USING RESILIENT SYSTEMS INFERENCE FOR ESTIMATING HOSPITAL ACQUIRED INFECTION PREVENTION INFRASTRUCTURE PERFORMANCE
20220391735 · 2022-12-08
Inventors
Cpc classification
G16H40/20
PHYSICS
International classification
Abstract
The present disclosure presents systems and methods for assessing hospital acquired infection reduction strategies. One such method comprises analyzing, by a computing device, a risk of hospital acquired infections, using supervised learning to generate fuzzy set membership rules; assessing resilience based on observed hospital acquired infection risk moderation performance level across a continuum of fuzzy membership sets; and inferring, by the computing device, a performance of a hospital in hospital acquired infection risk factor prevention employing the fuzzy membership set rules. Other systems and methods are also provided.
Claims
1. A method for assessing hospital acquired infection reduction strategies, comprising: analyzing, by a computing device, a risk of hospital acquired infections, using supervised learning to generate fuzzy set membership rules; assessing, by the computing device, resilience based on observed hospital acquired infection risk moderation performance level across a continuum of fuzzy membership sets; and inferring, by the computing device, a performance of a hospital in hospital acquired infection risk factor prevention employing the fuzzy membership set rules.
2. The method according to claim 1, wherein said analyzing risk comprises selecting risk features according to a principal component analysis.
3. The method according to claim 1, wherein said analyzing risk comprises determining a likelihood of exposure.
4. The method according to claim 1, wherein said analyzing risk comprises determining a likelihood of event reversibility.
5. The method according to claim 1, wherein the hospital acquired infection is methicillin-resistant Staphylococcus aureus.
6. The method according to claim 1, wherein the hospital acquired infection is Clostridioides difficile.
7. The method according to claim 1, wherein the hospital acquired infection risk factor prevention is dependent on at least a cost-effectiveness analysis.
8. The method according to claim 1, wherein the hospital acquired infection risk factor prevention is dependent on at least a patient safety analysis.
9. The method according to claim 1, wherein the risk of hospital acquired infections is analyzed with respect to at least risk event identification, risk mitigation, and risk prevention.
10. The method according to claim 1, wherein the resilience is assessed with respect to an ability of a hospital to anticipate, avoid, and manage hospital acquired infections.
11. The method according to claim 1, wherein said analyzing risk of hospital acquired infections comprises use of fuzzy cognitive mapping to increase reliability and validity of risk mitigation strategies.
12. The method according to claim 1, wherein said analyzing risk of hospital acquired infections comprises use of fuzzy cognitive mapping to assess a stability of a risk event.
13. The method according to claim 1, wherein said analyzing risk of hospital acquired infections comprises use of fuzzy cognitive mapping to assess a reversibility of a risk event.
14. A method for assessing hospital acquired infection risk, comprising: determining, by a computing system, fuzzy inference system rules based on resilience inference fuzzy membership categories dependent on at least fuzzy risk capacity, resilience capacity, and performance safety; receiving, by the computing system, information about a hospital acquired infection risk and hospital acquired infection resilience membership function parameters; and predicting, by the computing system, specific hospital acquired performance safety outcomes by employing a fuzzy inference model dependent on the fuzzy inference system rules and the receiving information.
15. The method according to claim 14, wherein said hospital acquired infection risk is derived though machine learning and fuzzy cognitive mapping.
16. The method according to claim 14, further comprising using fuzzy cognitive mapping to assess a stability of a hospital acquired infection risk event.
17. The method according to claim 14, further comprising using fuzzy cognitive mapping to assess a reversibility of a hospital acquired infection risk event.
18. A system for assessing hospital acquired infection risk, comprising: at least one server computing device; and at least one application executable in the at least one server computing device, wherein when executed the at least one application causes the at least one server computing device to at least: determining, by a computing system, fuzzy inference system rules based on resilience inference fuzzy membership categories dependent on at least fuzzy risk capacity, resilience capacity, and performance safety; receiving, by the computing system, information about a hospital acquired infection risk and hospital acquired infection resilience membership function parameters; and predicting, by the computing system, specific hospital acquired performance safety outcomes by employing a fuzzy inference model dependent on the fuzzy inference system rules and the receiving information.
19. The system of claim 18, wherein said hospital acquired infection risk is derived though machine learning and fuzzy cognitive mapping.
20. The system of claim 18, further comprising using fuzzy cognitive mapping to assess a stability of a hospital acquired infection risk event and to assess a reversibility of a hospital acquired infection risk event.
Description
BRIEF DESCRIPTION OF THE FIGURES
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[0120] MRSA; resilience μ.sub.F(X): decolonization regimen post discharge.
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DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0126] Due to the increasing complexity of systems in safety-critical organizations like acute care health systems, there is an urgent and growing need for anticipatory rather than reactive operational performance response. The resilience inference methodology demonstrate value in interpreting regional, environmental, and demographic risk and operational resilience factors that are relevant in HAI prevention in acute care environments. This approach to Resilience Inference, which uses both data analysis and fuzzy inference techniques, assists health systems and population health stakeholder groups in understanding factors in potential patient catchment areas that are related to infectivity risks. Like state health safety requirements, regionally specific healthcare design and building codes, and demographically specific population health improvement efforts this information is instrumental in creating more resilient healthcare infrastructure tailored to community safety and risk mitigation priorities.
[0127] The present technology includes: Risk Analysis: using supervised learning techniques on regional data, and Fuzzy Logic to operationalize the combined processes of: Risk Event Analysis (processes of Risk Event Identification and System Likelihood of Hazard Exposure Determination); Risk Mitigation Evaluation (Risk Parameter Assessment, Likelihood of Risk Exposure Stability, and Risk Event Reversibility); and Risk Prevention Capacity (attribution of the level of system HAI hazard prevention capacity based on Risk Event exposure and Risk Mitigation opportunity); Resilience Assessment: how to measure the Resilience Repertoire of a system which includes a system's Risk Management Inventory, Risk Avoidance Resources, and Resilience Strategy Assessment based on their observed HAI risk moderation performance level across a continuum of Fuzzy Membership sets; and Performance Inference: the process for evaluating the impact of resilient procedural inventory and system resource performance capability based on regional health system HAI risk factor prevention capacity using applied Fuzzy Inference Systems. An illustration of the Resilience Inference methodology process explanation segments is delineated in
[0128] Mathematically derived predictive frameworks may be defined to identify the boundaries of system risk and its impact on system resilience behavior, and test for potential origins and types of hazardous events or risk factors that can then facilitate experimental comparisons of system resilience interventions (Iran et al., 2017). In the scenario of understanding HAI risk, a macro-ergonomic, perspective situates systems rules and procedures in a broader operational context (Carayon et al. 2013). This approach analyzes which variable groupings external to health systems scope of control may have the most significant impact on overall safety outcomes in health system infection control. The resultant information is then be used to guide health system operational process or policy decisions that relate to safer care delivery.
[0129] The process of analyzing system risk is usually expressed as an index that involves the quantification of the components of hazard exposure effect. (Linkov et al. 2014). Its use is to understand what external community infectivity hazards factors impact internal hospital infection exposure and how these relate to CDI and MRSA HAI effects. This formula can be expressed within the Risk Analysis framework to ascertain specific health system resources HAI Risk Mitigation capacity. The results of system capacity calculations are a first step in evaluating the potential impacts infectivity risk reduction interventions may have on health systems' resiliency to HAI. For this analysis representative numerical approximations based on High Stability, Moderate Stability, and Low Stability as well as Easily Reversible, Somewhat Reversible, and Difficult to Reverse can be used to define condition stability and condition reversibility. An illustration of this approach as it applies to CDI and MRSA HAI is delineated Table 1.
TABLE-US-00001 TABLE 1 Risk Analysis operationalization framework Risk Event Evaluation Risk Mitigation Evaluation Hazard Event Likelihood of exposure Condition Stability Condition Reversibility Risk Prevention Capacity CDI HAI Somewhat High High Stability Easily Reversible Somewhat Low MRSA HAI High Moderate Stability Somewhat Reversible Low Very High Low Stability Difficult to Reverse Very Low
[0130] Often a comprehensive and systematic evaluation of risk factors reveals that safety-critical systems must demonstrate the ability to operate effectively and with performance continuity outside its formal design-basis (Furniss et al. 2010).
[0131] Supervised Learning data analysis techniques manifest relationships between variables that are sometimes perceived as disparate within complex systems. Such an approach offers better ability to calculate both emergent areas of risk as well as the efficacy of responses to potential infection hazard and risk. The process of Supervised Learning analysis can be applied retrospectively on large repositories of readily available data in an automated manner. The application of Fuzzy Logic to systems control specializes in dealing with uncertainty and imprecision. In the context of engineered systems, differences in system performance abilities can be represented through differing fuzzy membership functions relevant to risk mitigation and resilience, attributes critical to each structural element (Muter, 2012).
[0132] Fuzzy logic is used in concert with resilience assessment techniques by using linguistic terms to rank both a risk prevention and avoidance capacity: A risk mitigation framework may be constructed that draws on systems subject matter expertise to understand the means and setting of system operation. Modes of system operation in sociotechnical contexts, like healthcare delivery, can be described in part by condition stability and reversibility (Sikula et al., 2015). Using the qualitative information derived from the system expert's subject knowledge to understand system contextual performance is often vague and nuanced in its interpretation. Work that has proposed an applied fuzzy theory to apportion risk categorization has proven effective in introducing a way to more easily quantify risk levels defined through linguistic variables and thus measure subjectively defined performance attributes (Chang & Cheng, 2010). One strength of fuzzy logic is that it is able to draw in semantic expressions of experts to derive operable rules. An example of HAI Risk Prevention capacity levels can be described along a continuum of representative Triangular Fuzzy Number (TFN) and Trapezoidal Fuzzy Numbers (TrFN) that define membership categories as illustrated by
[0133] There are validated advantages of tapping into specific areas of SME to define the boundaries of fuzzy probabilistic risk and adaptive response to hazard models for the analysis of the interaction between human activity and socio-technical systems (Konstandinidou et al., 2006; Hollnagel, 1998). Furthermore, fuzzy categories of risk defined by natural language along a multidimensional scale that considers degrees of likelihood and conditions of occurrence offer a way to introduce greater quantitative rigor in hazard occurrence prediction. Integrating least square and statistically derived feature selection ranking methods offer the potential for generating more accurate fuzzy classifications and greater robustness against analysis uncertainty (Zhang & Chu, 2011).
[0134] Assumptions that risk categorizations are equally weighted can lead to an oversimplification of system abilities and incorrect inferences regarding system risk and performance safety (Chang & Cheng, 2010). Employing statistical methods such as Ordinary Least Squares (OLS) improves the accuracy of analysis model variable matching and the likelihood of relational outcomes by comparison of overall system fit. OLS has demonstrated suitability in offering higher specificity to the weighting of different system risk categories (Cheung, 2007). It also has well-established precedence of being used in comparing overall historical patterns, in health systems risk analysis (Fuller et al. 2016). Used iteratively and continuously validated augmented risk management techniques can offer a viable pathway for system resilience building (Sikula et al., 2015). This extension of Risk Analysis by using Supervised Learning, OLS likelihood metrics and Fuzzy Logic for hazard identification, exposure, effect partitioning, and weighting, and risk mitigation capacity potential lays the foundation for infection prevention strategy development and resilience efficacy testing.
[0135] Data Sources: Separate data on regional population demographical characteristics such as population by states that were over the age of sixty-five (65), state population density; and urban and rural designated proportion by U.S. state were accessed and downloaded. Additionally, living conditions that necessitate more than one-person occupying a room on a full-time basis are designated as “over-crowded” by the U.S. Department of Housing and Urban Development (Blake et al. 2007). This specific aspect of population density was considered necessary since community survey data on overcrowding in housing has been specifically associated with increased incidences in community-onset MRSA (Immergluck et al., 2019; See et at 2017). Datasets available from USICH on the number of homeless adults in 2017 were accessed and downloaded. Additionally, HRSA's portal on medically underserved health service areas (MUA) and medically underserved population (MUP) were also downloaded.
[0136] According to the HRSA, the difference between these two designations is that MUAs are identified as regions that have a shortage of primary care health services for residents within a geographic area, such as (Description of “Medically Underserved Areas and Populations” (MUA/Ps): HRSA, 2016): a whole county; a group of neighboring counties; a group of urban census tracts; and a group of counties or civil divisions. MUPs are related to MUA but are representative of persons rather than geographic areas. MUP represents a count of specific sub-groups of people living in a defined geographic area with a shortage of primary care health services. These groups may also be identified as particular populations that struggle with economic, cultural, or linguistic barriers to health care. Examples include, but are not limited to, those who are (Description of “Medically Underserved Areas and Populations” (MUA/Ps): HRSA, 2016): homeless; low-income; Medicaid-eligible; Native American; and migrant farmworkers.
[0137] This data was then organized into subsets as delineated by U.S. Census regions. These Census groupings subdivide the continental U.S. and the states of Alaska, Hawaii and the District of Columbia into four (4) geographic areas for presentation of population census data. These regions are demarcate) as follows (Mackun, et al. 2011): North East (NE); South (SE) (Includes Washington D.C.); Midwest (MW); and West (WE) (includes Alaska and Hawaii). This effort was made to balance the population and to ensure regional data, as opposed to U.S. state-based data, was the geographic factor driving the analysis. For example, individual states such as New York and California have more numerous residents in general and cities with higher population densities than other states within their geographic regions such as Maine and Idaho. Additionally, states with geographies like Alaska are designated as almost 100% “Rural,” and Washington D.C. is designated as 100% urban.
[0138] CDC's National Healthcare Safety Network (NHSN) data on State-specific observed HAI incidence rates in hospital settings was also downloaded. It was determined that data to be used for this analysis are constrained to only the most current inpatient observed amounts of CDI and MRSA HAI that had been documented within a single year (e.g., 2017). Furthermore, since Acute Care Hospitals had the highest percentage and most consistent health system reporting the decision was also made to constrain data to this environment of care setting. The United States Virgin Islands, and territories of Puerto Rico and Guam were not included due to the lack of available reported data on observations of hospital-onset CDI and MRSA in acute care settings.
[0139] Data Analysis: Supervised learning methods can be used to make predictions about variables with known outcomes when the specific values of input variables are also identifiable (Obenshain, 2004), in this study, the outcome or dependent variables are known and indicated in the CDC data. Namely: Hospital-onset CDI specific incidence rates in acute care settings; Hospital-onset MRSA specific incidence rates in acute care settings. Furthermore, the values of the input or independent variables were also indicated in the data, which included the data subset categories of: Regional Population; Population over the age of 65; Number of Homeless Adults; People per Square Mile (i.e., Population Density); Number of People living in crowded homes; Medically Underserved Areas (MUA) by region; Medically Underserved Populations (MUP) by region; Amount of region designated as “Rural”; and Amount of region designated as “Urban”.
[0140] In Supervised Learning analysis procedures and observed outputs are part of the training data that augments reliability in testing outputs (James et al, 2013). Of the Supervised Learning techniques used in conjunction with Fuzzy Logic, OLS is both robust and mathematically well established (Guillaume, 2001). This method has also proven useful in selecting the most influential factors and firing strengths for input membership functions which have also helped guide Fuzzy Inference System Rule formation (Cheung, 2007; Destercke et al. 2007).
[0141] A critical aspect of ensuring efficiency and accuracy of Supervised Learning techniques is feature selection (James et al., 2013). Both data preprocessing, feature selection and parameter tuning or finding the best combination of parameters have a significant impact on data analysis performance that can surpass the importance of the actual choice of the analysis outcome classification model (Ehrentraut et al., 2012). Feature selection in the data for membership weights and to inform fuzzy rule formation was done by using “Pearson's r” for determining significant correlations between CDI and MRSA HAI and independent regional geographic and demographic data subsets. This method has proven suitable for feature selection in the research of supervised learning outcomes reliability. Pearson's r as a filter method for data preprocessing, estimate generalization, and to remove irrelevant attributes before induction occurs, has also demonstrated the improvement of function validity (Weston et al., 2001).
[0142] For certain types of characteristics, even vast quantities of data representative of large populations are frequently not perfectly normally distributed, which was the case with much of the open-source data obtained as described above. The benefit of using OLS for data analysis is that standard proof of the unbiasedness of its estimates does not require the assumption of data distribution normality or constant variance (Cheung, 2007). According to research on OLS regression techniques, one reason these methods are ubiquitous in their use is because of them being more robust than other methods of statistical analysis against violations of normality and providing unbiased, efficient and consistent estimators in most situations (Habeck & Brickman, 2018). When data is not normally distributed, the mean of the analysis outcome may or may not be a goal measure of central tendency but maybe a suitable indicator of the proportion of risk and representative risk differences between variables (Cheung, 2007). This approach to analyzing RAI risk data could provide a viable way of interpreting CDI and MRSA Risk Prevention capacity. The weighting of Risk Event potential outcomes using the information provided as a result of applied. OLS regression therefore improves the accuracy of insight into the fuzzy levels of Risk Prevention capacity based on the estimated significance of relationships between dependent and independent variables.
[0143] Reframing Risk Analysis Hypotheses as Fuzzy Inference Rules: Orthogonal transformation methods can also provide a vehicle for budding Fuzzy Inference System rules from a limited subset of data relationships deemed to be statistically meaningful. OLS has been used to create rules from a set of training data by selecting those most important though linear regression techniques (Destercke et al., 2007). The generation of rule formation is a critical component in the development of Fuzzy Inference System informed strategies to mobilize resilient response goals. This rule appears to be especially true in circumstances where human sentience plays a critical role in systems resilience potential such as healthcare (Anooj, 2012; Leite et al., 2011). In using a Fuzzy Inference System for the development of performance safety estimation for infection control, it is helpful to establish MAX and MIN vectors that serve as the basis for decision rules that can specify the risk and resilience level for health system infrastructure based on a set of numerical variables. These decision support rules can aid system analysts in more accurately diagnosing and mitigating risk factors and integrating reliable resilience resources for moderating the effects of risk (Anooj, 2012).
[0144] Reframing the Risk Analysis hypotheses as Fuzzy IF-THEN rules, for example, based on the information obtained in the comprehensive literature review regarding CDI and MRSA risk factors, hypotheses derived membership categories are as follows (dependent and independent variables have been italicized): IF region Population numbers are large, THEN both CDI and MRSA HAI Likelihood of Exposure is High; IF region Population numbers above 65 years old are great, THEN CDI HAI Likelihood of Exposure is High; IF region Homeless Populations are large, THEN MRSA HAI Likelihood of Exposure is High; IF regional area Population Density Proportion is high, THEN MRSA HAI Likelihood of Exposure is High; IF regional area Household Crowding Proportion is high, THEN MRSA HAI Likelihood of Exposure is High; IF region MUA Amount is large, THEN CDI and MRSA HAI Likelihood of Exposure is High; IF region MUP Amount is large, THEN CDI and MRSA HAI Likelihood of Exposure is High; IF region Rural Designated Area is large, THEN CDI HAI Likelihood of Exposure is High; IF region Urban Designated Area is large, THEN MRSA HAI Likelihood of Exposure is High; IF regional Geographical Location is in a certain area in the continental U.S., THEN CDI or MRSA HAI Likelihood of Exposure is High; IF regional MRSA HAI incidence rates are high, THEN CDI HAI Likelihood of Exposure is High; and IF regional CDI HAI incidence rates are high, THEN MRSA HAI Likelihood of Exposure is High.
[0145] Resilience Assessment: Incidences of using statistical analysis and fuzzy logic for analyzing risk is extended to evaluate systems' adaptive capacity performance. System resilience assessment can also employ the use of fuzzy linguistic variables to express the relative importance of identifiable operational weighted resilience factors in a broader systemic context to identify the relational synergies between system elements (Tadić, et al., 2014).
[0146] The linguistic variables that define the performance qualities of resilient systems are both vague and context-specific (Dubois, 1980); in other words, resilience capability measures could in and of themselves be considered Fuzzy Sets. Furthermore, system resilience technical performance variables could also be reasonably linked to the fuzzy constructs of plausibility in performance reliability and belief in systems fitness for compensatory procedures. These conditions create a need for considering the possibility for system adaptive capacity and necessity for active resilience (Klir & Yuan, 1995). This occasion exemplifies both the opportunity and challenge for preemptively identifying and developing ways to measure the effects of resilience response concurrently to defined risk prevention capacity.
[0147] The same approach to defining Risk Prevention capacity categories of Somewhat Low, Low, and Very Low can be generalized into Resilience Potential capability. Using the nested groups that define the performance of a system's resilience repertoire lend themselves almost naturally to this effort. Each of these characteristics can be translated into fuzzy membership functions for system risk response capabilities to external variable impact. The memberships for these resilience nested attributes can also be represented by a combination of TFN membership functions (i.e., T-norms), and TrFN membership function (T-conorms).
[0148] The same approach can also be used to construct overlapping the membership categories of performance output variables that can be aggregated to define a specific resilience output capability based on fuzzy input variables. These categories are grouped by their perceived percentage level of systems resilience performance, and then apportioned into associated resilience performance membership functions [μF(x)].
[0149] An obstacle to reliably evaluating system response resilience is the current deficit of data related to this area. This barrier is especially true for HAI incidence “lessons learned.” This issue is because processes that enable organizationally specific HAI incidence rate improvement or information regarding known internal failure point moderation within systems are rarely if ever made public. However, there is a burgeoning number of discrete case study research articles that can be mined for this purpose. This data on resilience performance perhaps lacks the external validity and accuracy of the supervised learning approaches that can be applied to risk-related information contained in vast publicly available data repositories. However, it does present a way to capture and use HAI event-specific improvement metrics and consider them within a broader framework of infection prevention operational resilience within health systems. Aggregated rules reliant on risk mitigation and resilience capability level allow for the construction of a set of conjunctive system of rules. Safety potential to infer system performance safety is inferred where both conditions are jointly satisfied (Ross, 2009).
[0150] Evaluating HAI resilience in healthcare settings often presents the need to estimate systems performance in the context of vague and stochastic circumstances such as infection control “Safety” and healthcare environment “Infectivity” level. Using case study derived performance data, resilience capability potential is assigned based on a comparison of the effectiveness of HAI impact reduction strategies. Speculative performance data could then be predicted using Fuzzy Logic membership functions as a basis for parametric evaluation of a consequent resilience metric from a given case study with an associated fuzzy membership set metric. Classification of specific resilience interventions may be organized according to the strategy level categories outlined in the Resilience Inference Model. This develops insight into what types of health systems infrastructure enhancements may offer the best performance safety outcomes related to specific HAI (e.g., CDI, MRSA, or both). It also helps to better understand the level of effort and investment a particular type of HAI resilience intervention required for achieving a certain level of infection prevention performance safety.
[0151] More generally, the present invention provides a paradigm for use of uncontrolled or non-experimental data, social science literature, and other expert or scholarly literature, in controlling investments and policies derived from implication rather than proven cause and effect. While the particular data employed herein relates to HAI, and the results applied in that arena, the methodology is not so limited, and rather exploits the resilience inference model.
[0152] An example of how fuzzy resilience membership level assignment is assigned based on HAI reduction performance derived from case studies is outlined in Table 2.
TABLE-US-00002 TABLE 2 Quantified HAI Resilience Potential Capability HAI Resilience Strategy HAI Reduction Resilience Level Theme Relevance Strategy Employed Outcome Potential Physical CDI Self-disinfecting copper 78% Resilient Systems reduction in CDI Predictive or MRSA Feedback about related 31% Somewhat Preventative preventable factors reduction in Strong/Strong Feedback MRSA Adaptive MRSA Post-discharge hygiene plus 30% Somewhat Response or decolonization regimen training reduction in Strong/Strong Capacity MRSA Risk Prevention CDI Multidisciplinary HAI reduction 42.7% Strong Panarchy oversight of clinical & reduction in environmental operations & CDI hand
[0153] In the application of resilience engineering analysis and strategy formation, the management of membership function values is facilitated through the integration of fuzzy set theory. The construction and comparison of conjoined membership functions lead to the generation of a set of fuzzy rules (Anooj, 2012). This proposed method is predicated on the building of fuzzy modalities, which allows for the creation of fuzzy values from a predefined set of quantitative controls. The parameters of each of the membership function category are defined to determine HAI Performance safety. The Risk Prevention classification uses the Resilience Inference model as a guide and is based on hazard event parametric quantification as wet as being intrinsically linked to a mitigation mode of operations. The resilience potential levels are derived from the evidence-based performance capabilities previously explained as they relate to specific HAI reduction. Using the Risk and Resilience fuzzy membership levels allow inputting these two disjoint functions into an analysis software platform capable of computing and running multiple tests on variable input and output combinations. The numerical parameters for each of the Resilience Inference component membership categories are diagramed in Table 3.
TABLE-US-00003 TABLE 3 Resilience Inference Fuzzy Membership Categories Risk Prevention Resilience Potential Performance Safety OLS Level μF(x) μ.sub.F(x) μ.sub.F(x) p < .05 Somewhat Low 0.60-1.0 Resilient .65-1.00 Very Safe 0.60-1.0 p < .01 Low 0.30-.70 Very Strong .45-.75 Safe 0.30-.70 p < .001 Very Low 0-.40 Strong .25-.55 Somewhat Safe 0.00-.40 Somewhat Strong .sup. 0-.35
TABLE-US-00004 TABLE 4 Fuzzy Rule Basis for Resilience Inference and HAI Performance Safety 1 IF Risk Prevention is Very Low AND Resilience is Somewhat Strong THEN Performance Safety is Somewhat Safe. 2. IF Risk Prevention is Very Low AND Resilience is Strong THEN Performance Safety is Safe. 3. IF Risk Prevention is Very Low AND Resilience is Very Strong THEN Performance Safety is Safe. 4. IF Risk Prevention is Very Low AND Resilience is Resilient THEN Performance Safety is Safe. 5. IF Risk Prevention is Low AND Resilience is Somewhat Strong THEN Performance Safety is Safe 6. IF Risk Prevention is Low AND Resilience is Strong THEN Performance Safety is Safe. 7. IF Risk Prevention is Low AND Resilience is Very Strong THEN Performance Safety is Very Safe. 8. IF Risk Prevention is Low AND Resilience is Resilient THEN Performance Safety is Very Safe. 9. IF Risk Prevention is Somewhat Low AND Resilience is Somewhat Strong THEN Performance Safety is Safe. 10. IF Risk Prevention is Somewhat Low AND Resilience is Strong THEN Performance Safety is Very Safe. 11. IF Risk Prevention is Somewhat Low AND Resilience is Very Strong THEN Performance Safety is Very Safe. 12. IF Risk Prevention is Somewhat Low AND Resilience is Resilient THEN Performance Safety is Very Safe.
[0154] The potential efficacy of HAI resilience interventions on estimated HAI risk prevention levels are analyzed in order to estimate HAI performance safety outcomes. Fuzzy Inference Systems provide insight into the potential expected safety outcomes based on Risk and Resilience inputs: The risk analysis may be extended to assertions based on combined inference to create the foundational rule basis that the Fuzzy Inference System uses for defuzzifying inputs to create a crisp output of estimated safety. However, these rules are meant for HAI safety inferential purposes only. For actual validation of resilience, real-world testing with a control group is used to determine safety outcomes.
[0155] The analysis approach combines the use of supervised learning data analysis and Fuzzy Logic and Fuzzy Inference Systems as the primary methods used in operationalizing a HAI Resilience Inference methodology. The selection of these methods was based on their precedent combined utilization for risk analysis and increasing use in extending Resilience Assessment frameworks. Analysis included observations of CDI, and MRSA HAI in acute care settings in the U.S. CDI and MRSA observed HAI in acute care settings were selected because of their escalating risk prevalence and AMR concerns, documented impact on hospital reimbursement, as well as the availability of third-party validated (e.g., CDC) incidence data.
[0156] The sourcing of data considered date alignment for information whenever possible. Careful observation of this practice was engaged for fluctuating data with the real potential to change significantly on an annual basis. Additionally, even though OLS regression does not specifically require it, the removal of outliers from independent variable sets was performed to improve distribution normality and ultimately outcome inference generalizability. Although this reduced the final number of observations that is used for training and testing data in the OLS, it was a factor to address for Pearson's r feature selection and was therefore also carried through to regression to determine variable relationship strengths. The increasing of distribution normality was also the rationale for the grouping of independent variables by region as opposed to states.
[0157] The system tools used for data analysis feature selection and regression was performed on Python Anaconda Navigator in Jupyter Notebooks using the following import modules: Seaborn; dumpy; Matplotlib.pyplot; Pandas; Statistics; Scipy.stats: norm; Sklearn: LabelEncoder; LinearRegression; StandardScaler; and Statsmodels. MATLAB R2019a Fuzzy Logic Designer toolbox was used for the development of fuzzy membership parameters for all Resilience Inference input and output components. This system was also used for the integration of all fuzzy inference system rules and simulated outcomes.
[0158] The rising demand for increased accountability in safety-critical system performance in many high-risk industries predicates the necessity for improved reliability of systems' performance safety. This potential growth trajectory will be aided greatly by contextual resources such as machine learning which could help to facilitate this method of predictive analysis and make it more efficient to apply (Anooj, 2012).
[0159] An applied supervised learning and fuzzy inference approach is used for the evaluation of readily available and open-source data. Information on U.S. based-regional population, demographic, and healthcare access data is compared to national incident reporting on HAI caused by MRSA and C. difficile bacteria. The analysis suggests that meaningful relationships between U.S.-based geographic risk factors and dangerous pathogens causing HAI can be derived using these methods. Analyzing regional demographic and environmental data could aid U.S. health systems in more effectively predicting and thus proactively preventing the incidence of HAI in their patient populations in an acute environment of care setting.
[0160] Data Preprocessing for Analysis Preparation: Organization of data in comma-separated value (CSV) files, as well as a preliminary review and preprocessing of data distribution, was performed using Python data analysis for U.S. state and region. State population numbers were used as a baseline for HAI incidence rates analysis. U.S. geographic regions are defined by the states that comprise them, and more people per region are directly related to more observed incidences of CDI and MRSA HAI. An evidence-based intuitionistic “Risk Event” evaluation degree of likelihood matrix was created.
TABLE-US-00005 TABLE 5 Risk Event Evaluation Pre-Analysis Likelihood Assignments Risk Likelihood IF/THEN CDI MRSA Population High Likelihood High Likelihood >65 Years Old High Likelihood Homeless High Likelihood Density High Likelihood Crowding High Likelihood MU_HSA High Likelihood High Likelihood MUP High Likelihood High Likelihood Rural High Likelihood Urban High Likelihood U.S. Geography High Likelihood High Likelihood DII High Likelihood MRSA High Likelihood
[0161] Table 5 represents the research hypothesis questions rewritten as IF/THEN rules and then organized in a matrixed format. Reframing the research question hypotheses in this manner provides a basis for building up to and validating the items comprising the Fuzzy Rule Basis for Resilience Inference as represented by HAI Performance Safety estimates. This information is indicated in Table 4 through supervised learning analysis methods. However, Table 4 was set up only as an illustration of presumptions before actual quantitative analysis. Likelihood scale assignment was resultant from a review of precedent research because there was no basis for comparison of these types of regional risk event probabilities before the data analysis. Given this scenario, all variables were equally likely at a medium range of a scale of “high” likelihood. These relationships of independent variables to the dependent variables of CDI and MRSA were then updated after each stage of Risk Analysis (e.g., feature selection through Person's r and Ordinarily Least Squares Regression). Categorical numerical codes were assigned to each U.S. Census-defined region in the continental U.S. This was done primarily for consistency and to dictate dummy variable coding assignment for the Ordinarily Least Squares (OLS) regression. The original data for the entire U.S. regional population Mean, Median, and Standard Deviation are as follows (as represented by Total State Population/10,000): U.S. State Population Observations: N=51; μ=638.67; M=445.42; σ=724.47. A distribution plot of the population data of all 50 states and the District of Columbia (N=51) confirmed a positively skewed and high variance distribution of data. Additionally, when viewed as a boxplot diagram by designated U.S. Census regional groupings, the distribution of these subsets of data showed several state-based outliers in populations in the West (4), Southeast (3), and Midwest (2) groups. The Northeastern region (1) data contained no such population outliers.
[0162] Plots of other independent variable data also indicated a high degree of skewness and leptokurtic distributions due to the presence of outliers. This information included state-based density variables of people per square mile (i.e., population density) and overcrowding conditions in housing. Additionally, Medically Underserved Areas (MUA) had a positive skew due to regional outliers. Finally, the independent variable of regional areas designated as “Rural,” and “Urban” were highly negatively and positively skewed, respectively. This outcome was unsurprising since according to the U.S. Census Bureau, only 3% of the entire land area of the U.S. is considered “Urban”. Rural designated areas comprise 97% of the geographic area of the U.S., but only 19.3% of the population lives in these areas (Ratcliffe et al., 2016). The individual distributions of independent variables with high levels of skewness and leptokurtic distributions due to the presence of outliers.
[0163] Understanding that improving the normality of the data distribution was necessary not only for primarily feature selection but also to augment the accuracy of predicting common regional HAI risk factors, state-based high-density population outliers were removed. Trimming states with very low populations was also done to improve data generalizability. States with much higher than average levels in housing overcrowding and levels of MUA were removed from the regional analysis sample taken from the U.S. population. Finally, trimming states that were almost 100% rural such as Alaska and Wyoming and areas that were 100% urban such as Washington, D.C. was also done to improve data normality. The trimmed data for the U.S. regional population Mean, Median, and Standard Deviation after outliers were removed from all independent variables with highly skewed distributions are as follows (as represented by Total State Population/10,000): Trimmed U.S. State Population Observations: n=30;
[0164] Although the sample of U.S. regions included only 30 states and districts rather than the original 51, improving data normality was imperative for using Pearson's r correlation for feature selection. The aim of selecting features was to include only those that were the most statistically viable. These remaining variables are then used in the OLS regression model that compares CDI and MRSA HAI incident rates with regional and demographic risk factors. Additionally, outcomes from a representational sample of U.S. regions were obtained that offer insight into significant geographic, environmental, and population factors that demonstrated statistically meaningful relationships with CDI and MRSA HAI incident rates in acute care settings. Even feature selection methods that were tenable with highly skewed samples of U.S. data would have eroded the intent of assumption generalizability.
[0165] It is important to understand apparent relationships that exist between geographic demographics and health access characteristics across the U.S. (e.g., Regional Populations, Rural/Urban Designation Proportion, Populations of comprised of many older adults, Medically-Underserved Areas and Population, et al.) and the number of observations of HAI caused by specific pathogens (e.g., C. diff. and MRSA) in acute care settings to establish an appropriate model for HAI regional risk analysis. A Pearson's r correlation coefficient was computed to assess the relationship between each regional predictor and HAI target variable to evaluate contextual correlations between these factors.
[0166] An initial baseline Pearson's Correlation of predictive variable of the regional population was performed to establish a relationship based on the assumption, that more people per collective state-based region is directly related to more people infected by CDI and MRSA HAI did indeed exist. Overall, there was a strong, positive correlation between regional population and both observed CDI and MRSA HAI as indicated in
[0167] The same Pearson's correlation process was used for comparison of the independent variables with the type of HAI (e.g., CDI, MRSA or both) that had been cited in the literature review as potential risk factors for these two specific HAI. These independent variables included the following: Population over the age of 65; Number of Homeless Adults; People per Square Mile (i.e., Population Density); Number of People living in crowded homes; Medically Underserved Areas (MUA) by region; Medically Underserved Populations (MUP) by region; Amount of land area of region designated as “Rural”; and Amount of land area of region designated as “Urban”.
[0168] Additionally, CDI incident rates were compared to MRSA rates, and vice versa, to ascertain meaningful relationships between variables. Comparison of the four regions (i.e., Northeast, Midwest, Southeast, and West) was excluded from feature selection, as these are categorical variables and thus inappropriate for a Pearson's r correlation statistical inference method. The results of the Pearson's correlation between dependent and independent variables are indicated in Table 6. The organization of this information has been ordered in a manner that relates these outcomes directly back to the research question hypotheses.
TABLE-US-00006 TABLE 6 Pearson's r of relational CDI & MRSA HAI env. and demographic factors Hypothesis Correlation r alpha Baseline Higher numbers for a regional population are associated with higher Y 0.97 1.50E−18 numbers of CDI incidences Higher numbers for a regional population are associated with higher Y 0.85 2.20E−09 numbers of MRSA incidences Question 1: What is the relationship between regional population compositional factors and the risk for AMR hospital-onset infectivity? Hypothesis 1a: U.S. regions with a larger proportion of their populations Y 0.98 8.7E−22 over the age of 65 have an increased risk for incidents of hospital-onset CDI. Hypothesis 1b: U.S. regions with higher populations of homeless Y 0.39 0.032 persons have an increased risk for hospital-onset MRSA. Hypothesis 1c. i.: U.S, regions with higher populations densities have Y 0.41 0.026 an increased risk for hospital-onset MRSA. Hypothesis 1c. ii: U.S. regions with higher populations of housing over- Y 0.62 0.00003 crowding have an increased risk for hospital-onset MRSA. Question 2: How does healthcare accessibility appear to effect and the risk for AMR hospital-onset infectivity? Hypothesis 2a. i: U.S. regions with more Medically Undeserved Areas Y 0.59 0.00054 (MUA) have an increased risk for incidents of hospital-onset CDI. Hypothesis 2a. ii: U.S. regions with more Medically Undeserved Y 0.36 0.048 Populations (MUP) have an increased risk for incidents of hospital- onset CDI. Hypothesis 2b. i: U.S. regions with more Medically Underserved Y 0.71 .sup. 2E−05 Areas (MUA) have an increased risk for hospital-onset MRSA. Hypothesis 2b. ii: U.S. regions with more Medically Underserved Y 0.37 0.043 Populations (MUP) have an increased risk for incidents of hospital- onset MRSA. Question 3: How does the rural or urban status of health systems patient catchment area affect the risk for AMR hospital-onset infectivity? Hypothesis 3a: U.S. regions with more “rural status” defined areas N −0.90 1.6E−11 have an increased risk for incidents of hospital-onset CDI. Hypothesis 3b: U.S. regions with more “urban status” defined areas Y 0.90 1.9E−11 have an increased risk for incidents of hospital-onset MRSA Question 4: What is the relationship between U.S. geographic region and the risk for AMR hospital-onset infectivity? Hypothesis 4a: There is a relationship between U.S. regional NA NA NA geographic location and incidents of hospital-onset CDI. Hypothesis 4b: There is a relationship between U.S. regional NA NA NA geographic location and incidents of hospital-onset MRSA Question 5: What is the relationship between the co-occurrence of hospital-onset C. diff. and hospital-onset MRSA in acute care hospitals in U.S. regions? Hypothesis 5a: The incident rates of hospital-onset MRSA are Y 0.90 .sup. 1E−11 related to the incident rates of hospital-onset CDI. Hypothesis 5b: The incident rates of hospital-onset CDI are related Y 0.90 .sup. 1E−11 to the incident rates of hospital-onset MRSA.
[0169] After preliminary evaluation, it was determined which specific regional factors were the most strongly associated with MRSA and C. diff. HAI occurrence. These results offer insight into factors which elicit the strongest potential as associated predictors for HAI risk from these two-specific antibiotic-resistant bacteria. See, (Roberts et al., 2009). Regional predictor independent variables with a strong correlation with the two targeted HAI dependent variables were selected for evaluation to develop an efficient Ordinary Least Squares (OLS) regression model. Population density was left out of the OLS regression due to the low variance and because overcrowding was a reasonable proxy to compare to MRSA incidence rates (Immergluck et al., 2019; See et al., 2017). It also had a higher correlation and level of significance to MRSA than density. This effort was also made to increase the U.S. state-based sample size from n=30 to n=32.
[0170] Homelessness, even though the correlation was somewhat weak, was left in as a variable to be regressed against the HAI targets. This choice was made because Homelessness did meet an appropriate alpha of p<0.05 and because there was no other independent variable representation that could serve as a reasonable proxy for this factor. Additionally, the same rationale drove the decision to leave in “Medically Underserved Populations” (MUP). Medically Underserved Areas (MUA) although somewhat related to MUP, as previously explained, is the accrual of area (geographies) rather than populations (people) and therefore not a direct substitute for this variable. The relationship between rural area designation and CDI HAI in the Pearson's r indicated a different relationship than Hypothesis 4a. indicated. The nut hypothesis was accepted. However, the variable was also included in the OLS due to the strong inverse relationship between Rural status and CDI incidence.
[0171] The updated variable relationships and Risk Event likelihood levels are outlined in the post-feature selection Table 7. The associated correlation strength and significance level between HAI target and geographic and demographic predictor variables are indicated in this next iteration of the Risk Analysis matrix.
TABLE-US-00007 TABLE 7 Risk Event Evaluation Post-Feature Selection Likelihood Assignments Risk Likelihood (IF/THEN) CDI MRSA Population Very High/p < .001 Very High/p < .001 >65 Years Old Very High/p < .001 Homeless Very High/p < .001 Density Somewhat High/p < .05 Crowding Very High/p < .001 MU_HSA Very High/p < .001 Very High/p < .001 MUP Somewhat High/p < .05 Somewhat High/p < .05 Rural Very High/p < .001 Urban Very High/p < .001 U.S. Geography Categorical Categorical DII Very High/p < .001 MRSA Very High/p < .001 * Density variable removed due to assumption “Crowding” is adequate proxy with higher significance and to increase “n.”
[0172] The following states that remained in the sample for analysis: Maine; New Hampshire; Pennsylvania; Indiana; Iowa; Kansas; Michigan; Minnesota; Missouri; Nebraska; Ohio; Wisconsin; Alabama; Arkansas; Kentucky; Louisiana; Mississippi; North Carolina; Oklahoma; South Carolina; Tennessee; Virginia; West Virginia; Colorado; Hawaii; Idaho; Montana; Nevada; New Mexico; Oregon; Utah; and Washington. Although smaller in number than the original population, except for the Northeastern region the remaining three regions are relatively equal in amount to one another.
[0173] The Northeast Region has the fewest states, but has the biggest proportion of the population, as illustrated by the violin plot in
[0174] The sample size and a description of the central tendency for the sample used in the OLS are as follows: U.S. State Population Observations for OLS: n=32;
TABLE-US-00008 TABLE 8 OLS regression results using CDI HAI Incidents as a dependent variable OLS Regression Results for CDI Dep. Variable: Y R-squared: 0.984 Model: OLS Adj. R-squared: 0.981 Method: Least Squares F-statistic: 313.9 Date: Mon, 10 Jun. 2019 Prob (F-statistic): 2.26E−22 Time: 17:30:36 Log-Likelihood: −199.33 No. Observations: 32 AIC: 410.7 Df Residuals: 26 BIC: 419.5 Df Model: 5 Covariance Type: Nonrobust Coef std err t P > |t| [0.025 0.975] const −289.876 64.298 −4.508 0.000 −422.043 −157.709 SE Region 250.125 99.199 2.521 0.018 46.218 454.032 WE Region 271.663 77.945 3.485 0.002 111.445 431.880 NE Region 204.583 84.244 2.428 0.022 31.417 377.749 Population >65 0.001 0.000 9.671 0.000 0.001 0.001 MRSA 3.415 0.686 4.980 0.000 2.005 4.824 Omnibus: 2.036 Durbin-Watson: 2.292 Prob (Omnibus): 0.361 Jarque-Bera (JB): 1.863 Skew: −0.535 Prob (JB): 0.394 Kurtosis: 2.497 Cond. No. 5.19E+06
[0175] The independent variables selected for this OLS regression model explained ninety-eight percent (R.sup.2=0.984) of the variance in the dependent variable prediction. The independent variables that indicated the strongest prediction for CDI HAI were as follows: Southeastern Region: (β=250.125; p<0.01); Western Region: (β=271.663; p<0.001); Northeastern Region: (β=271.663; p<0.05); Population over 65 Years old: (β=0.001); MRSA: (β=3.415; p<0.001). These results suggest that CD HAI incident rates have a significant relationship with certain geographic regions of the U.S. as well as with areas comprised of older populations. Additionally, this analysis implies that the level MRSA HAI is considered as a viable predictor for CU HAI.
TABLE-US-00009 TABLE 9 OLS regression results using MRSA HAI Incidents as a dependent variable OLS Regression Results for MRSA Dep. Variable: Y R-squared: 0.943 Model: OLS Adj. R-squared: 0.937 Method: Least Squares F-statistic: 155.3 Date: Mon, 10 Jun. 2019 Prob (F-statistic): 1.49E−17 Time: 18:07:10 Log-Likelihood: −148.02 No. Observations: 32 AIC: 304 Df Residuals: 28 BIC: 309.9 Df Model: 3 Covariance Type: Nonrobust Coef std err T P > |t| [0.025 0.975] const −8.101 10.185 −0.795 0.433 −28.965 12.763 MW Region 73.141 9.848 7.427 0.000 52.968 93.314 Population −0.130 0.063 −2.048 0.050 −0.259 0.000 CDI 0.136 0.020 6.731 0.000 0.095 0.178 Omnibus: 0.837 Durbin-Watson: 2.388 Prob (Omnibus): 0.658 Jarque-Bera (JB): 0.815 Skew: 0.168 Prob (JB): 0.665 Kurtosis: 2.294 Cond. No. 4.06E+03
[0176] The independent variables selected for this OLS regression model explained approximately ninety-four percent (R.sup.2=0.943) of the variance in the dependent variable prediction. The only predictor variables that showed a positive significance level in prediction for MRSA in this model were: Midwestern Region: (β=73.141; p<0.001); CDI: (β=0.136; p<0.001). The results of this analysis suggest that U.S. health systems boated in the Midwestern part of the U.S. and those with higher rates of CDI might use these as indicators of higher levels of risk for MRSA HAI. Interestingly, Population appears to have a slightly inverse relationship with MRSA HAI (β=0.130; p<0.05). Notably, regional influence appears to be a significant predictor for both CDI and MRSA. The pairwise graph below provides a visualization of these regional outcomes. Based on this analysis; it is not clear what specific factors may be driving these results. However, U.S. regionality did appear to be related to both CDI and MRSA HAI incidence rates.
[0177] The results of the OLS regression were used to reject or accept the null hypotheses of each of the five Risk Analysis research questions. The results are tabulated in Table 10.
TABLE-US-00010 TABLE 10 OLS Relational CDI and MRSA HAI Environmental and Demographic Factors Risk Analysis Research Hypotheses OLS R.sup.2 Alpha Baseline Higher numbers for the regional population are associated with higher N 0.981 NA numbers of CDI incidences Higher numbers for the regional population are associated with higher N 0.937 p < .05 * numbers of MRSA incidences Question 1: What is the relationship between regional population compositional factors and the risk for AMR hospital-onset infectivity? Hypothesis 1a: U.S. regions with a larger proportion of their populations over Y 0.981 p < .001 the age of 65 have an increased risk for incidents of hospital-onset CDI. Hypothesis 1b: U.S. regions with higher populations of homeless persons N 0.937 NA have an increased risk for hospital-onset MRSA. Hypothesis 1c. i: U.S. regions with higher populations densities have an N 0.937 NA increased risk for hospital-onset MRSA. Hypothesis 1c. ii: U.S. regions with higher populations of housing over- N 0.937 NA crowding have an increased risk for hospital-onset MRSA. Question 2: How does healthcare accessibility appear to effect and the risk for AMR hospital-onset infectivity? Hypothesis 2a. i: U.S. regions with more Medically Underserved Areas N 0.981 NA (MUA) have an increased risk for incidents of hospital-onset CDI. Hypothesis 2a. ii: U.S. regions with more Medically Underserved Populations N 0.981 NA (MUP) have an increased risk for incidents of hospital-onset CDI. Hypothesis 2b. i: U.S. regions with more Medically Underserved Areas N 0.937 NA (MUA) have an increased risk for hospital-onset MRSA. Hypothesis 2b. ii: U.S. regions with more Medically Underserved Populations N 0.937 NA (MUP) have an increased risk for incidents of hospital-onset MRSA. Question 3: How does the rural or urban status of health systems patient catchment area affect the risk for AMR hospital-onset infectivity? Hypothesis 3a: U.S. regions with more “rural status” defined areas N 0.981 NA have an increased risk for incidents of hospital-onset CDI. Hypothesis 3b: U.S. regions with more “urban status” defined areas N 0.937 NA have an increased risk for incidents of hospital-onset MRSA Question 4: What is the relationship between U.S. geographic region and the risk for AMR hospital-onset infeCTivity? Hypothesis 4a: There is a relationship between U.S. regional geographic Y 0.981 p < .01; location and incidents of hospital-onset CDI. p < .05** Hypothesis 4b: There is a relationship between U.S. regional geographic Y 0.937 .sup. p < 001*** location and incidents of hospital-onset MRSA Question 5: What is the relationship between the co-occurrence of hospital- onset C. diff. and hospital-onset MRSA in acute care hospitals in U.S. regions? Hypothesis 5a: The incident rates of hospital-onset MRSA are related to Y 0.981 p < .001 the incident rates of hospital-onset CDI. Hypothesis 5b: The incident rates of hospital-onset CDI are related to the Y 0.937 p < .001 incident rates of hospital-onset MRSA
[0178] The results of the OLS analysis avowed for a final reframing of the hypotheses statements as fuzzy IF-THEN statements.
TABLE-US-00011 TABLE 11 Hypotheses Fuzzy Membership Rules after OLS Regression data scaling 1. IF region Population numbers are large THEN CDI and MRSA Risk Prevention cannot be associated based on the data analysis. 2. IF region Population numbers above 65 years old are great, THEN CDI Likelihood of Exposure is Very High. 3. IF region Homeless populations are large THEN MRSA Risk Prevention cannot be associated based on the data analysis. 4. IF region area population Density proportion is Low, THEN MRSA Risk Prevention cannot be associated based on the data analysis. 5. IF region area household Crowding proportion is Low THEN MRSA Risk Prevention cannot be associated based on the data analysis. 6. IF region MUA amount is Low THEN, CDI and MRSA Risk Prevention cannot be associated based on the data analysis. 7. IF region MUP amount is Low, THEN, CDI and MRSA Risk Prevention cannot be associated based on the data analysis. 8. IF region Rural designated area is large THEN CDI Risk Prevention cannot be associated based on data analysis. 9. IF region Urban designated area is large, THEN MRSA Likelihood of Exposure is Very High. 10. IF regional Geography is in a certain location in the continental U.S. THEN CDI Likelihood of Exposure is High, and MRSA Likelihood of Exposure is Very High. 11. IF regional MRSA incidence rates are Low, THEN CDI Likelihood of Exposure is Very High. 12. IF regional CDI incidence rates are Low, THEN MRSA. Likelihood of Exposure is Very High.
[0179] This effort allowed for the manifestation of the geographic and demographic factors most closely associated with CDI and MRSA HAI to become salient. The specificity of this information was notable for several reasons. One reason included obtaining validated metrics to govern the firing strengths of Risk Analysis variables within Fuzzy Risk Prevention membership categories and determining the specific areas of HAI risk so that they were aligned with certain areas of resilience interventions that were assumed tenable. Furthermore, the OLS method has proven to be useful in selecting the essential Fuzzy Rules based on their contributions of variance and significance to the analysis output. (Yen & Wang, 1999). The associated significance level between HAI target and geographic and demographic predictor variables are indicated in the final iteration of the Risk Analysis matrix.
TABLE-US-00012 TABLE 12 Risk Event Evaluation Post-OLS Regression Selection Likelihood Assignments Risk Likelihood (IF/THEN) CDI MRSA Population N/A/p < .05 N/A/p < .05 >65 Years Old Very High/p < .001 Homeless N/A/p < .05 Density N/A/p < .05 Crowding N/A/p < .05 MU_HSA N/A/p < .05 N/A/p < .05 MUP N/A/p < .05 N/A/p < .05 Rural N/A/p < .05 Urban N/A/p < .05 U.S. Geography High/p < 01 Very High/p < .001 DII Very High/p < .001 MRSA Very High/p < .001
[0180] The following sections discuss how this probabilistic environmental and population hazard data evaluation (i.e., Risk Analysis) guided the possibilistic adaptive response intervention appraisal (i.e., Resilience Assessment). Additionally, a review of how both processes can be integrated into a Fuzzy Inference System is discussed. The present technology provides a comprehensive Resilience Inference approach that offers improved insight into CDI, and MRSA HAI prevention performance safety when Risk, Resilience, and associated Performance Safety outcomes are considered.
[0181]
[0182] Table 13 describes the associated metrics assigned to the three phases of Risk Analysis.
TABLE-US-00013 TABLE 13 Risk Analysis: Event Likelihood, Mitigation Potential, Prevention Level Metrics Risk Mitigation Risk Event Evaluation Evaluation Hazard Likelihood of Stability Reversibility Event exposure (L) (S) (R) Risk Prevention CDI Somewhat 0.05 100 100 Somewhat 0.60-1.0 High Low MRSA High 0.01 50 50 Low 0.30-.70 Very High 0.001 25 25 Very Low 0-.40
[0183] Predictive likelihood metrics for Risk Event Evaluation factors are determined by their significance level resultant from the OLS regression analysis of HAI predictor and target variables. Likelihood metrics for Risk Event Evaluation serve as firing strengths weights in defining the degree of membership in Risk Prevention fuzzy category continuum. The Risk Analysis operationalization framework is also contingent upon defining system mode of operation Condition Stability and Reversibility. It is presumed the same membership scale metrics that are used for Risk Prevention fuzzy membership levels is used for defining capacity levels for these two areas to simplify the analysis.
TABLE-US-00014 TABLE 14 Risk Analysis Mode of Operation Condition Stability and Reversibility Levels Condition Stability (S) Condition Reversibility (R) High Stability 60-100 Easily Reversible 60-100 Moderate Stability 30-60 Somewhat Reversible 30-60 Low Stability 0-40 Difficult to Reverse 0-40
[0184] An actual model of operation capacity levels may be set by specific clinical SME within the health systems undertaking HAI Resilience Inference, with distinct area dependent factors (e.g., cities, locales, and neighborhoods). Predictor variables related to patient demography proportion and other community-specific geographically dependent variables diverge depending on where in the U.S. defined region, even when the Resilience inference was taking place. This may be accounted for using known techniques. Arbitrary levels for Risk Mitigation Condition Stability and Condition Reversibility are used for illustrating this phase of HAI Risk Analysis.
[0185] The Risk Prevention Fuzzy Membership levels are defined by the TEN and TrFN illustrated in
TABLE-US-00015 TABLE 15 Presumed Risk Mitigation Mode of Operations Rankings Risk Mitigation Evaluation Assumed Assumed Risk Event Evaluation (REE) Stability (S) Reversibility (R) Age and CDI 50 25 Geography and CDI 100 25 Geography and MRSA 100 25 CDI due to MRSA Incidence 50 100 MRSA due to CDI Incidence 50 100
[0186] Given the Risk Event weighting and mitigation potential levels, a Risk Prevention level may be calculated based on a compounded level of Risk Event Evaluation, (REE) and Risk Mitigation Evaluation (RME) amounts. The formula for this approach is delineated in Table 16 in relation, to each of the five risk events resulting as significant by the OLS.
TABLE-US-00016 TABLE 16 Calculations for HAI Risk Prevention Capacity μF(X) degree of membership Risk Percentile Mitigation Risk Rank Risk Event Weighted average Evaluation Average Prevention REE + Evaluation (REE) ((S(L) + R(L)/(L + L))/100 (RME) (S + R)/N)/100 Ranking (REE*RME) Age and CDI 0.1255 0.375 0.173 Geography and CDI 0.135 0.625 0.219 Geography and MRSA 0.126 0.625 0.205 CDI due to MRSA 0.5005 0.75 0.876 Incidence MRSA due to CDI 0.5005 0.75 0.876 Incidence
[0187] This computation provides the information needed for Risk Fuzzy Set input level metrics to incorporate into a FIS that provides specificity on HAI prevention and adaptive response expectations.
[0188]
TABLE-US-00017 TABLE 17 Risk and Resilience Inputs into FIS Risk Prevention Resilience Potential HAI Risk Membership Level Membership Level Event Factor (FIS-Input 1) (FIS-Input 2) Age above 0.173 78% reduction in CDI .780 65 and CDI (copper-physical) Geography 0.219 42.7% reduction in .427 and CDI CDI (panarchy) Geography 0.205 30% reduction in .300 and MRSA MRSA (decolonization- adaptive capacity) CDI due 0.876 42.7% reduction in .427 to MRSA CDI (panarchy) Incidence MRSA due to 0.876 31% reduction in .310 CDI Incidence MRSA (feedback)
[0189] The interventions indicated in Table 17, although specific to HAI type, are arbitrary and for model demonstration. For example, a “Panarchy-based” strategy is substituted for the microbial resistant copper physical intervention for Age above 65 and CDI.
[0190] Based on the input risk and resilience fuzzy membership levels, the intersection of the two truncated disjoint membership functions fats within the fuzzy membership continuums of Risk Prevention and Resilience Potential.
[0191]
[0192] Based on the inputs of the Risk and Resilience examples, the results join these two memberships. Specifically, a strong or higher resilience achievement membership level improves HAI adaption performance safety even if the potential to prevent HAI risk were very low. Inputting the “Fuzzy Rule Basis for Resilience Inference and HAI Performance Safety”, permits visualization of the juncture of fuzzy TFN and TrFN of risk prevention and resilience potential as it relates to Performance Safety.
[0193] CDI and MRSA HAI specific fuzzy membership risk prevention levels can now be input along with case-study derived intervention strategies deemed as most relevant to CDI and MRSA HAI, as resilience potential fuzzy membership levels. The fuzzy membership inputs, when evaluated together using a centroid analysis to establish a crisp output in the context of the fuzzy rule basis, offers the following crisp outputs as it relates to HAI control performance safety.
TABLE-US-00018 TABLE 18 HAI Resilience Inference FIS Performance Safety Results Associated Risk Risk Factor and Prevention Ranking HAI Specific Resilience Performance Safety Inference HAI Event Input Potential Input Output Age and CDI 0.173 Very Low 78% (0.780) Resilient 0.50 Safe Geographical 0.219 Very Low 42.7% (0.427) reduction Strong 0.50 Safe Region and CDI in CDI (panarchy) Geographical 0.205 Very Low 30% (0.300) reduction Somewhat 0.297 Somewhat Region and MRSA in MRSA (decolonization) Strong/Strong safe CDI due to 0.876 Somewhat Low 42.7% (0.427) reduction Strong 0.831 Very Safe MRSA Incidence in CDI (panarchy) MRSA due to 0.876 Somewhat Low 31% (0.310) reduction Somewhat 0.672 Safe/Very CDI Incidence in MRSA (feedback) Strong/Strong Safe
[0194] The visualizations for the above referenced FIS Performance Safety results outlined in Table 18 are illustrated in
[0195] This analysis is adequate to test the assumption that a strong or higher resilience achievement membership level could improve HAI adaption performance safety regardless of its risk prevention level. The continuum of resilience prevention spans the range of the lower levels of “Strong” (e.g., post-discharge decolonization regimen at 30% MRSA HAI reduction) to “Resilient” (e.g., copper finishes installed in acute care inpatient settings at 78% CDI HAI reduction). The number of tested HAI Resilience Inference observations is small, which is to a degree contingent upon the data available for testing. However, the distribution of these outcomes is generally parametric. A t-Test that compares the associated likelihood levels of Risk Event Evaluation with the fuzzy inferred Performance Safety likelihood levels considering resilience strategy integration suggests the difference between these outcomes is significant,
TABLE-US-00019 TABLE 19 t-Test comparison of Risk Event and HAI Performance Safety Likelihood t-Test Paired Two Sample for Means Risk Likelihood Safety Likelihood Mean 0.278 0.56 Variance 0.041 0.0405885 Observations 5.000 5 Pearson Correlation 0.871 Hypothesized Mean Difference 0.000 Df 4.000 t Stat −6.141 P(T <= t) one-tail 0.002 t Critical one-tail 2.132 P(T <= t) two-tail 0.004 t Critical two-tail 2.776
[0196] These results validate the assumption that integrating even a comparatively low cost and easy to implement resilience improvement strategy to augment healthcare delivery infrastructure, may offer a meaningful impact on improving HAI control Performance Safety outcomes.
TABLE-US-00020 TABLE 20 Extending Risk Analysis Hypotheses Rule Basis through FIS output 1 IF CDI associated Risk Prevention is Very Low because healthcare region's population numbers above 65 years old are large AND Health Systems Infrastructure Resilience is Resilient, THEN C. diff. HAI Prevention Performance is Safe. 2. IF CDI associated Risk Prevention is Very Low because the regional geography of the patient catchment area is in the southeastern, western, or northeastern part of the U.S. AND Health Systems Infrastructure Resilience is Strong THEN C. diff., HAI Prevention Performance is Safe. 3 IF MRSA associated Risk Prevention is Very Low because the regional geography of the patient catchment area is in the midwestern part of the U.S. AND Health Systems Infrastructure Resilience is Somewhat Strong to Strong THEN C. diff. HAI Prevention Performance is Somewhat Safe. 4. IF CDI associated Risk Prevention is Somewhat Low because MRSA HAI incident rates are high AND Health Systems Infrastructure Resilience is Strong, THEN C. diff. HAI Prevention Performance is Very Safe. 5. IF MRSA associated Risk Prevention is Somewhat Low because of C. diff. HAI incident rates are high, AND Health Systems Infrastructure Resilience is Somewhat Strong to Strong THEN MRSA HAI Prevention Performance is Safe to Very Safe.
[0197] There are several limitations to the Risk Analysis portion of this evaluation that are noteworthy. The approach selected for identifying regional U.S. risk factors associated with HAI caused by antimicrobial-resistant bacteria required an assumption of normally distributed data. However, even with the removal of state-based outliers, it is evident that none of the distributions of variables selected was perfectly normally distributed. This analysis only viewed incidences of C. diff. and MRSA infectivity in acute care settings.
[0198] Individual states were trimmed from this analysis, such as California, Florida, New York, and Texas, due to the presence of both population and environmental based outliers. The removal of these states improved normality of the regional data distribution and thus potentially increased the potential of model outcomes assumption generalizability. However, all four of these states based on the most current 2017 CDC SIR data rank the highest in both CDI and MRSA observed HAI in acute care settings. Therefore, availability of state-specific data with recorded discrete community-based HAI observations across different acute care health systems, rather than the accumulated lump sum HAI data, is beneficial.
[0199] Currently, the only real potential for gathering HAI resilience, data is to draw it from discrete peer-reviewed published case studies. These investigations typically are done under specific conditions and with a comparatively much smaller N-value than those found in nationally reported HAI rates. This makes the generalizability between Risk Factor and Resilience Potential data sources particularly challenging. However, using evidence-based data, while not as robust as Supervised Learning data analysis efforts is at present, is one of the only feasible ways of comparing risk and resilience-based outcomes that relate to hospital-onset infections.
[0200] The Resilient Systems Inference Model provides a somewhat self-contained interactive system performance forecasting and evaluation framework that healthcare organizations could feasibly apply independently. Health systems are often understandably reticent to expose internal challenges they may be having regarding infection control and HAI incidence to external researchers or consultants that have advanced system analysis expertise. This issue can make it difficult for healthcare organizations to gain meaningful and accurate insight into the potential value, or lack thereof, their infection prevention strategies have on increasing patient safety.
[0201] Performing an environmental scan of a hospital's regional catchment area could demonstrate if a significantly high percentage of their patient population was over the age of 65. This could make a compelling case for a facility investing in well tested environmental contact surfaces resilient to Clostridioides difficile pathogen propagation such as antimicrobial copper. Additionally, hospitals based in the midwestern portion of the U.S. might be well advised to implement a low-cost system of clinical feedback related to patients presenting with MRSA in acute care settings in order to improve operational resilience to associated HAI incidences in their inpatient population. Neither of these options requires any substantive additional analysis on hospital administrators' part, but both offer an evidence-based approach to potentially improving resilience potential of acute care HAI reduction strategies.
[0202] Arbitrary levels for Risk Mitigation “Condition Stability” and “Condition Reversibility” were used because the actual operation capacity levels for these two factors need to be assigned by specific clinical and administrative SME within specific health systems. As indicated, exogenous system area dependent factors (e.g., cities, locales, and neighborhoods) and potentially endogenous health system unit-based factors (e.g., ICU, Med/Surg, and Labor and Delivery) wad both influence these metrics. If the Resilient Systems Inference Model presented were to be used by an individual health system, Fuzzy Cognitive Mapping is used as a way of improving the validity and accuracy of Risk Mitigation “Condition Stability” and “Condition Reversibility” measures.
[0203] Fuzzy Cognitive Mapping (FCM) is a technique that captures the relationships between both human and engineered system elements. FCM graphs structure that provides a human-driven and flexible method for intuitively representing complex relationships between endogenous and exogenous system elements. FCM is constructed of intersecting cognitive concept nodes representing task-related events; the links that connect the nodes are then assigned vague “fuzzy” strengths in the interval range [−1,1], indicating the degree to which one event influences another (Smith and Eloff, 2000). A critical component to realizing effective and safe workflow design, which also avoids situational or systemic error, is to ensure that human cognition is endemic to process development (Sutcliffe, 2006). Additionally, a primary focus of Human Factors safety is how to best design shared cognitive systems so that people working in groups can perform successfully in the diverse circumstances in which they have to function (Woods et al., 2017). Furthermore, the construct of Fuzzy Logic is not only a proper way of capturing the quantitative interpretation of linguistic measures but a useful method for human learning and development in natural and, technology-based environments and should, therefore, be treated by the system designers and engineers as a valuable component for establishing a process or place design requirements (Karwowski et al., 1999).
[0204] FCM offers a promising method for approaching an overall understanding of risk mitigation potential based on care delivery workflow within specific environments of care serving unique patient populations. In HAI Risk Mitigation workflow development, an understanding of the cognitive functioning supports and impediments that are placed on both clinical and operational support staff is important for successful care delivery to patients in complex and mutable settings. Cognitively complex situations increase the potential for risk and human error (Reason, 2016). Using FCM for analysis of specific HAI Risk Mitigation potential or barriers within clinical workflow could provide more context-relevant data that aids in better defining specific types of HAI “Condition Stability” and “Condition Reversibility” level metrics.
[0205] Although, overcrowding and Homelessness did not register in the OLS as being statistically significant risk indicators of incidences of MRSA. They did register as having a significant relationship with MRSA in the Pearson's r. There also appears to be a visual relationship between the factors of Homelessness and Crowding as it relates to the incidence of MRSA. Some research has linked these environmental factors with increased observed incidences of MRSA (Immergluck et al., 2019; See et al. 2017). Moreover, MRSA has the potential of being classified as an endemic infection in vulnerable populations, an epidemic if occurring with flu and or pneumonia, or a pandemic if occurring with a virulent pathogenic strain of Type A Influenza (MacIntyre & Bui, 2017). These characteristics make it a worthwhile area for improving healthcare resilience in general (Schoch-Spann et al., 2018).
[0206] There is growing evidence that there are many environmental conditions that are suspected as being linked to the pervasiveness of pathogens that cause human infectivity. Although “rural” designated land areas were evaluated as part of this analysis, this factor had a significantly inverse relation with both CDI and MRSA. However, most of the land is in the U.S. is designated as rural (Ratcliffe et al., 2016). The terms “rural” and “agricultural” do not necessarily have the same meaning and much of the rurally designated area in the U.S. is not used for the type of farming purposes that exposes humans to the type of waste streams directly associated with Clostridioides difficile infectivity (Brown & Wilson, 2018; Freeman et al, 2010). Geographic information system (GIS) data that indicated specific regions used for high infectivity-risk agricultural waste stream exposure along with the incidence of hospital and community-onset CDI offers a depiction of the relationship between this type of environmental risk factor and HAI. In addition to regional commercialization patterns such as agricultural exposure, there is growing research that links climatic conditions to infectivity prevalence. Research on areas with warmer temperatures have drawn links between these climatic conditions and the prevalence of both CD and MRSA (Sahoo et al. 2017; Naggie, 2010). Additionally, research on climate change has suggested that there is a relationship between warming temperatures and AMR in general (European Society of Clinical Microbiology and Infectious Diseases, 2019; MacFadden et al., 2018).
[0207] The effects of warming temperature and HAI prevalence also raises questions about measuring the effects of relevant HAI risk factors in urban environments. Research indicates that urban areas due to socioeconomic factors can struggle with MRSA (See et al. 2017) and there does appear to be a visible relationship in the data used for this study and incidence of MRSA in Urban areas. However, this incidence rate may be related to the prevalence of the “urban heat island effect” that can cause cities on a yearly average to be significantly warmer than non-urban areas, except for those urban areas in biomes with arid and semiarid climates (Imhoff et al., 2010). The effort to delve more deeply into urban conditions, which lead to increased HAI risk factors is another area that is aided through access to GIS specific data and area-specific HAI rate data. More generally, the resilience model may be location-based, and dependent on geographic information system information and/or weather or climactic conditions.
[0208] Principal Component Analysis (PCA), Spatial PCA, and other techniques for analysis of location and geographic information, may be employed as part of the initial data analysis and/or preparation of the actions to be applied. Shandoosti (2016), Wang (2017).
[0209] Environmentally based resilience interventions, such as using copper for high contact surfaces in patient treatment areas, are often more expensive but could also prove more stable than behavior change because they are fixed improvement interventions. A common way of determining the cost-benefit value of environmental investments is by conducting a Life-cycle cost analysis (LCCA). This process assesses all the costs associated with acquiring, maintaining, disposing, and replacing a building material, fixture, or system. LCCA is especially valuable when design or building project alternatives exceed the initial or “first costs” of items which fulfill the same performance requirements but may not have the same performance features. LCCA allows high-performance, but higher first cost alternative system components to be compared similar lower first cost but weaker performance system elements to select the one that maximizes net saving (Fuller, 2016).
[0210] Cost-benefit analysis (CBA) is a way to incorporate LCCA and human costs to assess the net benefit of implementing specific operational improvement initiatives. These often highly detailed and longitudinal analysis when used for complex organizational improvement and capital planning development include not only initial construction and actual procurement costs but also the following (Abernathy, 2012): Project-people costs, which are estimates of associated work hours required by full-time employees or subcontractors required to implement the improvement initiative; Target-people costs refer to the cost of time needed to train people interacting with the new system to use and upkeep it so that it maintains operational effectivity. The estimate also includes any downtime or lost production time due to project implementation; Technical-support costs refer to fees paid to consultants or product providers who needed for quality control, installation, debugging, and other required support for strategy implementation; Resource costs are the cost of materials or equipment required to support or mobilize the improvement initiative; Maintenance costs are annualized expenses required for maintaining the solution after its implementation.
[0211] Using CM provides a viable way of evaluating the advantage of integrating specific HAI resilience strategies. As a means of demonstrating the value of CBA applied to HAI resilience strategies using data tom case studies related to cost savings and patient safety accessed for this research can be employed for demonstrating the value of this approach.
[0212] There have been several studies that have evaluated the antimicrobial performance of copper as a healthcare environment finish. Measurable CDI HAI reductions have been observed in health systems that have used copper as an experimental contact surface in comparison to patient rooms that did not have this type of surface treatment instated (Sifri et al., 2016; von Dessauer et al., 2016). There have also been studies related to the surplus cost of inpatient care that can be directly attributed to hospital-onset CGI (Zhang et at 2018; McGlone et at 2012). Not only do HAIs impact hospital reimbursement from CMS (CMS, 2018), they also can impact patient perceptions of care quality and experience which may further impact both CMS reimbursement based on patient experience as wet as current and future hospital revenue (Burnett et al, 2010). This scenario presents an opportunity to evaluate how a health system may amortize higher initial costs that may be incurred in the procurement of copper Finishes, Fixtures, and Equipment (FFE) over time through recouped patient care savings. A cost-benefit model for the use of copper fixtures was developed by The University of York, in the U.K. that facilitates the input of first cost pricing for patient room furnishings with copper and “regular” finishes. Some of these furnishings included copper finished: Bedrails; Overbed tray table; Call button; IV Pole. The York cost-benefit model template is based on a 20-bed ICU and predicts payback for investing in antimicrobial copper furnishings for patient treatment areas in less than one year (Taylor et al. 2013).
[0213] Using resilient systems inference for estimating hospital-acquired infection prevention infrastructure provides a valuable tool for forecasting performance safety outcomes for infection control strategies based on health system risk capacity and resilience capability. Furthermore, this approach assists in informing health resource planning with valid evidence, and as a result, improve adaptive capacity in medically underserved urban and rural geographic locations where vulnerable patients are most at risk of contracting HAI. Another benefit for using resilient systems inference model is to enable diverse healthcare teams to achieve infection prevention and control standards along with regional health resources better suited to safe, effective, and sustainable care delivery for their own regional patient population needs.
[0214] Hospital-onset infections present a significant risk in undermining the health and well-being of many people tying in the U.S. and worldwide. The rise of community-based infections and growing level of antimicrobial resistance due to factors outside the scope of control of health systems exacerbates this risk. Poor quality in preventable hospital-onset infections can create a vicious cycle of increased incidence rates in acute care inpatient environments that can be extremely costly to health systems. A system's performance HAI resilience inference model permits analysis of underexplored regional, environmental, and social risk infectivity factors. Concurrent adaptive response efficacy of both available and needed health resources are invaluable to infection control planning in health systems. This approach provides an ability to engineer health systems that were truly resilient to HAI increasing incidence rates and challenging to predict potentials for health system's environment of care infectivity.
[0215] The present disclosure presents exemplary systems and methods for forecasting potential outcomes of operationally based infection control interventions in acute care settings that can enable healthcare quality and safety teams to gain meaningful and accurate insight into the possible performance safety level of environment of care infection prevention strategies through the application of a series of System Science derived mathematical models. Referring to
[0216] The computer server 120 includes one or more computer processors, a memory storing the predictive model 130, supervised machine learning 125, and other hardware and software for executing the respective model. More specifically, the software may be computer readable instructions, stored on a computer readable media, such as a magnetic, optical, magneto-optical, holographic, integrated circuit, or other form of non-volatile memory. The instructions may be coded, for example, using C, C++, JAVA, SAS or other programming or scripting language. To be executed, the respective computer readable instructions are loaded into RAM associated with the computer server 120.
[0217] The predictive model 130 may be a linear regression model, a neural network, a decision tree model, or a collection of decision trees, for example, and combinations thereof. The predictive model 130 may be stored in the memory of the computer server 120, or may be stored in the memory of another computer connected to the network 140 and accessed by the computer server 120 via a network 140. The predictive model 104 preferably takes into account a large number of parameters, such as, for example, characteristics of electronic records (e.g., performance characteristics in addition with other design characteristics). The predictive model 130 may then be used by the computer server 120 to estimate the likelihood that a particular building infrastructure material will reduce the risk of infection of one or more bacterial and/or viral agents. Additionally or in the alternative, an exemplary predictive model may estimate the likelihood that a design of a building (having various infrastructure materials) will act in reducing the risk of infection of one more biological agents.
[0218] Next,
[0219] Stored in the memory 204 are both data and several components that are executable by the processor 202. In particular, stored in the memory 204 and executable by the processor 202 are code for implementing one or more neural networks 211 (e.g., artificial and/or convolutional neural network models) and code 212 for using machine learning, such as neural network models 211, for hospital acquired infection prevention data analysis. Also stored in the memory 204 may be a data store 214 and other data. The data store 214 can include an electronic repository or database relevant to computable records of hospital acquired infection prevention data analysis. In addition, an operating system may be stored in the memory 204 and executable by the processor 202. The I/O devices 208 may include input devices, for example but not limited to, a keyboard, mouse, etc. Furthermore, the I/O devices 208 may also include output devices, for example but not limited to, a printer, display, etc.
[0220] The feature or features of one embodiment may be applied to other embodiments, even though not described or illustrated, unless expressly prohibited by this disclosure or the nature of the embodiments. The phrase “configured to” means a specification or clarification of the structure or composition of an element defining what the element is, by way of a specific description of its configuration and interface with other elements or an external constraint. Functional language within such a specification is taken to be an affirmative limitation, and not a mere intended use. The disclosure has been described with reference to various specific embodiments and techniques. However, many variations and modifications are possible while remaining within the scope of the disclosure. The claims hereinbelow are to be construed as excluding abstract subject matter as judicially excluded from patent protection, and the scope of all terms and phrases is to be constrained to only include that which is properly encompassed. By way of example, if a claim phrase is amenable of construction to encompass either patent eligible subject matter and patent ineligible subject matter, then the claim shall be interpreted to cover only the patent eligible subject matter, consistent with any presumption of validity to be applied. This rule of construction overrides other claim construction predicts and linguistic presumptions. The various disclosure expressly provided herein, in conjunction with the incorporated references, are to be considered to encompass any combinations, permutations, and subcombinations of the respective disclosures or portions thereof, and shall not be limited by the various exemplary combinations specifically described herein.
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