Patent Publication Number: US-2013253942-A1

Title: Methods and Apparatus for Smart Healthcare Decision Analytics and Support

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims priority of U.S. provisional application No. 61/613,981 filed Mar. 22, 2012, and which the disclosure is hereby incorporated by reference by its entirety. 
    
    
     FIELD OF INVENTION 
     The present invention relates to methods and an apparatus for developing, analyzing, investigating, supporting and advising healthcare and well-being related decisions. In particular, the present invention relates to the architecture of systems in either stand-alone or distributed/collaborative/pervasive settings, the components of the systems and their underlying processes and couplings, the computational techniques built into the methods, input data sources integrated into and output results produced and distributed by the systems, as well as the apparatus for carrying out the corresponding user interaction, data access and collection, data integration and processing, data-driven inferences and simulation, intelligent computations, decision analytics, and decision support to generating solutions to various healthcare analytics and decision-making problems for either daily services and operations (e.g., time block assignment; service/quality management) or strategic planning (e.g., resource optimization and allocation). This invention also relates to two working illustrations of the methods and apparatus that present the embodiment illustrations of the present invention. One embodiment illustration is related to generating adaptive operating room (OR) time block allocation solutions for a medical services-providing institution. The generated outputs can readily be used to help ORs maintain a stable performance in the face of dynamically changing and non-deterministic patient arrivals (e.g., due to geodemographic, environmental/climate, and socioeconomic variations). Here, non-deterministic means that the quantity in question may be predicted by various statistical and mathematical techniques although particular outcomes may not happen with total certainty. Another embodiment illustration is on performing decision analytics tasks and adaptive decision support in regional healthcare resource allocation that has the advantages of reducing healthcare performance disparities and/or the optimization of resource usage and performance. 
     BACKGROUND OF INVENTION 
     Healthcare decision analytics and support are crucial functions for healthcare service-providing organizations, practitioners, researchers, decision makers, patients, general users, and other relevant stakeholders. The present invention of decision analytics and support methods and apparatus helps them to extract and/or infer, integrate, fuse, and interpret information (e.g., detecting and explaining complex healthcare systems behavior); provides functions and techniques to scientifically develop, analyze, investigate and evaluate healthcare and well-being related decisions for either daily services and operations (e.g., time block assignment service/quality management) or strategic planning (e.g., resource optimization and allocation) that involve many dynamically-interacting intrinsic (endogenous, internal) and extrinsic (exogenous, external) impact factors exerting influences on the performance and outcomes of the complex healthcare systems in multiple temporal and spatial scales; and produces evidence-based recommendations and/or analytics support to healthcare service-providing organizations, practitioners, researchers, decision makers, patients, general users, and other relevant stakeholders as well as for direct integration into healthcare services. 
     Potential users for the present invention include healthcare administrators both at a regional level or an individual health service level, healthcare service-providing organizations such as hospitals and labs, healthcare workers such as doctors and nurses, stakeholders such as secondary service providers and patients (here, patients should be taken in a broad sense, which include all the potential healthcare service users). For instance, regional (e.g., a country, a province, a city, or a district) healthcare administrators will be supported by the present invention when they plan and allocate healthcare resources and propose strategies and procedures for public healthcare infrastructures and services. Hospital and other healthcare service administrators will be aided by the invention when they analyze, evaluate, and predict the outcomes and efficacy of their strategies and operations, e.g., in scheduling physical and human resources and smoothing the logistic processes among different units. Healthcare service-providing organizations and healthcare workers such as doctors will be assisted by the invention for which helps them make their clinical decisions on treating patients based on evidences derived from different sources such as historical patient clinical data and academic/medical research findings. With the invention, healthcare researchers will be aided in conducting clinical trials, as the decision analytics and support apparatus provides some suggestion/recommendations based on comprehensively analyzing historical clinical health records and academic/medical research findings (e.g., via text and semantic analytics functions). As well, patients will be benefited in their own health related decisions (e.g., daily care, doctor or treatment selections), as the invention offers evidence-based information and decision suggestions with respect to their own specific profiles. 
     Users access the smart healthcare decision analytics and support apparatus and present their analytics and decision problems in any of centralized, distributed, and pervasive/mobile manners. The objective(s), problem types, issues, sub-questions, criteria, requirements (e.g., indicators and measurements), and corresponding decision/control variables and constraints for the decision analytics problem should be automatically extracted and/or inferred from users&#39; problem sketches or descriptions. At the same time, the present invention extracts and/or infers the contextual information for users and analytics problem at hand, such as users&#39; profiles and the analytics scales of the problems (e.g., decision analytics and supports for a region or for a hospital). The present invention has the abilities to record and recall encountered users and to automatically identify and/or infer the types of subsequent/new users with their profiles and relate their needs (i.e., required decision analytics and support problems) together, in doing so to intelligently and automatically infer and recommend the decision analytics problems for subsequent/new users. 
     To achieve the objectives (which are extracted and/or inferred automatically from users&#39; problem description) of different healthcare analytics and decision problems, five major categories of data sources will be utilized by the decision analytics and support apparatus. The first major category of data sources corresponds to the existing healthcare service operations, including the patient profiles and clinical information from actual healthcare systems/subsystems, the investment, policies, and management information, both at a regional level and an individual healthcare service level. That is, the inputs of actual healthcare service systems/subsystems. The second category of data sources is related to the ubiquitous patient data, including personal information (e.g., personal profiles and daily activities) and patient health information routinely tracked/collected from ubiquitous devices (e.g., smart phones), and clinical and patient information distributed in health related physical and online communities (e.g., forums). The third category of data sources comes from the healthcare related secondary service providers, such as community health service centers, rehabilitation centers, insurance companies, pharmacy companies, and medical apparatus and instruments companies. The fourth data source relates to the exogenous factors, dynamic or static, that affect the inputs of actual healthcare service systems, such as geodemographic, environmental/climate, and socioeconomic related factors and human behaviors, which serve as the impact factors and/or essential contexts for healthcare and well-being related decisions. And finally, the academic/medical research databases are incorporated into the decision analytics and support apparatus with prior academic/medical research findings which are utilized for healthcare evidential inferences, hypothesis generation, model construction, as well as mining and/or discovering explicit and implicit relationships among impact factors/determinants/conditions and decision parameters and variables, e.g., drug-drug interactions in drug development. The healthcare decision analytics and support apparatus accesses, extracts and/or infers, and maintains the above-mentioned data sources through either an integrated or a distributed/pervasive interface. 
     The present invention is able to identify, infer, and support the analytics and decision making tasks at different service scales, depending on users&#39; decision making needs and requirements. The analytics techniques, which will be automatically used either individually/sequentially or in an integrated manner depending on the specific tasks at hand, include: statistical analysis tools (e.g., regression, ANOVA, and structural equation modeling), intelligent analysis tools (e.g., artificial intelligence, machine learning, and data mining techniques), and most importantly, an intelligent complex-healthcare-systems modeling and strategic analysis module that analyzes, predicts, and evaluates designed strategies by means of an integrated utilization of complex systems modeling techniques (e.g., autonomy-oriented computing (AOC)-based modeling and queueing modeling), optimization and intelligent computation (e.g., mathematical programming), numerical or agent-based or AOC-based simulation, and visualization. This intelligently configured and integrated processing capability allows for producing solutions to practical healthcare decision analytics problems that involve complex-systems behaviors due to the large number of intrinsic and extrinsic impact factors exerting influences on healthcare outcomes in different temporal and spatial scales. 
     In the art, there exist general-purpose decision support systems for healthcare decision analytics and support, such as clinical decision support systems and medical expert systems. The existing decision support systems are normally established for one type of decisions (e.g., clinical treatment decisions), and comprise limited data sources (e.g., existing hospital operation data). Nonetheless, there lacks a system in the art that comprises an integration of techniques and various data sources to provide comprehensive intelligent decision analytics and support functions for different users in healthcare, e.g., when dealing with practical decision analytics problems that involve complex-systems behaviors due to the large number of intrinsic and extrinsic impact factors exerting influences on healthcare outcomes in different temporal and spatial scales. 
     The objective of the present invention is to provide methods and apparatus for developing, analyzing, investigating, and advising healthcare and well-being related decisions. In particular, the present invention provides the architecture of systems in either stand-alone or distributed/collaborative/pervasive settings, the components of the systems and their underlying processes and couplings, the computational techniques built into the methods, input data sources integrated into and output results produced and distributed by the systems as well as the apparatus for carrying out the corresponding user problem description and interaction, contextual information collection, decision problem extraction/inference and recommendation, data access and collection, data integration and processing, data-driven inferences and simulation, intelligent computations, decision analytics, and decision support to generating solutions to various healthcare analytics and decision-making problems of varying complexity. Here, two embodiments are described later as working examples to illustrate the present invention, i.e., the working of the methods and apparatus. One is to illustrate the working of the apparatus in performing decision analytics tasks and adaptive decision support in regional healthcare resource allocation that has the advantages of reducing healthcare performance disparities, and/or the optimization of resource usage and performance. 
     Another is to illustrate the working of the apparatus in generating adaptive operating room (OR) time block allocation solutions for a medical services-providing institution. The generated outputs are readily used to help ORs maintain a stable performance in the face of dynamically-changing and non-deterministic patient arrivals (e.g., due to geodemographic, environmental/climate, and socioeconomic variations). 
     Citation or identification of any reference in this section or any other sections of this application shall not be construed as an admission that such a reference is available as prior art for the present application. 
     SUMMARY OF INVENTION 
     The present invention contains methods, apparatus, and illustrative working embodiments for smart healthcare decision analytics and support. 
     Users of the present invention include healthcare service-providing organizations (e.g., hospitals, clinics, and labs), healthcare workers (e.g., general practitioners and specialists, and nurses), researchers, decision makers (e.g., administrators), patients, general users, and other relevant stakeholders (e.g., insurance companies, pharmacy companies, and medical apparatus and instruments companies). The decision analytics and support problems will vary for different users. Hence, in a first aspect, the present invention provides methods and apparatus (1) for users to present decision analytics problems at hand via centralized, distributed, and/or pervasive/mobile manners, (2) to extract and/or infer the contextual information for users and analytics problem, such as users&#39; profiles and analytics scales of the problems (e.g., decision analytics and supports for a region or for a hospital) during the user-system interaction process, (3) to automatically extract, infer, and/or refine objective(s), problem types, issues, sub-questions, criteria, requirements (e.g., indicators and measurements), and corresponding decision/control variables and constraints for the decision analytics problems from users&#39; problem sketches or descriptions, (4) to record and recall encountered users and to automatically identify and/or infer the types of subsequent/new users with their profiles and relate their needs (i.e., required decision analytics and support problems) together, in doing so to intelligently and automatically infer and recommend the decision analytics problems for subsequent/new users, and (5) to gather and incorporate user-initiated feedback (e.g., on intermediate result evaluation) and/or intelligently/automatically infer feedback on behalf of users, during the analytics processes. 
     The core and the most important system of the apparatus in the present invention is the healthcare decision analytics and support system (HDASS). HDASS receives the input information from users through either an integrated or a distributed/pervasive user-HDASS interface. With an analytics engine, HDASS automatically extracts and/or infers the desired type of the problems (e.g., whether which are optimization problems or statistical analysis problems) and desired issues to be tackled for users (e.g., which candidate techniques should be chosen and how the selected techniques are individually/sequentially/iteratively, or integrally used) from the input information; automatically determines, accesses, retrieves, organizes, and preprocesses required data for analytics; automatically generates analytics solutions, performs the analytics tasks based on the empirical and secondary data stored, maintained, and integrated in the information management system (IMS), and intelligently fine-tunes the solutions according to users&#39; criteria, requirements, and feedbacks on intermediate results during the analytic, investigating, and/or simulation processes. At the end of the analytics process, HDASS returns the analytics results in forms of comprehensive textual and/or graphical reports, with outputs of recommendations, scenario analysis, predictions, evaluations, visualizations, intelligent data analysis, data mining, and statistical analysis. Furthermore, it retains resulting healthcare decision analytics solutions (i.e., in terms of the generalized flows of problem-solving with respect to the computational types, issues, and sub-questions of the decision analytics problems, instead of the exact instances of the problems) in its solution repository, such that the accumulatively aggregated solutions in the repository can be stored, inter-connected, updated, and utilized for tackling similar or more complex types, issues, and sub-questions of future problems. 
     The analytics engine in HDASS implements and intelligently deploys three main groups of analytics methods, although these do not exclude other groups of methods. The first and the most important group of methods are for strategic analysis. Exemplified methods in this group include techniques for algorithmic/mechanism design, exact or approximate queueing modeling, discrete event simulation, optimization (e.g., mathematical programming), and autonomy-oriented computing (AOC)-based modeling. The intelligently configured and integrated strategic analysis methods model practical healthcare analytics problems, investigate and evaluate healthcare and well-being related decisions that involve many dynamically-interacting intrinsic and extrinsic impact factors exerting influences on the performance of the complex healthcare systems in multiple temporal and spatial scales, and predict and simulate the effects of such healthcare decisions, so as to produce evidence-based recommendations and/or analytics support as well as for integrated implementation in healthcare services. This group of methods, intelligently integrated with the following two groups if needed, is especially useful in performing the tasks/steps of solving complex decision analytics problems. The second group of analytics methods are intelligent data analysis methods containing artificial intelligence techniques, machine learning techniques, data mining techniques, and pattern recognition techniques. The third group of analytics methods are data-driven statistical analysis methods such as regression, ANOVA, structural equation modeling, and factor analysis. 
     Depending on different decision analytics and support problems, the three deployable groups of analytics methods will be intelligently and automatically utilized either individually/sequentially/iteratively or in any integrated manner, depending on the specific tasks at hand. For instance, in some cases, the results of data-driven analysis will be used to support the further intelligent data analysis and the strategic analysis tasks; the intelligent data analysis results will also feed the strategic analysis methods. In other cases, the three groups of analytics methods, as well as their underlying possessed techniques, will be integrally utilized, e.g., the simulation, evaluation, and/or prediction results obtained from the strategic analysis module will be further investigated by employing data-driven analysis and/or intelligent data analysis. 
     Data stored, maintained, and integrated in IMS is collected from five major data sources related to healthcare. The first typical data sources included in the present invention are the existing hospital operation databases, such as electronic health record databases (EHR), electronic medical record (EMR) databases, hospital information system (HIS) databases, and management information system (MIS) databases. Ubiquitous patient health data is the second major data source. Ubiquitous patient health data includes personal information (e.g., personal profiles and daily activities) and patient health information routinely tracked/collected from ubiquitous devices (e.g., smart phones), and clinical and patient information (e.g., experiences of treatments and/or medication) distributed in health related physical and online communities (e.g., forums). IMS also contains data from the secondary service providers related to healthcare, such as community health service centers, rehabilitation centers, insurance companies, pharmacy companies, and medical apparatus and instruments companies. Since the demands of healthcare are constantly affected by certain extrogenous factors to the healthcare system, primary and secondary data on the determinants for healthcare such as demographic (usually represented by census data), environmental/climate, and socioeconomic related factors and human behaviors, is gathered, stored, and tracked in IMS. The fifth and final data source integrated in the present invention is the academic/medical research or other relevant databases such as Medline and PubMed, which will feed the decision analytics and support system with prior academic/medical research findings, and thus they are utilized for healthcare evidential inferences, hypothesis generation, model construction, as well as mining and/or discovering explicit and implicit relationships among impact factors/determinants/conditions and decision parameters and variables, e.g., drug-drug interactions in drug development. 
     In IMS, those data sources are collected, cleaned, and integrated through an input information bus (that is implemented either locally or remotely via network connectivity). The preprocessed data in IMS then supports the decision analytics and support tasks in HDASS by its standard query through an output information bus (that is implemented either locally or remotely via network connectivity). 
     Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. 
     The invention includes all such variations and modifications. The invention also includes all of the steps and features referred to or indicated in the specification, individually or collectively and any and all combinations or any two or more of the steps or features. 
     Throughout this specification, unless the context requires otherwise, the word “comprise” or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers. It is also noted that in this disclosure and particularly in the claims and/or paragraphs, terms such as “comprises”, “comprised”, “comprising” and the like can have the meaning attributed to it in U.S. Patent law; e.g., they can mean “includes”, “included”, “including”, and the like; and that terms such as “consisting essentially of” and “consists essentially of” have the meaning ascribed to them in U.S. Patent law, e.g., they allow for elements not explicitly recited, but exclude elements that are found in the prior art or that affect a basic or novel characteristic of the invention. 
     Furthermore, throughout the specification and claims, unless the context requires otherwise, the word “include” or variations such as “includes” or “including”, will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers. 
     Other definitions for selected terms used herein may be found within the detailed description of the invention and apply throughout. Unless otherwise defined, all other technical terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which the invention belongs. 
     Other aspects and advantages of the invention will be apparent to those skilled in the art from a review of the ensuing description. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The above and other objects and features of the present invention will become apparent from the following description of the invention, when taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  shows an overview of the apparatus (consisting modules of the Smart User Interface  103 , HDASS  104 , and IMS  105 ) and its interactions (e.g., data communications) with users (such as Health Workers  100 , Stakeholders  101 , Advisory Groups  102 , and healthcare service-providing organizations), and at least five groups of centralized/distributed/pervasive/mobile data sources (consisting of the Existing Hospital Operation Databases  106 , Ubiquitous Patient Health Data Sources  107 , data sources of Secondary Service Providers  108 , data sources of Determinants for Healthcare  109 , and Academic/Medical Research Databases  110 ). 
         FIG. 2  shows the components of the Smart User Interface module  103 , of the Healthcare Decision Analytics and Support System (HDASS) module  104 , and of the Information Management System (IMS) module  105 . 
         FIG. 3  shows the functions and examples of integrated techniques provided by the component of Analytics Engine  207  inside the HDASS module  104 . 
         FIG. 4  shows the components, functions, and employed techniques in the first embodiment illustration of the present invention on adaptive operating room (OR) time block allocation. 
         FIG. 5  shows the produced OR scheduler with a designed feedback mechanism in Analytics Engine  207  inside the HDASS module  104  in the first embodiment illustration of the present invention. 
         FIG. 6  shows the adjusted window mechanism for updating the OR time blocks for urgent surgeries in the produced Adaptive Oreg. Scheduler  403  as the embodiment of Algorithmic/Mechanism Design  329  in Analytics Engine  207  inside the HDASS module  104  in the first embodiment illustration of the present invention. 
         FIG. 7  shows the embodiment of Queueing Model  330 , a Multi-Priority, Multi-Server, Non-Preemptive Queueing Model  402  with an entrance control mechanism in Analytics Engine  207  inside the HDASS module  104  in the first embodiment illustration of the present invention. 
         FIG. 8  shows a generated Decision Evaluation Output  218  about the simulated average waiting time versus the actual average waiting times in one year (δ a =2) in the first embodiment illustration of the present invention. 
         FIG. 9  shows a generated Decision Evaluation Output  218  about the number of bumped surgeries with and without the adaptive strategy in one year (δ 0 =2, Δp=Δq=1, T=1 week, θ 1 =θ 2 =2) in the first embodiment illustration of the present invention. 
         FIG. 10  shows a generated Decision Evaluation Output  218  about the number of OR time blocks allocated to urgent surgeries with the adaptive strategy in one year (δ 0 =2, Δp=Δq=1, T=1 week, θ 1 =θ 2 =2) in the first embodiment illustration of the present invention. 
         FIG. 11  shows a generated Decision Evaluation Output  218  about the effectiveness of ORs with different initial urgent OR time blocks (where AS denotes adaptive strategy; Δp=Δq=1, T=1 week, θ 1 =θ 2 =2) in the first embodiment illustration of the present invention. 
         FIG. 12  shows a generated Decision Evaluation Output  218  about the effectiveness of ORs with different thresholds (Δp=Δq=1, δ e =1, T=1 week) in the first embodiment illustration of the present invention. 
         FIG. 13  shows a generated Decision Evaluation Output  218  about the effectiveness of ORs with different step sizes (θ 1 =θ 2 =2, δ e =1, T=1 week) in the first embodiment illustration of the present invention. 
         FIG. 14  shows the components, functions, and employed techniques for adaptive regional healthcare resource allocation in the second embodiment illustration of the present invention. 
         FIG. 15  shows the embodiment of Structural Equation Modeling  340  in Analytics Engine  207  inside the HDASS module  104  to investigate the relationships between geodemographic profiles and healthcare service characteristics. 
         FIG. 16  shows a generated Statistical Analysis Output  221  of the structural equation modeling testing results in the second embodiment illustration of the present invention. 
         FIG. 17  shows the embodiment of AOC-Based Model  333  in Analytics Engine  207  inside the HDASS module  104  in the second embodiment illustration of the present invention. 
         FIG. 18  shows the embodiment of Queueing Model  330  in Analytics Engine  207  inside the HDASS module  104  for modeling the operations of hospitals. 
         FIG. 19  shows the city-hospital bipartite network. This information is utilized by autonomous behavior-based entities as the environment input during their behavioral selection as in the embodiment of AOC-Based Model  333  in the second embodiment illustration of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF INVENTION 
     The present invention is not to be limited in scope by any of the specific embodiments described herein. The following embodiments are presented for exemplification only. 
       FIG. 1  illustrates schematically three of key modules for the smart healthcare decision analytics and support apparatus, i.e., the Smart User Interface  103 , the Healthcare Decision Analytics and Support System (HDAMSS) module  104  and the Information Management System (IMS) module  105 , and its interactions with users (wherein comprising Health Workers  100 , Advisory Groups  102 , healthcare service-providing organizations, and other Stakeholders  101 ) and healthcare related data collected from Existing Hospital Operation  106 , Ubiquitous Patient Health Data Sources (e.g., patient online communities)  107 , Secondary Service Providers (e.g., insurance companies and pharmacy companies)  108 , Determinants for Healthcare (e.g., geodemographic, environmental/climate, socioeconomic related behavior)  109  and Academic/Medical Research Databases (e.g., Medline and PubMed)  110 . 
     Smart User Interface  103  is capable of (1) permitting users to access the smart healthcare decision analytics and support apparatus in any of centralized, distributed, and/or pervasive/mobile manners, and to input their sketches or descriptions on analytics and decision problems as well as to optionally modify solution repository, settings, and configurations, (2) automatically extracting and/or inferring the contextual information for users and analytics problems at hand, (3) automatically extracting, inferring, and/or refining objective(s), problem types, issues, sub-questions, criteria, requirements (e.g., indicators and measurements), and corresponding decision/control variables and constraints for the decision analytics problems, (4) intelligently and automatically inferring and recommending the decision analytics problems for subsequent/new users, and (5) extracting and/or inferring feedback from users (e.g., on intermediate result evaluation) and/or intelligently/automatically inferring feedback during the analytics process. 
     Upon the automatically extracted and/or inferred inputs from Smart User Interface  103  on Decision Analytics Problem Description  111 , Contextual Information  112 , Criteria and Requirements  113 , and Feedback  114 , the HDASS module  104  provides Intermediate Results  115  during the analytics process and final results in the forms of textual and/or graphical Comprehensive Report  116 , Decision Recommendation Report  117 , Decision Scenario Analysis Output  118 , Decision Prediction Output  119 , Decision Evaluation Output  120 , Simulation Visualization Output  121 , Intelligent Data Analysis and Data Mining Output  122 , and/or Statistical Analysis Output  123 . Prior to doing so, the HDASS module  104  will perform Data Query  124  to the IMS module  105  in order to retrieve the recorded healthcare related information in the form of Standard Query Results  126 . Such information will be extracted based on the collected operational data  124  from the Existing Hospital Operation  106 , Ubiquitous Patient Health Data  107 , Secondary Service Providers  108 , Determinants for Healthcare  109 , and Academic/Medical Research Databases  110 . Furthermore, it will retain resulting healthcare decision analytics solutions (i.e., in terms of the generalized flows of problem-solving with respect to the computational types, issues, and sub-questions of the decision analytics problems, instead of the exact instances of the problems) in its solution repository, such that the accumulatively aggregated solutions in the repository can be stored, inter-connected, updated, and utilized for tackling similar or more complex types, issues, and sub-questions of future problems. 
     The operations of the Smart User Interface  103 , of the Healthcare Decision Analytics and Support System (HDASS) module  104 , and of the Information Management System (IMS) module  105  are carried out by their components as presented in the drawing of  FIG. 2 . 
     Users access the present invention in any of centralized, distributed, and pervasive/mobile manners aided by User Accessing  200  within Smart User Interface  103  module. Functions of Collecting Decision Analytics Problem Description  201  permit users to present decision analytics problems at hand, and then automatically extract, infer, and/or refine objective(s), problem types, issues, sub-questions, criteria, requirements (e.g., indicators and measurements), and corresponding decision/control variables and constraints for the decision analytics problems. At the same time, User Profiling  202  is able to extract and/or infer the contextual information for users and analytics problem, such as users&#39; profiles and analytics scales of the problems (e.g., decision analytics and supports for a region or for a hospital) during the user-system interaction process. With the functions provided by Inferring and Recommending User&#39;s Needs in Decision Analytics  203 , Smart User Interface  103  is able to record and recall encountered users and to automatically identify and/or infer the types of subsequent/new users with their profiles and relate their needs (i.e., required decision analytics and support problems) together, so as to intelligently and automatically infer and recommend the decision analytics problems for subsequent/new users. Smart User Interface  103  runs consistently during the analytics processes to gather and incorporate user-initiated feedback (e.g., on intermediate result evaluation), and/or intelligently/automatically infer feedback on behalf of users by Gathering User-Initiated Feedback or Intelligently Inferring Feedback on Intermediate Results  204 . 
     HDASS  103  module offers methods and apparatus for recognizing and/or inferring decision analytics problems, automatically building and fine-tuning solutions, supporting techniques, and automatically generating various kinds of outputs (e.g., decision recommendation output and statistical analytics output) for users. With a centralized/distributed/pervasive User-HDASS Interface  205 , the output of Smart User Interface  103  (i.e., Decision Analytics Problem Definition  111 , Contextual Information  112 , Criteria and Requirements  113 , Feedback  114 ) will be temporarily stored in Input Information Repository  201 , from which Solution Builder  210  within Analytics Engine  207  will then be invoked to (1) recognize and/or infer problems (e.g., types, issues, and sub-questions) to be tackled, to select suitable solutions and intelligently integrate the suitable techniques (i.e., generate a solution for an analytics task), (2) to determine necessary data sources for analytics and access, retrieve, organize, and preprocess the needed data queried by HDASS-IMS Interface  209  from IMS  105  to parameterize and support various analytics and decision making tasks, (3) to operate the embodiments of Strategic Analysis  211 , Intelligent Data Analysis  212 , Data-Driven Statistical Analysis  213  individually/sequentially, or in an integrated manner upon the treated data, (4) to automatically and intelligently fine-tune the solution as well as the parameter settings in the solution according to users&#39; criteria and requirements and the extracted/inferred contextual information, (5) returns intermediate and final analytics results automatically generated by modules of Comprehensive Report  214 , Decision Recommendation Output  215 , Decision Scenario Analysis Output  216 , Decision Prediction Output  217 , Decision Evaluation Output  218 , Simulation Visualization Output  219 , Intelligent Data Analysis and Data Mining Output  220 , Statistical Analysis Output  221 , and Intermediate Results  222 , and (6) retains resulting healthcare decision analytics solutions (i.e., in terms of the generalized flows of problem-solving with respect to the computational types, issues, and sub-questions of the decision analytics problems, instead of the exact instances of the problems) in its solution repository, such that the accumulatively aggregated solutions in the repository can be stored, inter-connected, updated, and utilized for tackling similar or more complex types, issues, and sub-questions of future problems. 
     The IMS module  105  collects, preprocesses, and maintains hospital operation databases such as EHR  241 , EMR  242 , HIS  243  and MIS  244 , Ubiquitous Patient Health Data Sources  245 , Secondary Service Providers&#39; Data Sources  246 , Census Data Sources  247 , and Academic/Medical Research Databases  248 . It contains Input Information BUS  249  for handling database input  258  to  265 , and Output Information BUS  240  for handing communications  250  to  257  between HDASS  104  and IMS  105 , in centralized, distributed, and/or pervasive/mobile manners. 
     The functions and examples of integrated techniques provided by Analytics Engine  207  of the HDASS module  104  are presented in the drawing of  FIG. 3 . Identifying Problem Types  300  sub-module within Solution Builder  210 , supported by the functions of Semantic Analysis  312  (e.g., XML-based, HL 7 Standards-based) and Problem Classification and Matching  313 , will automatically infers the type/scope of analytics problems (e.g., optimization problems or statistical analysis problems or a combination/integration of both problem types) and issues/sub-questions to be tackled from Input Information Repository  206 . 
     Later on, with respect to the identified problem types, scope, issues, and sub-questions, Determining Solution  301  sub-module will choose suitable existing solutions and/or intelligently extend/revise/customize/integrate the suitable techniques (i.e., generate a solution for an analytics task) to build new solutions by calling Retrieving Existing Solutions  314 , Meta-Knowledge About the Relationship Between Problems and Solutions  315 , and Required Analytics Techniques Extension/Customization/Revise/Integration  316 . The embodiments of techniques categorized in Strategic Analysis  211 , Intelligent Data Analysis  212 , Data-Driven Statistical Analysis  213  will be used individually/sequentially, or in an integrated manner for solving decision analytics problems at hand. 
     During the analytics process, Determining Solution  301  sub-module will monitor and evaluate the automatically built solution based on users&#39; criteria, requirements, and feedback on intermediate results, so as to automatically and intelligently improve the solution by calling Fine-Tuning Solution  318 . The updated or new-built solutions will be incrementally stored and maintained in Maintaining Solution  304  sub-module by calling Updating Personalized Solution Information  323  and Updating Technique Repositories of Strategic Analysis/Intelligent Data Analysis/Data-Driven Statistical Analysis  324 . This function of the present invention allows for the solutions to be accumulatively aggregated for future re-use. 
     Solution Builder  210  also determines needed data sources for analytics by Determining Required Data Sources  319  within Acquiring Required Data  302 , and prepares the needed data by calling Required Data Accessing, Retrieving, Organizing, and Preprocessing  320  to support various data analytics and data-driven modeling steps. 
     Before executing techniques already chosen and extended/customized/revised/integrated in solution, Configuring Solution  303  sub-module of Solution Builder  210  will initialize and parameterize the techniques with related variables by calling Initializing and Parameterizing Techniques in Solution  321 . As well, during the analytics process, Configuring Solution  303  sub-module will automatically and intelligently fine-tune the parameter settings in the solution according to users&#39; criteria and requirements, contextual information, intermediate analytics results  232 , and users&#39; feedback by calling Fine-Tuning Parameter Settings  322 . 
     After the intelligent selection and composition of decision analytics and support techniques in providing solution(s) by Solution Builder  210 , Analytics Engine  207  will execute the embodiments of techniques categorized as Strategic Analysis  211 , Intelligent Data Analysis  212 , and Data-Driven Statistical Analysis  213 . 
     In Strategic Analysis  211 , the functions  305  include Modeling  325 , Evaluation  326 , Simulation  327 , and/or Predication  328  of selected strategies, where techniques from Computational Modeling and Simulation Analysis Technique Repository  306 , as exemplified by Algorithmic/Mechanism Design  329 , Queueing Model  330 , Discrete Event Simulation  331 , Optimization such as mathematical programming  332 , and AOC-Based Model  333 , will be used The Strategic Analysis  211  phase will be carried out separately, or based on the results  229  and  231  from the Intelligent Data Analysis  212  and the Data-Driven Statistical Analysis  213  phases and vice versa (i.e., providing results to Intelligent Data Analysis  212  and Data-Driven Statistical Analysis  213 ). In Intelligent Data Analysis  212 , the data analysis functions will be achieved by utilizing techniques in Intelligent Data Analysis Technique Repository  308 , as exemplified by Artificial Intelligence Techniques  334 , Machine Learning Techniques  335 , Data Mining Techniques  336 , and Pattern Recognition Techniques  337 . The Intelligent Data Analysis  212  phase will also be executed based on the result  230  from the Data-Driven Statistical Analysis  213  phase (and vice versa), in which techniques from Data-Driven Statistical Analysis Technique Repository  310 , as exemplified by Regression  338 , ANOVA  339 , Structural Equation Modeling  340 , and Factor Analysis  341 , will be used. 
     In what follows, two embodiment illustrations on the methods and apparatus of this invention will be described to detail their implementations. The first embodiment illustration (as presented in the drawing of  FIG. 4 ) shows the working of the apparatus in developing an adaptive mechanism (as presented in the drawings of  FIGS. 5 and 6 ) for allocating OR time blocks to cope with non-deterministic patient arrivals. In order to exemplify the performance of the adaptive strategy, this embodiment illustration of the invention automatically builds, parameterizes, and executes a solution comprising techniques of queueing model and discrete-event simulation for the inferred decision analytics problem, contextual information, criteria, and requirements. Specifically, this embodiment illustration of the present invention (1) automatically builds a queueing model (a multi-priority, multi-server, non-preemptive queueing model with an entrance control mechanism as presented in the drawing of  FIG. 7 ) based on the real-world practices, e.g., those of cardiac surgery operating rooms in Hamilton Health Sciences Centre (HHSC) in Ontario as an example, and (2) later on automatically configures the embodiment of queueing model and carries out discrete-event simulations. 
     The second embodiment illustration of the present invention (as presented in the drawing of  FIG. 14 ) demonstrates the processes of designing, analyzing, evaluating, and supporting adaptive regional healthcare resource allocation strategies for maintaining a stable healthcare performance and reducing wait time disparities in a region. Specifically, taking the cardiac surgery services in Ontario, Canada as an example, this embodiment illustration of the invention (1) identifies/infers the decision analytics problem, contextual information, criteria, and requirements from problem sketch/description as an integration of data analysis, data-driven modeling, and simulation based optimization problem, (2) automatically builds a solution includes techniques of structural equation modeling (SEM), autonomy-oriented computing (AOC), queueing model, and discrete-events simulation, as well as their integration manner and coupled flow, and (3) carries out the embodiments of selected, revised, customized, initialized, and parameterized techniques included in the solution. Specifically, this embodiment illustration of the invention (1) automatically produces/recommends hypotheses (as presented in the drawing of  FIG. 15 ) based on prior studies and uses the SEM technique to investigate the relationships between geodemographic profiles (e.g., population size, age profile, and service accessibility) and healthcare characteristics (e.g., arrival, operating room capacity, physician supply, and wait time), (2) based on the generated findings of SEM testing and decision theory, automatically builds and configures a specific autonomy-oriented computing (AOC)-based model for the cardiac surgery system that comprises autonomous behavior-based entities of patients, general practitioners, and hospitals along with their behaviors and interactions (as presented in the drawing of  FIG. 17 ), (3) automatically builds and configures a queueing model for hospital OR operations (as represented in the drawing of  FIG. 18 ), (4) automatically performs discrete-events simulations on the AOC-based model to investigate the temporal-spatial hospital service utilization patterns, to capture the complex emergent behavior of the exemplified healthcare system, to show the dynamics of patient arrivals and hospital performance, and hence, to shed lights on designing better resource allocation strategies for reducing wait time disparities in a region, and (5) automatically and intelligently fine-tunes the parameter settings of embodiments of aforementioned techniques to provide enhanced results that meet users&#39; needs. 
     Embodiment Illustration One 
     Methods and Apparatus for Adaptive OR Time Block Allocation Analytics and Decision Support 
     Operating room (OR) is one of the major cost areas in medical services providing institutions such as hospitals. Therefore, improving OR performance is particularly important for lowering the cost and providing need-based services in a timely manner, and therefore attracts big attention from hospital administrators. 
     Imagine that you are a hospital administrator at Hamilton Health Science Centre in Ontario. You would like to make a reasonable and evidence-based decision on how to improve the hospital&#39;s OR time block allocation method to cope with dynamically-changing/non-deterministic patient arrivals. You seek the help from the present invention, and sketch/describe your decision analytics and support problem like this: 
     “How to adaptively allocate operating rooms time blocks to maintain a stable OR performance in the face of dynamically-changing/non-deterministic patient arrivals?” 
     After receiving users&#39; request and problem description, the present invention automatically and intelligently identifies the problem types, builds a solution, employs/extends/customizes techniques for decision analysis, and finally returns an adaptive OR time block allocation method with necessary support (e.g., method evaluation output) outputs to you. 
     In what follows, this embodiment illustration will show the operational processes and apparatus of the present invention that produce an adaptive method for allocating OR time blocks after receiving a user&#39;s (i.e., you as in the aforementioned scenario) problem description. 
     Detailed Description in the First Embodiment Illustration 
     The drawing of  FIG. 4  presents schematically the key modules in the first embodiment illustration, i.e., the Smart User Interface  103 , the Healthcare Decision Analytics and Support System (HDAMSS) module  104  and the Information Management System (IMS) module  105 , and its interactions with the user (i.e., as a Health Workers  100 ) and healthcare related data collected from Existing Hospital Operation  106 . 
     After the user accesses the smart healthcare decision analytics and support apparatus via User Accessing  200  of Smart User Interface  103  in any of centralized, distributed and pervasive/mobile manners, Collecting Decision Analytics Problem Description  201  of Smart User Interface  103  will collect the general description of the problem (i.e., how to adaptively allocate operating rooms time blocks to maintain a stable OR performance in the face of dynamically-changing/non-deterministic patient arrivals?). At the same time, User Profiling  202  of Smart User Interface  103  extracts and/or infers the contextual information for the user and the analytics problem at hand, such as the user type is a hospital administrator, the work place and analytics context is cardiac surgery ORs in Hamilton Health Science Centre. The objective(s), problem types, issues, sub-questions, criteria, requirements (e.g., indicators and measurements), and corresponding decision/control variables and constraints for the decision analytics problem will be automatically extracted, inferred, and/or refined from users&#39; problem sketches or descriptions and extracted and/or inferred contextual information. For instance, the objective should be to provide an adaptive method for OR time block allocation. Sub-questions inferred will involve (1) how to characterize dynamically-changing/non-deterministic patient arrivals, (2) how to characterize the operations of ORs, and (3) what a mechanism helps to adaptively allocate the OR time block allocation for urgent/non-urgent patients, because reserving more time blocks than the real needs may cause a lower OR utilization and longer waiting time for non-urgent surgeries, whereas reserving insufficient time blocks may increase the risk of urgent patients, incur high cancellations of non-urgent surgeries. The criteria and requirements include the trade-off between the number of bumped non-urgent surgeries and unused urgent time blocks for ORs time block allocation, the average wait time for measuring the performance of ORs, and the wait time dynamics of ORs with/without the produced adaptive OR time block allocation method. 
     Solution Builder in the First Embodiment Illustration 
     Upon the inputs of Decision Analytics Problem Description  111  (e.g., objective(s), problem types, issues, and sub-questions), Contextual Information  112  (e.g., users&#39; profiles and analytics context for problems), and Criteria and Requirements  113  from Smart User Interface  103 , Solution Builder  210  of HDASS module  104  identifies and/or infers problem types based on the functions provided by Semantic Analysis  312  and Problem Classification and Matching  313  within Solution Builder  210 . According to the problem sketch from the user and the inferred objective, problem type, issues, sub-questions, contextual information, criteria, requirements (e.g., indicators and measurements), and corresponding decision/control variables and constraints, the problem will be solved by integrating mechanism design-based optimization along with simulation-based evaluation and ORs&#39; wait time dynamics demonstration. 
     To build a solution to achieve the analytics objective and to answer the sub-questions, apparatus of Retrieving Existing Solution from Solution Repository  314  and Meta-Knowledge About the Relationship Between Problems and Solutions  315  within Determine Solution  301  automatically derives that techniques of Queueing model  330  and Discrete Event Simulation  331  from Computational Modeling and Simulation Analysis Technique Repository  306  within Strategic Analysis  211  are useful approaches to modeling and simulating operations of ORs existing solutions. The Solution Builder  210  then automatically and intelligently builds a solution that sequentially utilize Algorithmic/Mechanism Design  329  to produce an adaptive OR time block allocation strategy, Queueing model  330  to model the operations of ORs, and Discrete Event Simulation  331  to simulate the embodiment of queueing model with an adaptive OR time block allocation strategy so as to evaluate (in terms of the trade-off between the number of bumped non-urgent surgeries and unused urgent time blocks for ORs time block allocation and the average wait time for measuring the performance of ORs) and fine-tune (through functions of Fine-Tuning Solution  318 ) the produced adaptive OR time block allocation method. 
     Accordingly, Acquiring Required Data  302  of Solution Builder  210  determines and accesses necessary data sources for developing, parameterizing, analyzing, and evaluating the adaptive OR time block allocation method aided by the functions of Determining Required Data Sources  319  and Required Data Accessing, Retrieving, Organizing and Preprocessing  320 . 
     IMS  105  has collected and stored/maintained necessary data for parameterizing, simulating, and evaluating the method of adaptive OR time block allocation about the existing operation of Hamilton Health Sciences Centre (HHSC) in Centralized/Distributed/Pervasive Management Information System (MIS) Databases  244 . Specifically, HHSC contains 6 specialized surgeons and 2 operating rooms, and provides 1400 cardiac surgeries annually. Table 1 shows a summary of the HHSC cardiac surgery data. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 The statistics of cardiac surgery in HHSC, 2004 (UMW/SMW/EMW: 
               
               
                 median waiting time of urgent/semi-urgent/elective surgeries). 
               
            
           
           
               
               
               
            
               
                   
                 Performance 
                 Data 
               
            
           
           
               
               
               
               
               
            
               
                   
                 Indicator 
                 UMW 
                 SMW 
                 EMW 
               
               
                   
                   
               
            
           
           
               
            
               
                 Waiting Time (days) 
               
            
           
           
               
               
               
               
               
            
               
                   
                 Quarter 1 
                 2 
                 9 
                 48 
               
               
                   
                 Quarter 2 
                 5 
                 9 
                 48 
               
               
                   
                 Quarter 3 
                 3 
                 9 
                 41 
               
               
                   
                 Quarter 4 
                 2 
                 7 
                 36 
               
            
           
           
               
            
               
                 Queue Length (at the end of a month) 
               
            
           
           
               
               
               
            
               
                   
                 Quarter 1 
                 156 
               
               
                   
                 Quarter 2 
                 159 
               
               
                   
                 Quarter 3 
                 149 
               
               
                   
                 Quarter 4 
                 147 
               
            
           
           
               
            
               
                 Cancellations 
               
            
           
           
               
               
               
            
               
                   
                 Bumped Non- 
                 77 
               
               
                   
                 urgent Surgeries 
               
            
           
           
               
            
               
                 Service Time 
               
            
           
           
               
               
               
            
               
                   
                 Average 
                 4.6 hours 
               
               
                   
                   
               
            
           
         
       
     
     Aided by the functions of Initializing and Parameterizing Techniques in Solution  321  of Configuring Solution  303  within Analytics Engine  207 , this embodiment illustration utilizes the data to initialize the parameter settings of adaptive OR time block allocation method, the embodiment of queueing model, and discrete event simulation. 
     Strategic Analysis in the First Embodiment Illustration 
     To achieve the objective of adaptive OR time block allocation, one embodiment of Algorithmic/Mechanism Design  329  in Analytics Engine  207  within HDASS  104  is an adaptive OR time block allocation scheduler  403  based on a feedback mechanism (illustrated in  FIG. 5 , where the time period is indicated in brackets). The main idea of this embodiment is to adjust time blocks for urgent surgeries periodically based on the feedback information corresponding to the arrivals of different priority groups and the effectiveness of ORs. 
     Specifically, this adaptive method utilizes an adjusted window mechanism  404 , which is shown in  FIG. 6 . When the OR scheduler makes a decision on allocating time blocks for the coming time period T, the information in the past time period T−1 will be fed back to the OR scheduler. If the number of bumped non-urgent surgeries is larger than a threshold θ 1  in T−1, the scheduler will increase the number of time blocks (R T ) for urgent surgeries with a step size ΔP in T. If the number of unused urgent time blocks is larger than a threshold θ 2 , the scheduler will decrease the time blocks for urgent surgeries with a step size Δq in T. 
     In order to exemplify the performance of the disclosed adaptive method, this embodiment illustration has specifically built a queueing model  405  (shown in  FIG. 7 ) based on the empirical data on cardiac surgery operating rooms in Hamilton Health Sciences Centre 1  (HHSC). In other words, this specific queueing model is parameterized as follows: (1) there are 2 homogeneous (in terms of the same service rate), (2) each OR has 2 time blocks on average per day, and (3) there are 5 working days per week. The 1400 arrivals each year for cardiac surgeries are categorized into three priority groups: urgent (U), semi-urgent (S), and elective (E). According to the historical data from Alter D A, Cohen E A, Wang X, Glasgow K W Slaughter P M, Tu J V. Cardiac procedures. In: Tu J V, Pinfold S P, McColgan P, Laupacis A, eds, Access to Health Services in Ontario. 2nd ed ICES Atlas, 2006, the ratios of U, S, and E patients are 0.23, 0.6, and 0.17, respectively. In addition, because of the seasonable factors (e.g., weather), the number of patient arrivals in winter is about one-quarter more than those in other seasons. Similar to most of the prior work, here in this embodiment illustration it is also parameterized that the arrival rate λ i  of each priority group i (i ε{U,S,E}) follows a suitable Point Process (e.g. Poisson process), and the service time of each OR follows an arbitrarily distributed random variable (e.g. exponential distribution) with mean 1/μ.  1 http://www.hamiltonhealthsciences.ca/ORs 
     Since a U patient has the highest priority, he/she should be immediately settled to an available OR. In the real OR operating, a number of OR time blocks are reserved to cope with the timely needs of U patients. In model, used herein, this embodiment illustration utilizes δ e  to denote the initial number of time blocks reserved for urgent surgeries. However, if all the ORs are unavailable, the U patient should wait and bump the first available OR block. S and E patients are scheduled by surgeons following a priority based service principle. Specifically, a new coming non-urgent (i.e., S and E) patient will be first assigned to a surgeon j (jε[1,6] in our case denotes one of the 6 surgeons) with a probability p j,Ū  (the symbol Ū denotes non-urgent patients). Then, the patient will stay in the queue of surgeon j. According to the real operation, surgeons can only perform non-urgent surgeries in time blocks allocated to them in advance. Therefore, this embodiment illustration sets that a patient at the head of a queue j will move to the OR with a probability at the next time step. In this case, P j,Ū  and q j,Ū  follow constant distributions in the simulations. 
     To simulate the queueing model, the technique of Discrete Event Simulation  331  is utilized. The simulations are carried out based on the HHSC statistical data. In order to compare the performance, this embodiment illustration carries out the simulations under the same conditions after a single run and obtains the results as shown in the following. It can also perform multiple simulation runs and algorithmically aggregate the results. 
     System Outputs in the First Embodiment Illustration 
     Embodiments of System Output  208  within HDASS  104  include Decision Evaluation Output  218  for the embodiment of queueing model and the adaptive OR time block allocation method by simulations, Decision Recommendation Output  215  for result findings, and textual and/or graphical Comprehensive Report  214  comprising the simulation results, sensitivity analysis for key parameters (e.g., the adjustment step sizes and the thresholds) of the adaptive OR scheduling strategy, Decision Evaluation Output  218  and Decision Recommendation Output  215 . 
       FIG. 8  shows a Decision Evaluation Output  218  on evaluating the effectiveness of the adaptive OR time block allocation method in terms of average wait time. From  FIG. 8 , one can see that the simulated waiting time grows up continually at first (defined as an increasing phase illustrated as the shadowed area in  FIG. 8 ). Then, it goes relatively stable (defined as a stable phase illustrated as the unshaded area in  FIG. 8 ). As shown, the generated/simulated average waiting time in the stable phase matches well with the real one, which is calculated based on Little&#39;s theorem: L σ =λW σ (L σ is the average queue length; λ is the arrival rate; W σ is the average waiting time). The increasing phase of simulated average waiting time is because in this embodiment illustration, the initial waiting time of all the patients in the queues is set to zero in the simulations. Here, apart from the averages, other relevant measures such as percentiles and full probability distributions can also be evaluated and examined. 
       FIGS. 9 and 10  show another two outputs as exemplified Decision Evaluation Output  218 .  FIG. 9  shows that the adaptive method can reduce the number of bumped non-urgent surgeries.  FIG. 10  shows the changes in the OR time blocks for urgent surgeries with the adaptive strategy over time. In the simulations, the total numbers of bumped non-urgent surgeries are 68 and 129 with and without the adaptive strategy in one year, respectively. 
     Since the effectiveness of OR may be sensitive to the number of time blocks for urgent surgeries, the traditional OR time block allocation strategy and the embodiments of this illustration are compared.  FIG. 11 , as exemplified Decision Evaluation Output  218 , shows that the number of bumped non-urgent surgeries (BNS) is dropping along the increasing of time blocks for urgent surgeries in the traditional allocation strategy. In contrast, the number of unused urgent time blocks (UUB) is going up at the same time. Furthermore, the results generated from this invention are robust because no matter what the number of initial time blocks for urgent surgeries is, the OR can maintain a trade-off between the number of bumped non-urgent surgeries and the number of unused urgent time blocks. Therefore, with the embodiments of the current invention, hospitals can quickly adapt to the dynamically-changing patient arrivals to achieve a better OR performance. Table 2, as another exemplified Decision Evaluation Output  218 , shows that when the initial number of urgent time blocks is four and the updating step size is one week or one month, the ORs become more effective (i.e., small numbers of BNS and UUB). 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 The generated simulation results with different δ    
               
               
                 and T (week) (Δp = Δq = 1, θ 1  = θ 2  = 2 * T). 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 δ   
                 T = 1 
                 T = 4 
                 T = 12 
                 T = 52 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 No. BNS 
                 2 
                 68 
                 68 
                 80 
                 129 
               
               
                   
                   
                 4 
                 57 
                 42 
                 37 
                 54 
               
               
                   
                   
                 6 
                 52 
                 38 
                 19 
                 16 
               
               
                   
                 No. UUB 
                 2 
                 35 
                 40 
                 28 
                 10 
               
               
                   
                   
                 4 
                 38 
                 58 
                 61 
                 39 
               
               
                   
                   
                 6 
                 48 
                 70 
                 103 
                 105 
               
               
                   
                   
               
               
                   
                     indicates data missing or illegible when filed 
               
            
           
         
       
     
     In addition, the adjustment step sizes (ΔP and Δq) and the thresholds (θ 1  and θ 2 ) may also influence the adaptive strategy. According to  FIG. 12 , one of the exemplified Decision Evaluation Output  218 , larger adjustment thresholds result in a larger number of unused urgent time blocks, along with a smaller number of bumped non-urgent surgeries. This is reasonable because there is only one time block reserved for urgent surgeries initially. Therefore, larger thresholds make ORs less likely to increase the time blocks for urgent surgeries, and vice versa. The last exemplified Decision Evaluation Output  218 ,  FIG. 13 , shows that larger step sizes produce fewer bumped non-urgent surgeries and more unused urgent time blocks. The reason is that larger step sizes lead to allocating more time blocks for urgent surgeries at a time. Therefore, the number of bumped non-urgent surgeries will decrease while the unused urgent time blocks will increase at the same time. 
     The Decision Recommendation Output  215  in the first embodiment illustration contains recommendations that (1) the generated adaptive OR time block allocation method is able to more efficiently regulate the OR time block reservation in accordance with the changing pattern of patient arrivals, (2) hospital OR scheduler employed the generated adaptive method can maintain a better trade-off between the number of bumped non-urgent surgeries and the number of unused urgent OR time blocks, and (3) frequently adjusting the OR time block allocation (i.e., once per week or per month) can improve ORs&#39; effectiveness. The Comprehensive Report  214  comprising the above-mentioned evaluation outputs and decision recommendation outputs is generated for the user. 
     Embodiment Illustration Two 
     Methods and Apparatus for Adaptive Regional Healthcare Resource Allocation Analytics and Decision Support 
     Healthcare resource allocation is one of the most important problems for regional healthcare administrators. Prior research such as McIntosh T, Ducie M, Charles M B, Church J, Lavis J, Pomey M P, Smith N, Tomblin S: Population health and health system reform: needs-based funding for health services in five provinces. CPSR 2010, 4:42-6 has advocated to allocate resources according to the occurrence and harmfulness of diseases in the population, for instance, as assessed by the population-needs-based funding formula based on neighborhood geodemographic factors (e.g., population size, age profile, geographic accessibility to services, and educational profile). However, by examining traditional estimation methods for service needs such as introduced in prior research Kephart G Asada Y. Need-based resource allocation: different need indicator, different result? BMC Health Service Research 2009, 9:122, it is often noted that there exist substantial differences between estimated and real needs in some regions. A possible explanation for the biased estimation is that the needs estimation method is simply a linear combination of the considered factors, without considering how these factors interact with one another as well as patients&#39; behavior related to healthcare. 
     Imagine that you are a provincial/regional healthcare administrator in Ontario. You find that the current resource allocation method for cardiac surgery services is static and results in a gap between estimated and real needs in regions. Therefore, you would like to make a reasonable and evidence-based decision on regional resource allocation for cardiac surgery to shorten the regional average wait time and reduce wait time disparities. You seek the help from the present invention, and sketch/describe your decision analytics and support problem like this: 
     “How to adaptively allocate cardiac surgery resources in Ontario to shorten the provincial average wait time and reduce wait time disparities in the face of dynamically-changing/non-deterministic patient arrivals?” 
     After receiving users&#39; request and general problem description, the embodiment of present invention automatically and intelligently identifies/infers the objective(s), problem types, issues, sub-questions, contextual information, criteria, requirements (e.g., indicators and measurements), and corresponding decision/control variables and constraints, builds a solution, employs/extends/customizes the identified techniques for decision analysis, and finally returns an adaptive regional resource allocation method, statistical and strategic analysis outputs, decision evaluation and recommendation outputs. 
     In what follows, this embodiment illustration will show the operational process and apparatus of the present invention to (1) analyze the relationships between neighborhood geodemographic factors and cardiac surgery characteristics (e.g., the number of patient arrivals) pertaining to the hospitals/networks, (2) model patient arrival behavior and cardiac surgery service operations in the hospitals, so as to investigate the temporal-spatial patterns of service utilizations and complex emergent behavior (i.e., behavior of a complex healthcare system, such as reneging behavior in hospital selection) of the exemplified cardiac surgery service through simulation, and (3) automatically generate an adaptive method for allocating regional cardiac surgery resources based on simulations. 
     Smart User Interface in the Second Embodiment Illustration 
     The drawing of  FIG. 14  presents the key modules in the second embodiment illustration of the present invention, i.e., the Smart User Interface  103 , the Healthcare Decision Analytics and Support System (HDAMSS) module  104  and the Information Management System (IMS) module  105 , and its interactions (e.g., the intermediate results and user&#39;s feedback on them) with the user (as a Health Workers  100 ) via Smart User Interface  103  and necessary healthcare related data about Existing Hospital Operation  106 , Determinants for Healthcare (e.g., demographic and socioeconomic related Behavior)  109  and Academic/Medical Research Databases  110 . 
     After the user accesses the smart healthcare decision analytics and support apparatus problems via User Accessing  200  of Smart User Interface  103  in any of centralized, distributed and pervasive/mobile manners, Collecting Decision Analytics Problem Description  201  of Smart User Interface  103  will collect the general description of the problem (i.e., how to adaptively allocate cardiac surgery resources in Ontario to shorten the province average wait time and reduce wait time disparities in the face of dynamically-changing/non-deterministic patient arrivals?). At the same time, User Profiling  202  of Smart User Interface  103  extracts and/or infers the contextual information for the user and the analytics problem at hand, such as the user type is a provincial healthcare service administrator, the analytics context is cardiac surgery services in Ontario. The objective(s), problem types, issues, sub-questions, criteria, requirements (e.g., indicators and measurements), and corresponding decision/control variables and constraints for the decision analytics problem will be automatically extracted, inferred, and/or refined from the user&#39;s problem sketch/description and the extracted and/or inferred contextual information. For instance, the objective is to provide an adaptive method for regional healthcare resource allocation in order to shorten the regional average wait time and reduce regional wait time disparities. Sub-questions will involve (1) what and how geodemographic factors affect the cardiac surgery service characteristics (e.g., the number of patient arrivals and wait time), (2) how to model patient service utilization behavior, so as to characterize dynamically-changing/non-deterministic patient arrivals, to investigate the temporal-spatial patterns of cardiac surgery service utilizations, and even to capture the emergent behavior (e.g., reneging behavior in hospital selection) of the exemplified complex healthcare system, (3) how to characterize the operations of cardiac surgery services, and (4) what a mechanism helps to adaptively allocate the cardiac surgery resources with respect to the regional heterogeneity in terms of geodemographic factors and the patient heterogeneity in terms of health service utilization behavior. Examples of the criteria and requirements include the measurement of regional wait time disparities, the temporal-spatial patterns and the dynamically-changing process of regional patient arrivals and wait time for cardiac surgery services. 
     Solution Builder in the Second Embodiment Illustration 
     Upon the input from Smart User Interface  103  on Decision Analytics Problem Definition  111  (e.g., objective(s) and sub-questions), Contextual Information  112  (e.g., users&#39; profiles and analytics context for problems), Criteria and Requirements  113  from Smart User Interface  103 . Solution Builder  210  of HDASS module  104  identifies problem types based on the functions provided by Semantic Analysis  312  and Problem Classification and Matching  313  within Solution Builder  210 . According to the problem sketch from the user and the inferred objective, problem type, issues, sub-questions, contextual information, criteria, requirements (e.g., indicators and measurements), and corresponding decision/control variables and constraints, the problem will be solved by means of integrating statistical analysis, mechanism design, modeling and simulation, and optimization. 
     To build a solution to achieve the analytics objective(s) and to answer the sub-questions, apparatus of Retrieving Existing Solution from Solution Repository  314  and Meta-Knowledge About the Relationship Between Problems and Solutions  315  within Determine Solution  301  automatically infers that (1) techniques of Structural Equation Modeling (SEM)  340  is suitable for modeling and analyzing the complex and hierarchical relationships between geodemographic factors and cardiac surgery service characteristics in that it is efficient in constructing latent variables (i.e., variables that cannot be measured directly), and testing complex relationships among observed and latent variables, as explained in Hair y, Anderson R E, Tatham R L, Black W C. Multivariate Data Analysis: with Readings. 4th edition. Englewood Cliffs, N.J.: Pearson Prentice Hall, 1995, (2) AOC-Based Model  333  is in favor of modeling the cardiac surgery system with respect to patient service utilization behavior, (3) Queueing model  330  and Discrete Event Simulation  331  from Computational Modeling and Simulation Analysis Technique Repository  306  within Strategic Analysis  211  are useful approaches to modeling and simulating operations of ORs existing solutions, and (4) Simulation-Based Optimization is beneficial to generate an adaptive resource allocation method through simulation independently or based on the embodiment of Algorithmic/Mechanism Design  329 . 
     The Solution Builder  210  then automatically and intelligently builds a solution that integrally utilize Structural Equation Modeling  340 , AOC-Based Model  333 , Queueing model  330 , Discrete Event Simulation  331 , Algorithmic/Mechanism Design  328 , and Simulation-Based Optimization  332  to achieve the objective(s) of the user and answer the closely-interrelated sub-questions. Specifically, the autonomy-oriented computing (AOC)-based modeling of the cardiac surgery system with respect to patient service utilization behavior (i.e., arrival behavior) will refer to the results of Structural Equation Modeling (SEM)  340 . The AOC-based cardiac surgery model comprising a specific queueing model for service operations. Both AOC-based multi-agent simulation and discrete event simulation will together support the implement of Simulation-Based Optimization. 
     Accordingly, Acquiring Required Data  302  of Solution Builder  210  determines and accesses necessary data sources for analytics problem aided by the functions of Determining Required Data Sources  319  and Required Data Accessing, Retrieving, Organizing and Preprocessing  320 . The data sources involved in this analytics problem contains Existing Hospital Operation  106  (about the characteristics of cardiac surgery services), Secondary Service Provider  108  (e.g., about the referral for cardiac surgery from family doctors), and Determinants for Healthcare  109  (e.g., the geodemographic profiles for a region). 
     IMS  105  has collected and stored/maintained necessary data for developing, parameterizing, analyzing, modeling, simulating, and evaluating of the adaptive resource allocation problem. MIS Databases  243 , IMS Databases  244  have collected and stored data representing cardiac surgery characteristics (i.e., arrival, capacity, supply and wait time) in Ontario, Canada in the years between 2004 and 2007. The Census Data Sources  237  has stored neighborhood geodemographic data gathered from the 2006 Canadian Census with respect to population size, age profile, and educational profile. In this illustration, 47 major cities/towns in Ontario with populations of more than 40,000 (this population cut-off point was determined such that cities/towns included in the sample represented approximately 90.72% of Ontario&#39;s population) have been selected to derive the geodemographic profiles for 14 LHINs. In addition, Secondary Service Providers&#39; data Sources  236  has collected and stored the driving time from each sampled city/town to the nearest hospital that provides cardiac surgery services to measure service accessibility. In this illustration, the driving times were estimated based on the “Get directions” function in Google Maps. 
     Tables 3 and 4 summarize the geodemographic profiles for the various Local Health Integration Networks (LHINs, i.e., the concerned neighborhood in this illustration) and the service characteristics for each hospital examined. 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 A summary of neighborhood geodemographic profiles for LHINs 
               
               
                 providing cardiac surgery services in Ontario, Canada 
               
            
           
           
               
               
               
               
               
               
            
               
                 LHIN ID 
                 LHIN name 
                 Population 
                 A (%) 
                 SA (%) 
                 E (%) 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 2 
                 South West 
                 762821 
                 32.55 
                 41.05 
                 62.68 
               
               
                 3 
                 Waterloo 
                 671710 
                 29.73 
                 77.69 
                 64.16 
               
               
                   
                 Wellington 
               
               
                 4 
                 Hamilton 
                 796558 
                 33.83 
                 51.54 
                 61.25 
               
               
                   
                 Niagara 
               
               
                   
                 Haldimand 
               
               
                   
                 Brant 
               
               
                 6 
                 Mississauga 
                 912270 
                 27.54 
                 88.20 
                 71.51 
               
               
                   
                 Halton 
               
               
                 7 
                 Toronto Central 
                 3813490 
                 29.97 
                 100.00 
                 70.12 
               
               
                 8 
                 Central 
                 637512 
                 30.06 
                 75.13 
                 69.35 
               
               
                 10 
                 South East 
                 198358 
                 33.90 
                 65.10 
                 66.37 
               
               
                 11 
                 Champlain 
                 651961 
                 32.80 
                 86.40 
                 74.16 
               
               
                 13 
                 North East 
                 189357 
                 37.32 
                 37.3 
                 61.37 
               
               
                   
               
               
                 A: age profile; SA: service accessibility; E: education profile. 
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                 A summary of the secondary data about the 
               
               
                 cardiac surgery characteristics (2004-2007) 
               
            
           
           
               
               
            
               
                   
                 Wait Time 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 LHIN 
                   
                   
                   
                   
                 UMW 
                 SMW 
                 EMW 
                   
               
               
                 ID 
                 Hospital 
                 C 
                 S 
                 A 
                 (d) 
                 (d) 
                 (d) 
                 QL 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 2 
                 London HSC 
                 4 
                 9 
                 111 
                 2 
                 6 
                 21 
                 69 
               
               
                 3 
                 St. Mary&#39;s 
                 3 
                 3 
                 51 
                 3 
                 8 
                 31 
                 58 
               
               
                   
                 General 
               
               
                   
                 Hospital, 
               
               
                   
                 Kitchener 
               
               
                 4 
                 Hamilton HSC 
                 4 
                 8 
                 112 
                 2 
                 7 
                 24 
                 99 
               
               
                 6 
                 Trillium HC, 
                 2 
                 5 
                 86 
                 3 
                 6 
                 18 
                 42 
               
               
                   
                 Mississauga 
               
               
                 7 
                 St. Michael&#39;s 
                 3 
                 6 
                 88 
                 5 
                 6 
                 18 
                 66 
               
               
                   
                 Hospital, 
               
               
                   
                 Toronto 
               
               
                 7 
                 Sunnybrook 
                 3 
                 10 
                 71 
                 3 
                 5 
                 16 
                 31 
               
               
                   
                 Health 
               
               
                   
                 Sciences 
               
               
                   
                 Centre 
               
               
                 7 
                 University 
                 5 
                 12 
                 143 
                 2 
                 7 
                 23 
                 165 
               
               
                   
                 Health 
               
               
                   
                 Network, 
               
               
                   
                 Toronto 
               
               
                 8 
                 Southlake 
                 2 
                 4 
                 64 
                 4 
                 7 
                 25 
                 57 
               
               
                   
                 Regional HC, 
               
               
                   
                 Newmarket 
               
               
                 10 
                 Kingston 
                 2 
                 3 
                 53 
                 4 
                 6 
                 21 
                 36 
               
               
                   
                 General 
               
               
                   
                 Hospital 
               
               
                 11 
                 University of 
                 4 
                 14 
                 91 
                 2 
                 9 
                 29 
                 79 
               
               
                   
                 Ottawa Heart 
               
               
                   
                 Institute 
               
               
                 13 
                 Hopital 
                 2 
                 5 
                 38 
                 3 
                 4 
                 19 
                 27 
               
               
                   
                 Regional 
               
               
                   
                 de Sudbury 
               
               
                   
               
               
                 C: service capacity; 
               
               
                 S: service supply; 
               
               
                 A: arrival; 
               
               
                 UMW: median wait time for urgent patients; 
               
               
                 SME: median wait time for semi-urgent patients; 
               
               
                 EMW: median wait time for elective patients; 
               
               
                 QL: queue length; 
               
               
                 d: day. 
               
            
           
         
       
     
     Aided by the functions of Initializing and Parameterizing Techniques in Solution  321  of Configuring Solution  303  within Analytics Engine  207 , this embodiment illustration utilizes the data for investigating the relationships between geodemographic factors and cardiac surgery service characteristics, to initialize the parameter settings of AOC-based cardiac surgery system model, the embodiment of queueing model, discrete event simulation, and Simulation-Based Optimization. 
     Data-Driven Statistical Analysis in the Second 
     Embodiment Illustration 
     As the determined solution, this embodiment illustration first automatically (1) builds hypotheses based on previous studies stored/maintained in Centralized/Distributed/Pervasive Academic/Medical Research Databases  257 , in which data is gathered from Academic/Medical Research Databases  110  (e.g., Medline, PubMed), and (2) utilizes the structural equation modeling (SEM) method to capture the relationships between geodemographic factors and patient arrivals for cardiac surgery services based on the data queried from Centralized/Distributed/Pervasive Hospital Information System (HIS) Databases  243 , Centralized/Distributed/Pervasive Management Information System (MIS) Databases  244 , and Centralized/Distributed/Pervasive Secondary Service Providers&#39; Data Sources. 
     An embodiment of Structural Equation Modeling  400  comprising all the hypotheses that are logically inferred and derived, as illustrated in the drawing of  FIG. 15 . For instance, previous studies such as Alguwaihes A, Shah B R. Educational attainment is associated with health care utilization and self-care behavior by individuals with diabetes. The Open Diabetes Journal 2009, 2:24-28 have suggested, certain geodemographic factors may moderate (i.e., change the direction and/or strength of) the effects that other geodemographic factors have on healthcare service characteristics. If one area has more healthcare service providers (e.g., hospitals providing cardiac surgery services), the burden of population growth and aging on the patient arrivals for a specific hospital in that area may be alleviated, as patients residing there have more choices and thus will be more likely to be distributed among multiple hospitals. This suggests that the geographic accessibility to services (referred to hereafter as service accessibility) may have potential moderating effects on the relationships between population size/age profile and arrival besides its direct effect on arrival. As an additional example, individuals (including seniors) with different educational backgrounds may have varying lifestyles that can influence their risk for cardiovascular disease and their healthcare service utilization behavior. This indicates that educational profile may have a potential moderating effect on the relationship between population size and patient arrival besides its direct effect on arrival. As in a summary, the automatically inferred research hypotheses based on previous studies stored in Centralized/Distributed/Pervasive Academic/Medical Research Databases  257  are as follows: 
     Hypothesis 1 (H1): 
     Population size (representing the total population in a neighborhood) has a direct positive effect on arrival (i.e., the number of patients registered in hospitals for a particular healthcare service). 
     Hypothesis 2 (H2): 
     Age profile (conceptualized as the proportion of people older than 50 in a neighborhood) has a direct positive effect on arrival. 
     Hypothesis 3.1 (H3.1): 
     Service accessibility (defined by the proportion of the population residing within a 30-minute driving time to the nearest hospitals providing cardiac surgery services in an area to represent the geographic accessibility to healthcare services) has a direct negative effect on arrival. 
     Hypothesis 3.2 (H3.2): 
     Service accessibility has a negative moderating effect on the relationship between population size and arrival. 
     Hypothesis 3.3 (H3.3): 
     Service accessibility has a negative moderating effect on the relationship between age profile and arrival. 
     Hypothesis 4.1 (H4.1): 
     Educational profile (defined as the proportion of the population with above high school education in a neighborhood) has a direct negative effect on arrival. 
     Hypothesis 4.2 (H4.2): 
     Educational profile has a negative moderating effect on the relationship between population size and arrival. 
     Hypothesis 4.3 (H4.3): 
     Educational profile has a negative moderating effect on the relationship between age profile and arrival. 
     Hypothesis 5.1 (H5.1): 
     Arrival has a direct positive effect on capacity (representing physical resources, e.g., operating rooms for cardiac surgery). 
     Hypothesis 5.2 (H5.2): 
     Arrival has a direct positive effect on supply (representing human resources, e.g., physicians for cardiac surgery). 
     Hypothesis 5.3 (H5.3): 
     Arrival has a direct positive effect on wait time (an indicator for timely access to healthcare service). 
     Hypothesis 5.4 (H5.4): 
     Capacity has a direct negative effect on wait time. 
     Hypothesis 5.5 (H5.5): 
     Supply has a direct negative effect on wait time. 
     Strategic Analysis in the Second Embodiment Illustration 
     According to the determined solution, the embodiment illustration automatically and intelligently models the cardiac surgery system considering patient arrival behavior based on the findings of SEM test and the technique of AOC-based modeling, so as to identify and evaluate the dynamics of patient arrivals and wait time, and capture the complex emergent behavior of the healthcare system. The embodiment of the AOC-based model of a cardiac surgery system as shown in the drawing of  FIG. 17 . In the AOC-Based Cardiac Surgery System Model  401 , the behavior of three types of autonomous behavior-based entities, i.e., patient, general practitioner (GP, i.e., family doctor) and hospital, their behavioral interactions as well as the environment actively carrying out information exchanges are automatically and computationally modeled. 
     As suggested by the preceding SEM-based Statistical Analysis Output  221  and prior literatures such as Harindra C Wijeysundera, Therese A Stukel, Alice Chong, Madhu K Natarajan, David A Alter. Impact of clinical urgency, physician supply and procedural capacity on regional variations in wait times for coronary angiography. BMC Health Services Research 2010, 10:5 doi:10.1186/1472-6963-10-5 and Cardiac Care Network of Ontario. Cardiac Care Network of Ontario Patient, Physician and Ontario Household Survey Reports: Executive Summaries. 2005 http://www.ccn.on.ca/ccn public/UploadFiles/files/CCN_Survey_Exec_Sum — 200508.pdf, the major factors should be considered in modeling autonomous patients&#39;/GPs&#39; hospital selection behavior include the quantities of healthcare physical (e.g., the number of operating rooms) and human resources (e.g., the number of physicians), the geographic distance from home to hospitals and the waiting time for receiving the request healthcare services. As in the actual cardiac surgery system, patients almost follow GPs&#39; referral suggestions. Therefore, this embodiment illustration sets that autonomous patient entities always select the hospital that their GPs recommend. 
     The autonomous hospital selection decision behavior of GP is automatically and computationally modeled based on the following decision process. When GP entities choose a hospital, they will first calculate the utility (representing the degree of satisfaction on a hospital in terms of travel distance, service quality assurance and wait time for receiving services) for each hospital based on released information and their experience on historical referrals in terms of wait time. The hospital that has the highest expected utility will be recommended. 
     The autonomous behavior of hospital entities is automatically and computationally modeled based on queueing processes. As the embodiment of Queueing Model  330  in this embodiment illustration, a general Multi-Priority, Multi-Server, Non-Preemptive Queueing Model  402  for a hospital is presented in the drawing of  FIG. 18 . Specifically, each hospital has three types of autonomous patient entities, urgent, semi-urgent and elective. The urgent patient entities have the highest treatment priority, while the elective patient entities have the lowest treatment priority. The arrival rate for each patient type follows a Poisson distribution. 
     The simulation environment shared by the autonomous entities and carrying out information is computationally modeled as a bipartite city-hospital network as shown in the drawing of  FIG. 19 . In this embodiment illustration, each node c i  (c i εC) represents a city/town which has more than 40,000 population in 2006 according to the census data in Ontario, in accordance with the city sampling cutoff point determined in the preceding embodiment of SEM analysis. Each node h j  (h j εH) denotes a hospital providing cardiac surgery services. And, each weighted edge d ij  (d ij εD) represents the driving time from a city/town c i (c i εC) to a hospital h j  (h j εH). Autonomous patient entities move to hospital nodes from city nodes. The timely information about hospitals&#39; characteristics (including the quantities of operating rooms and physicians) as well as the wait time announced will serve as the reference for the patient and GP entities when they make their hospital selection decisions. 
     Based on the afore-described AOC-based cardiac surgery system model, discrete-event simulations are carried out to validate the model, and to examine the temporal-spatial service utilization patterns, the dynamics of patient arrivals and healthcare service performance in terms of throughput, wait time and queue length, and the emergent behavior of the complex healthcare system in different scenarios. In addition, adaptive methods/strategies for healthcare resource allocation are automatically generated, evaluated, and recommended by means of AOC-based (i.e., AOC-by-self-discovery) modeling and simulation. 
     System Outputs in the Second Embodiment Illustration 
     This embodiment illustration provides decision analytics and support in the forms of textual and/or graphical Comprehensive Report  214 , Decision Recommendation Output  215 , Decision Scenario Analysis Output  216 , Decision Prediction Output  217 , Decision Evaluation Output  218 , Simulation Visualization Output  219 , and Statistical Analysis Output  221 . 
     In particular, the generated SEM testing results and suggestions on healthcare resource allocation are formatted and reported by the embodiments of Statistical Analysis Output  221  and Decision Recommendation Output  215  in the module of System Output  208  within HDASS  104 . In Statistical Analysis Output  221 , the generated SEM testing results show that population size and age profile have direct positive effects on arrival (β=0.737, p&lt;0.01; and β=0.284, p&lt;0.01, respectively), whereas service accessibility negatively affects arrival (β=−0.210, p&lt;0.01). Service accessibility decreases the effect of population size on arrival (β=−0.606, p&lt;0.01), and educational profile weakens the effects of population size and age profile on arrival (β=−0.595, p&lt;0.01; β=−0.286, p&lt;0.01, respectively). In Decision Recommendation Output  215 , the generated findings of the SEM testing results suggest that: (i) regional wait time disparities in cardiac surgery services are associated with differences in geodemographic profiles such as service accessibility and education; (ii) the allocation of resources for a particular healthcare service in one area should consider the geographic distribution of the same service in neighboring areas; and (iii) an increase in physician resources and the more efficient use of existing surgical facilities may contribute to a reduction in cardiac surgery wait time. 
     Built on the above results, the simulation results of the AOC-based cardiac surgery system modeling and the following strategic analysis on adaptive healthcare resource allocation are generated, formatted, and reported in the forms of textual and/or graphical Comprehensive Report  214 , Decision Recommendation Output  215 , Decision Scenario Analysis Output  216 , Decision Prediction Output  217 , Decision Evaluation Output  218 , and Simulation Visualization Output  219 . Specially, after parameterized by the actually geodemographic and hospital characteristics data, the AOC-based cardiac surgery system model is validated by autonomous behavior-based simulations. At the same time, the temporal-spatial hospital service utilization patterns and the dynamics of patient arrivals and hospital performance are generated and observed. Then, based on the validated AOC-based cardiac surgery system model, simulations run in different scenarios (e.g., sharply increase of urgent cardiac surgery patients because of cold weather, or hospitals providing more accurate and timely wait time information to represent their performance for patients) and generate and report the corresponding results and findings by Decision Scenario Analysis Output  216  and Decision Prediction Output  217 . In such simulations, interesting complex emergent behavior (e.g., patient reneging patterns represented by number of patients who left the nearest hospitals or before being transferred by their GPs) of the cardiac surgery system is generated and captured. Similarly, the effectiveness of adaptive resource allocation methods/strategies is evaluated by means of autonomous behavior-based simulations and reported by Decision Evaluation Output  218 . By utilizing and/or extending the functions of 2D or 3D geographical information systems such as Google earth, this embodiment illustration employs Simulation Visualization Output  219  to visualize the dynamics of patient arrivals and healthcare performance such as throughput, wait time and queue length, spatial-temporal service utilization patterns, as well as the emergent behavior of the complex healthcare system for all the above-mentioned simulations. 
     INDUSTRIAL APPLICABILITY 
     The present invention relates to methods and an apparatus for developing, analyzing, investigating, supporting and advising healthcare and well-being related decisions. In particular, the present invention relates to the architecture of systems in either stand-alone or distributed/collaborative/pervasive settings, the components of the systems and their underlying processes and couplings, the computational techniques built into the methods, input data sources integrated into and output results produced and distributed by the systems, as well as the apparatus for carrying out the corresponding user interaction, data access and collection, data integration and processing, data-driven inferences and simulation, intelligent computations, decision analytics, and decision support to generating solutions to various healthcare analytics and decision-making problems. This invention also relates to two working illustrations of the methods and apparatus that present the embodiment illustrations of the present invention. One embodiment illustration is related to generating adaptive operating room (OR) time block allocation solutions for a medical services-providing institution. The generated outputs can readily be used to help ORs maintain a stable performance in the face of dynamically-changing and non-deterministic patient arrivals (e.g., due to geodemographic, environmental/climate, and socioeconomic variations). Here, non-deterministic indicates that the quantity in question may be predicted by various statistical and mathematical techniques although particular outcomes may not happen with complete certainty. Another embodiment illustration is on performing decision analytics tasks and adaptive decision support in regional healthcare resource allocation that has the advantages of reducing healthcare performance disparities and/or the optimization of resource usage and performance. 
     If desired, the different functions discussed herein may be performed in a different order and/or concurrently with each other. Furthermore, if desired, one or more of the above-described functions may be optional or may be combined. 
     The embodiments disclosed herein may be implemented using general-purpose or specialized computing platforms, computing devices, computer processors, or electronic circuitries including but not limited to digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), and other relevant programmable logic devices configured or programmed according to the teachings of the present disclosure. Computer instructions or software codes running in the general-purpose or specialized computing platforms, computing devices, computer processors, or programmable logic devices can readily be prepared by practitioners skilled in the software or electronic art based on the teachings of the present disclosure. 
     In some embodiments, the present invention includes computer storage media having computer instructions or software codes stored therein which can be used to program computers or microprocessors to perform any of the processes of the present invention. The storage media can include, but are not limited to, floppy disks, optical discs, Blu-ray Disc, DVD, CD-ROMs, and magneto-optical disks, ROMs, RAMs, flash memory devices, or any type of media or devices suitable for storing instructions, codes, and/or data. 
     While the foregoing invention has been described with respect to various embodiments and illustrative working examples, it is understood that other embodiments are within the scope of the present invention as expressed in the following claims and their equivalents. Moreover, the above specific examples are to be construed as merely illustrative, and not limitative of the remainder of the disclosure in any way whatsoever. Without further elaboration, it is believed that one skilled in the art can, based on the description herein, utilize the present invention to its fullest extent. All publications recited herein are hereby incorporated by reference in their entirety.