that are recommended to be moved to another of the plurality of healthcare institutions.
<Supplementary Note 23>
The computing system in accordance with Supplementary Note 21,
wherein the outputting means identifies one or more healthcare institution resources at one of the plurality of healthcare institutions that are recommended to be moved to another of the plurality of healthcare institutions.
<Supplementary Note 24>
The computing system in accordance with Supplementary Note 23,
wherein the outputting means recommends at least one of temporary and permanent movement of the respective one or more healthcare institution resources.
<Supplementary Note 25>
A computer readable medium including computer program code configured to, with at least one processor, cause a computer at least to:
receive patient information for at least one patient;
determine, from the patient information:
a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient; and
a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient; and
select one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation; and
output a recommended healthcare institution resource allocation.

This application is based upon and claims the benefit of priority from Singapore patent application No. 10201609191R, filed on November 2, 2016, the disclosure of which is incorporated herein in its entirety by reference.

100 Method
200 Exemplary workflow
202 Patient
204 Healthcare institute
206 Optimizing system
208 Patient information
210 Gathered patient information
212 Recommended healthcare institution resource allocation
300 Another exemplary workflow
302 Patient
304 Healthcare institute
306 Optimizing system
310 Patient information
312 Recommended healthcare institution resource allocation
314 First group
316 Second group
400 Healthcare institute readmission risk impact assessment
402 Healthcare institute readmission probability
404 Severity of condition
406 Condition A
408 Condition B
410 Condition C
412 Condition D
500 Healthcare institute readmission risk impact and cost assessment
502 Elapsed time
504 Hospital cost
506 Highest
510 Exemplary targeted pool of patients
512 Pool of patients
514 Pool of nurses
516 Hospital cost
518 Hospital cost
520 Hospital cost
522 Hospital cost
524 Hospital cost
600 Method
700 Detailed workflow
702 Healthcare institute
704 Optimizing system
706 Patient information and resource availability
708 Recommended healthcare institute resource utilisation
800 Detailed workflow
802 Healthcare institute
804 Optimizing system
806 Patient information and resource availability
808 Recommended healthcare institute resource utilisation
810 Healthcare institute
812 Patient
900 System
902 Computer
904a Database
904n Database
906 User input module
908 User output module
1000 Computer system
1002 Display interface
1004 Processor
1006 Communication infrastructure
1008 Main memory
1010 Secondary memory
1012 Storage drive
1014 Removable storage drive
1022 Removable storage unit
1024 Communication interface
1026 Communication path
1030 Display
1032 Audio interface
1034 Speaker(s)
1040 Interface
1044 Removable storage medium

Claims

A method for optimizing healthcare institution resource utilisation, comprising:
receiving patient information for at least one patient;
determining, from the patient information:
a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient; and
a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient;
selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation; and
outputting a recommended healthcare institution resource allocation.
The method in accordance with claim 1,
wherein the selecting comprises selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement to minimise total healthcare institution resource utilisation.
The method in accordance with claim 1, further comprising
receiving a maximum healthcare institution resource availability,
wherein the selecting comprises selecting the respective first anticipated institution resource requirement or the second anticipated institution resource requirement for each patient so that, at any point in time, a time-based anticipated healthcare institution resource utilisation will not to exceed the maximum healthcare institution resource availability.
The method in accordance with claim 1,
wherein the one of the first anticipated institution resource requirement and the second anticipated institution resource requirement is selected for each patient so as to minimize the likelihood of readmission of the respective patient.
The method in accordance with claim 3,
wherein the selecting comprises:
analysing combinations of first anticipated institution resource requirements and the second anticipated institution resource requirements for respective patients;
identifying at least one combination providing a time-based anticipated healthcare institution resource utilisation that, at no point in time, exceeds the maximum healthcare institution resource availability; and
identifying, from the at least one combination, a combination that provides the minimum total healthcare institution resource utilisation.
The method in accordance with any one of claims 1 to 5, further comprising
receiving a patient condition severity score,
wherein the one of the first anticipated institution resource requirement and the second anticipated institution resource requirement is selected based on the patient condition severity score.
The method in accordance with any one of claims 1 to 6,
wherein the receiving the patient information comprises receiving patient information of a plurality of patients across multiple healthcare institutions.
The method in accordance with claim 7, further comprising
receiving a maximum healthcare institution resource availability for each of the plurality of healthcare institutions,
wherein the selecting comprises selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient so a time-based anticipated healthcare institution resource utilisation will not exceed the respective maximum healthcare institution resource availability of each respective healthcare institution.
The method in accordance with claim 7, further comprising
receiving a maximum healthcare institution resource availability for each of the plurality of healthcare institutions,
wherein the selecting comprises selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient so a time-based anticipated healthcare institution resource utilisation for all patients will not exceed a total of all the maximum healthcare institution resource availabilities for all of the plurality of healthcare institution.
The method in accordance with claim 9,
wherein the outputting the recommended healthcare institution resource allocation comprises identifying one or more patients at one of the plurality of healthcare institutions that are recommended to be moved to another of the plurality of healthcare institutions.
The method in accordance with claim 9,
wherein the outputting the recommended healthcare institution resource allocation comprises identifying one or more healthcare institution resources at one of the plurality of healthcare institutions that are recommended to be moved to another of the plurality of healthcare institutions.
The method in accordance with claim 11,
wherein the identifying one or more healthcare institution resources at one of the plurality of healthcare institutions that are recommended to be moved to another of the plurality of healthcare institutions comprises recommended at least one of temporary and permanent movement of the respective one or more healthcare institution resources.
A computing system for optimizing healthcare institute resource utilisation, comprising:
receiver means for receiving patient information for at least one patient;
determining means for determining, from the patient information:
a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient; and
a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient; and
selector means for selecting one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation; and
outputting means for outputting a recommended healthcare institution resource allocation.
The computing system in accordance with claim 13,
wherein the selector means selects one of the first anticipated institution resource requirement and the second anticipated institution resource requirement to minimise total healthcare institution resource utilisation.
The computing system in accordance with claim 13,
wherein the receiver means receives a maximum healthcare institution resource availability, and
the selector means selects the respective first anticipated institution resource requirement or the second anticipated institution resource requirement for each patient so that, at any point in time, a time-based anticipated healthcare institution resource utilisation will not to exceed the maximum healthcare institution resource availability.
The computing system in accordance with claim 13,
wherein the one of the first anticipated institution resource requirement and the second anticipated institution resource requirement is selected for each patient so as to minimize the likelihood of readmission of the respective patient.
The computing system in accordance with claim 15,
wherein the selector means is configured to:
analyse combinations of first anticipated institution resource requirements and the second anticipated institution resource requirements for respective patients;
identify at least one combination providing a time-based anticipated healthcare institution resource utilisation that, at no point in time, exceeds the maximum healthcare institution resource availability; and
identify, from the at least one combination, a combination that provides the minimum total healthcare institution resource utilisation.
The computing system in accordance with any one of claims 13 to 17,
wherein the receiver means receives a patient condition severity score, and
the one of the first anticipated institution resource requirement and the second anticipated institution resource requirement is selected based on the patient condition severity score.
The computing system in accordance with any one of claims 13 to 18,
wherein the receiver means receives patient information of a plurality of patients across multiple healthcare institutions.
The computing system in accordance with claim 19,
wherein the receiver means receives a maximum healthcare institution resource availability for each of the plurality of healthcare institutions, and
the selector means selects one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient so a time-based anticipated healthcare institution resource utilisation will not exceed the respective maximum healthcare institution resource availability of each respective healthcare institution.
The computing system in accordance with claim 19,
wherein the receiver means receives a maximum healthcare institution resource availability for each of the plurality of healthcare institutions, and
the selector means selects one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient so a time-based anticipated healthcare institution resource utilisation for all patients will not exceed a total of all the maximum healthcare institution resource availabilities for all of the plurality of healthcare institution.
The computing system in accordance with claim 21,
wherein the outputting means identifies one or more patients at one of the plurality of healthcare institutions that are recommended to be moved to another of the plurality of healthcare institutions.
The computing system in accordance with claim 21,
wherein the outputting means identifies one or more healthcare institution resources at one of the plurality of healthcare institutions that are recommended to be moved to another of the plurality of healthcare institutions.
The computing system in accordance with claim 23,
wherein the outputting means recommends at least one of temporary and permanent movement of the respective one or more healthcare institution resources.
A computer readable medium including computer program code configured to, with at least one processor, cause a computer at least to:
receive patient information for at least one patient;
determine, from the patient information:
a first anticipated institution resource requirement for providing a first current patient treatment for each patient to reduce a likelihood of readmission of the respective patient; and
a second anticipated institution resource requirement for providing a second current patient treatment and treatment upon readmission for each patient; and
select one of the first anticipated institution resource requirement and the second anticipated institution resource requirement for each patient, to optimize healthcare institution resource utilisation; and
output a recommended healthcare institution resource allocation.