Intelligent Healthcare flow Mechanism based on Analytics & Digital Transformation

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Intelligent Healthcareflow Mechanism based on Analytics & Digital Transformation Sumit Chakraborty BEE (Jadavpur University), Fellow (IIM Calcutta), India. E-mail: [email protected], Contact: 91-9940433441. Paper reference : IHMADT, version 1; Date : 15.08.2008. Abstract : This chapter presents a healhcareflow mechanism; the intelligence of the mechanism is explored from the perspectives of knowledge management through case based reasoning, decision making using analytics, workflow control subject to time and resource constraints, verification of fairness, correctness and privacy of patient data, multi-mode payment function, cooperative communication protocol for collaborative information seeking, enterprise application integration and life-science supply chain coordination. The basic objective is to improve the quality of healthcare service at fair reasonable cost by integrating different enterprise applications through systematic coordination of material, information and financial flows. This work also suggests the information system schema required for the mechanism in terms of computational intelligence, communication, data and application schema. Keywords: Intelligent mechanism, Case based reasoning, Analytics, Workflow control, ERP, Cooperative communication, Collaborative information seeking, Healthcare.

1. Introduction The rapid expansion of global market, the explosive growth of information and communication technologies, aggressive competition and the changing economic and social conditions have triggered tremendous opportunities to provide healthcare service electronically. E-health is a significant development of the use of emerging information and communication technologies in healthcare. E-health while promising also presents new business challenges in terms of acceptable standards, choice of technologies, overcoming traditional jurisdictional boundaries, upfront investment, privacy and confidentiality of critical data [8,9,12,15]. New and evolving information and communication technologies are being adopted by healthcare service providers worldwide. It is essential for an efficient healthcare information system to integrate different enterprise applications such as analytics, ERP, SCM, KMS, extranet, intranet and internet for proper information flow, rational decision making and fast and correct transaction processing [10,11,13,14]. E-health requires an efficient and intelligent mechanism. This work presents an intelligent mechanism for high quality healthcare service from the perspectives of a set of agents, inputs, outputs, strategic moves, protocol, revelation principle, payment function and information system schema. An efficient mechanism provides different benefits such as improved customer service, accuracy, ease of processing, increased productivity, quick access to information, greater geographical reach, better coordination, reduced transaction costs, rational decision making and efficient knowledge management. But, it has several constraints and challenges like high cost of computation and communication, information flow, privacy of data, coordination, economic modeling, pricing strategy, payment, fairness and correctness of service transactions and behavior of the service consumer and provider [4,5,21,22]. Research Methodoogy : The present work has reviewed relevant literature and analyzed a few explorative case studies on healthcare management, healthcare information systems, case based reasoning and cooperative communication schema based on web service and videoconferencing. The case study approach has been selected to support analytical rather than statistical generalization. Case studies capture reality in considerably greater detail and allow for the analysis of the problems of an adaptive enterprise. The business processes of a complex multi-tier healthcare supply chain in USA has been analyzed and a group of experienced healthcare system administrators have been interviewed. This work explores the basic requirements of healthcare mechanism from different perspectives such as rational analytical decision making, effective knowledge management, complex workflow control and improved coordination.

The present work is organized as follows. Section 1 defines the problem of today’s healthcare service, discusses related works on e-healthcare, the research methodology adopted for this work and the contributions. Section 2 presents the healthcare chain and healthcareflow mechanism for this model. Section 3 suggests the information system schema for the mechanism. Sections 3.1, 3.2, 3.3. and 3.4 highlight the issues of computational intelligence, communication, data and application schema. Section 4 analyzes the healthcareflow mechanism and section 5 concludes the work.

2. Healthcareflow Mechanism

Figure 1 : Healthcare Business Model

Figure 1 shows the complex multi-tier architecture of a healthcare service model: patients (tier 1); branded and nonbranded hospitals, medical surgery centers and physicians (tier 2): healthcare service providers (HCSP) having service centers and distribution centers (tier 3); distributors, wholesalers and retailers (tier 4); suppliers, drug manufacturers and medical device manufacturers (tier 5) and carriers ([tier 6). Information and funds flow from tier1 - tier2 - tier3 - tier4 - tier5 - tier6 through the information system and healthcare products and services flow in the reverse direction. The information is related to healthcare products, services, providers and consumers. Healthcare supply chain is a network of organizations that satisfies the demand of the service consumers for healthcare products and services. The basic objective is to improve the quality of service in patient care by integrating different business units through systematic coordination of material, information and financial flows. The following section outlines an intelligent mechanism for the above healthcare service model. Healthcareflow Mechanism (HM) : Agents: Service consumer or patient (C), Service providers (P): Workflow (P w), Healthcare (Ph), Testing (Pt), Financial service (Pf) and Supply chain (Ps); Input : Data of C, P, healthcare products, services and pricing plan; Strategic move: (a) Knowledge management through case based reasoning, (b) Decision making using analytics, (c) Workflow control subject to time and resource constraints, (d) Verification of fairness, correctness and privacy of data, (e) Multi-mode payment function, (f) Cooperative communication protocol, (g) Enterprise application integration and (h) Life-science supply chain coordination. Protocol: 1. Registration : Pw call workflow management system → register C through a service contract on fair healthcare and privacy policy; make a meeting plan for consultation. 2. Consulting : Ph call case based reasoning system → do health check-up and diagnosis; recommend medication and testing. It may be a single or multiple iterations depending on the complexity of the case.

3. Testing (optional) : Pw make a testing plan for C and sends it to Pt; Pt do testing; send test data to Ph; Ph call analytics → make decision and recommendations. 4. Sergical operations (optional) : Pw make operation plan; Ph perform operation on C. 5. Supply chain coordination: Ps call ERP-SCM system → do demand and distribution planning by CPFR, inventory control, sourcing, order management, warehousing and shipping of medicines and medicare products. 6. Receivables management : Pf call ERP system → generate invoice; process payment for C in single or batch mode. 7. Dispute resolution : C verifies fairness and correctness of all transactions. C and P negotiate and settle any medical, financial or privacy disputes mutually or with the intervention of a trusted third party. 8. Exit : Pw issues discharge certificate to C. C may exit from the system at any stage by submitting a bond to Pw. Revelation principle: (a) Privacy preserving data mining through cryptographic and secure multi-party computation protocols; (b) Secure data warehousing. Payment function: (a) Discriminatory pricing, (b) Free health check-up, (c) Health insurance, (d) Corporate mediclaim policy, (e) Credit card, (f) Direct cash payment and (g) Penalty. Information system schema : Computational intelligence : (a) Workflow control: Time scheduling and resource allocation; (b) Case based reasoning: case retrieval and adaptation mechanism; (c) Analytics: data warehousing, data mining, data visualization and performance scorecard; (d) Transaction processing for registration, testing, payment and discharge; Communication schema : Web, mobile communication and videoconferencing system for virtual patient care and telemedicine; Data schema : RDBMS, Data warehousing system; Application schema : Web enabled ERP, Business intelligence system, Knowledge management system. Output: Healthcareflow plans, Transaction documents, BI reports. In the above mechanism, the cost of communication depends on the interactions between the service consumer and service provider; the number of negotiation rounds and the frequency of information sharing among the trading agents associated with the supply chain. It depends on the complexity of critical patient care and workflow control subject to time and resource constraints. It also depends on cooperative communication among the healthcare specialists for collaborative information seeking and knowledge management through videoconferencing and wireless or wired networks. The cost of computation depends on the complexity of various algorithms associated with workflow control for time scheduling and resource allocation; case based reasoning; analytics for data warehousing, data mining, data visualization and performance scorecard and transaction processing for registration, testing, payment, discharge and supply chain management. It also depends on the complexity of encryption and decryption algorithm and signcryption to preserve the privacy of data. The cost of signcryption is relatively less than the cost of signature-then-encryption approach. The next section analyzes various components of the mechanism in details and suggests an information system schema. This analysis tries to identify the intelligence of the mechanism from the perspectives of a set of strategic moves, computational complexity, communication, data and application integration.

3. Mechanism’s Intelligence Theorem 1 : An optimal mix of strategic moves provides adequate intelligence to the mechanism (HM) for improved quality of service. The mechanism requires a fundamental rethinking and radical redesign of healthcare practice and infrastructure in terms of technology management, organization structure, operations, marketing, financial and human resources management. The basic objective of the service provider is to improve the quality of healthcare service at fair cost by adopting a set of intelligent rational strategic moves such as case based reasoning, decision making using analytics, workflow control subject to time and resource

constraints, verification of fairness, correctness and privacy of data, multi-mode payment processing system, cooperative communication protocol for collaborative information seeking, enterprise application integration and life-science supply chain coordination. The healthcare service agents should use intelligent information and communication technology schema for workflow control, transaction processing, complex decision making, knowledge management and improved supply chain coordination. The next theorems analyze these strategic moves in details. Additionally, the healthcare specialists should be able to utilize the intelligence of modern biomedical engineering (e.g. ventilation), bio-inspired artificial intelligence (e.g. robotics in surgical operations), advanced testing system (e.g. image processing, CT scanner, x-ray, homecare kits) and the innovations in life science industry (e.g. drug discovery) for critical patient care. But, these issues of medical science are out of the scope of the present work. Theorem 2 : The mechanism (HM) adopts discriminatory pricing strategy, multi-mode payment options, efficient ERP and application integration for fairness and correctness in revenue management and cost control. The healthcare service provider formulates a discriminatory pricing strategy for different types of service offerings to ensure good quality of service at reasonable cost. The pricing strategy requires competitive intelligence to ensure a sustainable business model. The mechanism must ensure fairness and correctness of computation for the service consumer in testing and financial transactions processing through the use of intelligent enterprise applications and honest and transparent work culture. A service provider can optimize profit and revenue through malicious business practice and incorrect computation. The mechanism requires the support of efficient regulatory compliance policy and dispute resolution protocol to resist malpractice and errors. Theorem 4 discusses these issues in details. The payment processing system offers multiple options such as health insurance, corporate mediclaim policy, credit card and direct cash payment. Such a flexible system requires effective enterprise application integration among multiple organizations like healthcare service provider, vendors, insurance companies and banks through an web enabled ERP system. Theorem 3 : The revelation principle of the mechanism (HM) ensures security and privacy of strategic

data through secure data warehousing, privacy preserving data mining and basic cryptographic tools like encryption, digital signature and signcryption. The privacy of healthcare information is protected by various regulations that apply to healthcare plans and electronic healthcare information in financial and administrative transactions. The healthcare organizations are known as covered entities in the regulation [8]. The regulation protects healthcare information only if it is identifiable and created or received by a covered entity. The healthcare service provider, health insurance provider and other healthcare professionals jointly maintain the privacy of medical data of the service consumers or patients. The mechanism requires a trusted computing environment. The primary objective of e-health is to increase the flow of healthcare products and information so that the patients can get right treatment in time. The participation of healthcare professionals and healthcare institutes in patient care should be nonrepudiable. A good e-patient care system is essential for global healthcare outsourcing business model where no direct patient interaction is involved. For example, there is shortage of medical experts for healthcare firm 1 at location A; the firm 1 uses an outsourcing business model. There are skilled medical experts in another healthcare firm 2 at location B. The files of x-rays, CT scans, MRI and other test reports of the patients of firm 1 are uploaded at the e-patient care system. The medical experts of firm 2 study the uploaded reports and perform the diagnosis. The critical success factors of this practice are strict regulatory compliance, liability, privacy and high quality medical practice, good outsourcing infrastructure, low cost base and proper utilization of time zone difference. E-patient care is particularly useful for rural healthcare at remote locations [23,24]. In this patient care model, signcryption ensures confidentiality, message integrity and non-repudiation of transmitted data. A realtime service oriented architecture can support critical patient care locally and remotely through secure transmission of medical data stream [25]. Here, data management is a critical issue since the patients are

attached with electronic sensors and life-support devices and these instruments transmit real-time data to the service provider through authenticated communication channel. Access control, security, privacy and trust are prominent issues of advanced patient care while the patient is located at home or another medical facility. The mechanism should ensure confidentiality, integrity and availability of data in real-time information exchange among various tiers of healthcare supply chain. It is required to protect the confidential information in storage and transmission. The information created and stored by the healthcare service provider needs to be available to authorized entities in a timely manner. The confidentiality of data may be affected by snooping i.e. unauthorized access or interception of data. The integrity of data can be threatened by modification, masquerading, replaying and repudiation. Another critical issue is denial of service which can threaten availability of data in time. The mechanism should protect the healthcare information system from all these malicious attacks for better business continuity and improved customer service. Privacy is required to maintain the competitiveness and reputation in e-healthcare and to avoid the bias of trading agents. But, absolute privacy may result loss in e-transactions due to increased transaction costs, lack of reputation, loss of coordination and relationship. Similarly, absolute anonymity may cause serious flaws in regulatory compliance of the healthcare business. The mechanism must address correct identification, authentication, authorization, privacy and audit for each e-transaction [2,16]. For any secure service, the system should ask the identity and authentication of one or more agents involved in a communication. The agents of the same trust zone may skip authentication but it is essential for all sensitive communication across different trust boundaries. After the identification and authentication, a service should address the issue of authorization. The system should be configured in such a way that an unauthorized agent cannot perform any task out of his scope. The system should ask the credentials of the requester; validate the credentials and authorize the user to perform a specific task. Each trading agent should be assigned an explicit set of access rights according to the assigned role. Privacy is another important issue. A trading agent can view only the information according to his authorized access rights. Finally, the system should audit each transaction, what has happened after the execution of a specific service transaction. Secure communication is a critical issue of service oriented computing model. The basic objective is to provide confidentiality, data integrity, authentication and non-repudiation in the communication of sensitive data. Cryptography ensures privacy and secrecy of sensitive data through encryption, digital signature and signcryption. The sender (S) encrypts a message (m) with encryption key and sends the cipher text (c) to the receiver (R). R transforms c into m by decryption using secret decryption key. An adversary may get c but cannot derive any information. R should be able to check whether m is modified during transmission. R should be able to verify the origin of m. S should not be able to deny the communication of m. There are two types of key based algorithms: symmetric and public key [12]. Symmetric key encryption scheme provides secure communication for a pair of communication partners; the sender and the receiver agree on a key k which should be kept secret. In most cases, the encryption and decryption keys are same. In case of asymmetric or public-key algorithms, the key used for encryption [public key] is different from the key used for decryption i.e. private key. The decryption key cannot be calculated from the encryption key at least in any reasonable amount of time. A digital signature is a cryptographic primitive by which a sender (S) can electronically sign a message and the receiver (R) can verify the signature electronically. S informs his public key to R and owns a private key. S signs a message with his private key. R uses the public key of S to prove that the message is signed by S. The digital signature can verify the authenticity of S as the sender of the message. A digital signature needs a public key system. A cryptosystem uses the private and public key of R. But, a digital signature uses the private and public key of S. A digital signature scheme consists of various attributes such as a plaintext message space, a signature space, a signing key space, an efficient key generation algorithm, an efficient signing algorithm and an efficient verification algorithm [6]. There are various forms of digital signature such as group signature and ring signature. A group signature scheme allows a member of a group to sign a message anonymously on behalf of the group. A designated entity can reveal the identity of the signer in case of any dispute. Traditional signature-then-encryption is a two step approach. At the sending end, the sender signs the message using a digital signature and then encrypts the message. The receiver decrypts the ciphertext

and verifies the signature. The cost for delivering a message is the sum of the cost of digital signature and the cost of encryption. Signcryption is a public key primitive that fulfills the functions of digital signature and public key encryption in a logically single step and the cost of delivering a signcrypted message is significantly less than the cost of signature-then-encryption approach [18]. The service oriented computing model is vulnerable to insecure communication. An application may fail to encrypt network traffic for sensitive communications. The basic objective of the proposed secure service oriented computing model is that the application properly signcrypts all sensitive data. A pair of polynomial time algorithms (S,U) are involved in signcryption scheme where S is called signcryption algorithm and U is unsigncryption algorithm [17]. The algorithm S signcrypts a message m and outputs a signcrypted text c. The algorithm U unsigncrypts c and recovers the message unambiguously. (S,U) fulfill simultaneously the properties of a secure encryption scheme and a digital signature scheme - confidentiality, unforgeability and nonrepudiation. Theorem 4 : The mechanism allows the service consumer and healthcare service provider to verify the fairness and correctness of transactions through an efficient dispute resolution protocol. Dispute resolution is a critical issue of patient care, strategic sourcing, accounts payable and receivables management and contracts negotiation in healthcareflow mechanism. If the trading agents violate the regulatory compliance of the mechanism, a trusted entity should resolve the issues of dispute and ensure the credibility, reliability and robustness of the mechanism. The service consumer verifies the fairness and correctness of invoice computation before clearing the payment to the service provider. The disputes may occur among multiple tiers of the healthcare supply chain such as between tier 1 and tier 2 or tier 2 and tier 3 or tier 3 and 4/5/6 (Figure 1). In case of accounts payable management, the receipt of an invoice from a vendor triggers the invoice verification process. The procedure involves a three way match between the purchase order; the goods receipt note and the invoice. If the quantity and price of the three documents match, the payment will be paid to the vendor as per payment terms. If there is any mismatch, the invoice will be blocked. The account payable associate investigates the disputes; takes the necessary corrective action and unblocks the invoices. If the vendor does not accept the recommendations of accounts payable management system, a third party trustee will review the fraud case and will verify the contract between the healthcare service provider and the vendor. The rating of the vendor may be reevaluated on the basis of such disputes and the vendor may be blacklisted for any malicious business practice.

Figure 2. Dispute Resolution Protocol In figure 2, Alice sends a contractual message m to Bob. Bob can verify that the message is sent by Alice since the public key of Alice is used for verification. The public key of Alice can not verify the signature signed by the private key of any other malicious agent. The scheme also preserves the integrity of the

message. If a message is altered during communication, the receiver can detect this change. Another important issue is nonrepudiation. If Alice signs a message and then denies it, Bob can prove that Alice signs the message. A trusted center (TC) resolves any dispute. Alice creates a signature from her message and sends the message, her identity, Bob’s identity and the signature to TC. TC verifies the source of the message using Alice’s public key. TC saves a copy of the message with the identity of Alice, the identity of Bob and a timestamp. TC generates a new signature from the message using its private key and sends the message, the new signature, Alice’s identity and Bob’s identity to Bob. Bob verifies the message using the public key of TC and his private key. In future, if Alice denies that she is the sender of the message, TC can show a copy of the saved message. Alice will lose the dispute if the message received by Bob is the duplicate of the message saved by TC.

3. Information System Schema An efficient healthcare information system integrates various enterprise applications while maintaining individual autonomy and self-governance [4]. The system should support confidentiality, message integrity, non-repudiation, auditing and availability of service in time. The system should support sharing of data in a collaborative business environment wherein a group of trading agents can exchange strategic business information maintaining the privacy of critical data [5,6]. Increased organizational agility is required for the cooperation of adaptive enterprises. Information technology can improve the quality of service and reduce cost in healthcare services [7]. The demand for critical patient care is growing. But, many small rural healthcare centers are facing problems to develop and maintain a costly IT infrastructure [24]. This forces those healthcare centers to search for innovative IT platform. E-health is a promising IT platform of healthcare services [8,9,10,11,12,13,14,15]. The concept of e-health is promising; still there are lots of challenges. Many healthcare service providers are reluctant to adopt or migrate to web enabled systems due to various reasons. One of the major threats is security and privacy of data [15]. Threat of Electronic Data Interchange [EDI] is another critical issue. EDI is the electronic exchange of business information in a standard format among the trading agents. Many healthcare service providers are still managing their business operations using EDI and FTP protocols. They believe that EDI is a reliable robust system which can ensure the security and privacy of data. But, EDI has several limitations. The major limitation is the investment in the initial set-up and the high cost of implementation, customization and training [20]. In healthcare business, the supply chain network of a healthcare service provider grows periodically. New trading partners get added to the existing supply chain architecture which become complex gradually. The cost of communication and adoption of EDI technology is relatively high as compared to web enabled system.

3.1 Computational Intelligence Theorem 4 : The mechanism (HM) uses the intelligence of analytics and case based reasoning for effective knowledge management and rational complex decision making. It has always been true that a significant part of an organization’s knowledge resides in its memory. While healthcare organizations create knowledge and learn, they also forget. Organizations are beginning to recognize that they can suffer a failure of collective corporate memory. The storage, organization and retrieval of relevant knowledge is known as organizational memory [38]. In healthcare management, this knowledge can be classified as declarative, procedural, causal, conditional and relational - what drug is appropriate for an illness; how the drug works, why the drug works, when the drug works and how a drug interacts with other drugs. An organizational memory system enables the integration of dispersed and unstructured knowledge by enhancing its access, dissemination and reuse among the authorized entities. Organizational memory is a comprehensive information system that captures accumulated knowhow, business activities, core competencies and other assets and makes them available to enhance the efficiency of knowledge intensive business processes in healthcare management. Knowledge from the past cases, experience and events can influence present activities and solution methodologies. In this

work, case based reasoning is selected as the mechanism of healthcare recommender system. It standardizes a set of processes, methods and best practices and saves time and cost in knowledge management. But, it may cause decision making bias and may affect innovation and creativity.

Case based reasoning (CBR) is a methodology for solving problems by utilizing previous experience [29]. It involves retaining a memory of previous healthcare problems and their solutions and solving new problems by referencing the past cases. A healthcare expert presents a new query case to the recommender system. The recommender system searches its memory of past cases stored in case base and attempts to find a case that has the same problem specification of the current case. If the system does not find an identical case in its case base, it will attempt to find the case or cases that match most closely to the current query case. There are two different types of search such as similarity search and neighborhood search [30]. In case of similarity search, the solution of the retrieved case is directly used for the current problem. The system adapts the retrieved cases if the retrieved case is not identical to the current case. In a complex search, the system requires the access of multiple case bases which are located at various locations. This collaborative information seeking requires a web service enabled platform for complex search. Agents : Healthcare consultant (Ph); Input: New case or query (q) regarding a patient (C); Output: Recommended solution; Retrieve the most similar cases (c1,…,ck) k nearest neighbors w.r.t. q from the case base; Adapt the proposed solutions to a solution s(q)  compute s(q) by combining the solutions sj of the cases cj. sj is weighted as per the differences between cj and q; Learn after applying s(q) to q in reality  Store the new solution in the case base for solving q’. Evaluate performance : Rejection ratio = no. of unanswered queries / total no. of queries. Figure 3 : Case based reasoning mechanism CBR is selected for the proposed healthcare recommender system due to various reasons. The healthcare domain has an underlying model, the process is not random and the factors leading to the success or failure of a solution can be captured in a structured way. Cases recur in healthcare domain though there may be exceptions and novel cases. Healthcare solutions can be improved through case retrieval and case adaptation. Relevant healthcare cases are available at different healthcare institutes; it s possible to obtain right data. Case retrieval is the process of finding within the case base those cases that are the closest to the current case [31]. There must be criteria that determine how a case is evaluated to be appropriate for retrieval and a mechanism to control how the case base is searched. Most often, an entire case is searched. But, partial search is also possible if no full case exists. Agents: Decision-making agents (DMAs), mediator (M); Input : Query case (q); Output: Retrieved cases s(q); 1. DMAs define the query case and inform the same to M. M requests DMAs to specify their preferential parameters. 2. DMAs negotiate with each other and define aspiration point (p a), reservation point (pr), indifference threshold (ith), strong preference threshold (st), weak preference threshold (wt) and veto threshold (vt). DMAs communicate this to M. 3. Repeat until DMAs are satisfied with a solution or concludes that no compromise point exists for the query case. 3.1 M retrieves a set of cases from the case base: the most similar case to the query case and its characteristics neighbors. M sends the search results to DMAs. 3.2 If the retrieved cases are acceptable to DMAs, the search process stops. Otherwise DMAs refine their preferential parameters; go to step 3.1. Figure 4 : Case retrieval mechanism

A case is a record of a previous experience or problem in terms of problem definition, patient’s symptoms, drugs, solution methodology, test results and recommendations [32]. A case base also stores global best practices, standards, valid drugs, price and contacts of specialists. Data is stored based on domain knowledge and objectives of the reasoning system. The cases should be stored in a structured way to facilitate the retrieval of appropriate case when queried. It can be a flat or hierarchical structure. Case indexing assign indices to the cases for retrieval and comparisons. There are different approaches of case retrieval [33]. In case of nearest neighbor search, the case retrieved is chosen when the weighted sum of the features that match the query case is greater than the other cases in the case base. A case that matches the query case on n number of features is retrieved rather than a case which matches on k number of features where k < n; different features may be assigned with different weights. Inductive approach is driven by a reduced search space and requires reduced search time. This results reduced search time for the queries. Knowledge based approaches select an optimal set of features of case by using domain knowledge. The complexity of case retrieval depends on multiple factors: (a) number of cases to be searched, (b) domain knowledge, (c) estimation of the weights for different features and (d) case indexing strategy. The mediator agent searches for a set of cases similar to the query case on the basis of the specifications of the query case and the preferential parameters as defined by the decision making agents. Aspiration point is the value of an attribute which is desirable or satisfactory to the DMAs. Reservation point is the value of an attribute that the DMAs like to avoid. DMAs inform the mediator agent regarding various preference thresholds in order to compare alternative cases. There is an interval of preference wherein it is not possible for the DMAs to distinguish between different alternatives due to imprecision and uncertainty of measurements of various attributes. This is indifference threshold. Strong preference threshold is defined as minimal change of any attribute that makes the new alternative case strictly preferred with respect to a set of attributes. There exists an intermediate region between indifference and strong preference threshold where the decision-making agent hesitates to compare alternatives. It is weak preference threshold. Veto threshold indicates what is the minimal change of any attribute that makes the new alternative unacceptable regardless of the value of other attributes. The mediator tries to explore the most similar case with respect to the query case and also a set of cases within the neighborhood of the most similar case. The neighborhood is defined by a set of cases that are not worse than the middle point. These cases indicate to what extent the values of particular attributes can be possible with respect to the most similar case.

Case adaptation is the process of translating the retrieved solution appropriate for the current problem; it adds intelligence to the recommendation process [34]. There are various approaches of case adaptation. The retrieved case can be directly used as a solution to the current problem without any modification. Otherwise, the retrieved solution should be modified according to the current problem. The steps or processes of the previous solution can be reused or modified. The solution of the current case can be derived by combining knowledge of multiple retrieved cases. Case adaptation is a complex decision making task, it considers multiple factors: how close is the retrieved case to the query case? How many parameters are different between the retrieved and the query case? DMAs can apply common sense or a set of rules or heuristics for case adaptation. input: Retrieved case(s), Query case; output: Recommended solution; 1. Cosense: DMAs view the complete information path, unified chronological ordering of all the events of the search process and try to make sense of the search results. 1.1 DMAs view the sense making trajectories of other agents. A DMA may hand-off the sense making task to an expert if it is difficult to understand the search results. 1.2 DMAs share relevant information and negotiate to reach an agreement. 1.3 DMAs verify whether the solution of the retrieved case can be applied to the current case directly. 1.4 DMAs analyze the gaps between the query case and the retrieved case (s) and sense the need of appropriate modifications by assessing risks, threats and opportunities of the current problem.

2. DMAs decide how to respond to the change and finally recommend the solution of the current case rationally. Figure 5 : Case adaptation Mechanism Making sense of the information found during an investigational web search is a complex task of case based reasoning. Sense making is to find meaning in a situation, it is the cognitive act of understanding information. The system should support collaborative information search by providing several rich and interactive views of the search activities of a group. One of the problems facing HCI research today is the design of computer interfaces to enable sense making of the processed information. Sense making is not only important for individuals, but also for groups to achieve shared goals. Traditional sense making tools focus on data mining, provide better information representation, visualization and organization of search results. But, it is also required to support the collaboration and communication that occurs among the investigators when they make sense of information together. Soft computing tools like artificial neural network (ANN) and memory based reasoning can be used as the computational components of the proposed healthcare recommender system [30,32]. In this scheme, prediction query manager (PQM) receives new query request and consults with ANN and MBR concurrently. When both predictors agree in prediction value, PQM normally returns the predicted value. When the predictions of ANN and MBR are significantly different, PQM reports failure and asks for the opinion of human experts. ANN is trained with the given data set or cases stored in the case base. The feature weights are calculated. When a new query comes in, k nearest neighbors are retrieved from the case base based on the feature weight sets. The prediction value of ANN is utilized in conjunction with the prediction of MBR system. ANN predicts on the basis of trained data and test data. MBR predicts the solution based on k-nearest neighbor cases. This provides extended information for the query with most similar cases in the case base.

Healthcare expert

New query

Prediction manager

Case adaptation

Predicted value

k-most similar cases

New cases Feature weights Online learning

Artificial neural network

Knowledge creation

Training algorithm

Memory based reasoning

Initial training data

Case retrieval

Healthcare Case Base Case maintenance

Best practices

Old Cases

Figure 6: Healthcare Recommender System Theorem 5 : The mechanism (HM) uses the intelligence of workflow management system for efficient

time management, exception handling and resource assignment during registration, consulting, testing and surgical operations.

The healthcare service provider should use a workflow management system to improve quality of service, operational efficiency and to ensure the safety of the service consumers through proper resource allocation, capacity utilization, meeting scheduling and exception management. The system requires proper integration among process definition, workflow engine, rules engine and healthcare information system through exchange of data, events and actions [35]. Generally, sequential and parallel control flows are used for simple time scheduling. Process optimization, high throughput and efficiency are essential to improve revenue and reduce cost of the service provider. Theorem 6 : The mechanism (HM) uses the intelligence of web enabled ERP system for improved

coordination and integration among various healthcare units. The mechanism uses an web enabled enterprise resource planning system for fast and correct transaction processing, financial management and supply chain coordination among various tiers of the healthcare chain. A typical ERP system should be used for sales and distribution, materials management, finance and cost control and human resource management. The ERP system should be integrated with workflow management, supply chain management and business intelligence systems for a complex and large healthcare organization. The supply chain management system should be used for collaborative planning, forecasting and replenishments, order management, distribution and demand planning, inventory control, warehousing and shipping functions. The BI system should have data warehousing, analytics, data visualization, data mining and performance measurement modules for strategic decision making. The enterprise applications of multiple tiers of the healthcare supply chain are integrated through internet.

3.2 Communication Schema Theorem 7: The mechanism (HM) uses the intelligence of web, videoconferencing and mobile

communication system telemedicine.

for collaborative information seeking, virtual and critical patient care and

An efficient networking schema should use web service, video conferencing and mobile communication intelligently. The service consumers and the healthcare service provider should be able to interact effectively during emergency. Video conferencing enables critical patient care and virtual patient visit. Web service provides a trusted computing platform where the agents can share data through secure communication channels for registration, workflow administration and time scheduling. Rural healthcare infrastructure should use secure wired and wireless communication system for urgent cases. The rural people from remote places should be able to communicate with healthcare specialists for necessary advice, fast-aid and making transportation arrangement through ambulances during emergency (e.g. sudden critical sickness or accidents). Further, the rural people should learn the basic knowledge of medicare, hygienic life style, family planning and preventive measures through television and radio broadcasts. Knowledge is a significant asset of any healthcare service provider. It is the state of knowing and understanding the medical problems of the patients. Knowledge management is a complex multidimensional concept; the basic objective is to support creation, transfer and application of knowledge appropriately [36]. Data is raw numbers and facts, information is processed data and knowledge is processed information. Knowledge management focuses on exposing individuals to potentially useful information and facilitating assimilation of information. It involves enhancing individuals learning and understanding through provision of correct information. The healthcare experts should use a cooperative communication system for effective learning, solving complex problems and intelligent decision making. A cooperative communication schema enables creation, storage, sharing, distribution and transfer of knowledge and information among a group of authorized entities of a complex healthcare organization. It also provides effective search and retrieval mechanisms for locating relevant information. It is essential for collaborative information search which may be explicit or implicit [37,40]. The level of mediation implies how aware a system is of the contribution of different searching agents and how it uses those

contributions to influence the search of the investigators. The decision making agents can collaborate synchronously or asynchronously; they may work at the same place at the same time or may be distributed at different sites. Different agents may play different roles in the searching process. The agents can divide the task in different ways depending on the roles. The allocation of tasks depends on the nature of tasks, skill and experience of the agents and the capabilities of the system that mediates information seeking. Collaborative information seeking is gradually becoming essential in healthcare management; the specialists should be able to find out good solutions for critical cases. Videoconferencing is an important component of cooperative communication schema that gives support to critical patient care and virtual patient visit, medical board meeting, consulting, dispute resolution, telemedicine and negotiation in trading process. It provides many benefits in terms of reduced travel cost, faster decision making, wider participation in decision making, improved quality of service, increased productivity, improved customer relationship, better team management and expanded global reach. It is particularly very useful for critical patient care and emergency situations when the required skill and domain knowledge is rare at a healthcare institute. But, a field study on medical professionals found that people process information differently between videoconference and face-to-face communication. In videoconference, people tend to be more influenced by heuristic cues and communication skill and likeability of the speaker rather than by the quality of arguments of the speaker [39]. Communication through videoconference presents the challenges of difficult audio localization, turn taking, conversation speed, change in cue salience, asymmetrical personal distance and high level of selfawareness and all these factors increase the cognitive workload demand from the participants as compared to face-to-face communication. Cognitive theory has a significant implication on sense making through videoconference. In spite of all these constraints, videoconference is useful to streamline knowledge adoption and transfer in healthcare management. Another important component of a cooperative communication schema is Internet, intranet and extranet. Web enabled enterprise applications are essential for efficient coordination, integration and workflow control. The trading agents should be able to share strategic information with confidentiality through a trusted computing environment. The online transactions should be processed through web maintaining privacy, confidentiality, message integrity and nonrepudiation. Secure Service Oriented Computing (SSOC) is the basic building block of enterprise application integration. It integrates a network of enterprises by positioning web services as the primary elements. Each web service exists as an independent software program with distinct design characteristics. Each service is assigned a specific function and capabilities. A service composition is a coordinated, aggregate of services that integrates different applications through robust interfaces. A service oriented computing platform is comprised of a distinct set of components; each component encapsulates specific business logic and service. A service oriented computing model is expected to provide a trusted computing environment to the users of the system. Otherwise, malicious agents can attack the healthcare system in different ways. The most promising technology that supports SSOC is web service. It supports the execution of various business processes that are distributed over a network and available through standard interfaces and protocols. Service oriented computing model requires an intelligent design paradigm to protect its users from miscellaneous types of malicious attacks such as phishing, cross site scripting, malicious file injection, insecure direct object reference, cross site request for query, information leakage, improper error handling, broken authentication and session management, insecure cryptographic storage and failure to restricted URL access [19]. The healthcare information system should have service oriented architecture to enhance the efficiency, agility and productivity of the agents.

3.3 Data Schema Theorem 8 : The mechanism (HM) uses the intelligence of an efficient data schema with proper access

control, data recovery and back up policy for intelligent data analysis, query and transactions processing. The healthcare information system requires a well-defined master data schema and configuration setting for fast and correct computation and intelligent query processing. The primary elements of data schema

are an efficient data extraction and noise filtering algorithm, a secure data warehouse and a set of data mining algorithms. Raw data is extracted from heterogeneous sources; the extracted data is filtered and stored in a secure data warehouse. The data mining algorithms are applied on the stored filtered data and new knowledge is discovered and applied for intelligent decision making. The healthcare service provider (Pf, Ps) evaluate the performance of the trading agents associated with the supply chain periodically based on historical trading data stored a secure data warehouse; this evaluation is important for efficient financial and cost accounting, sourcing and risk management. Pf compute the credit rating of the service consumers and also performs spend analysis; P s compute the vendor rating of the vendors on the basis of quality of products and delivery performance. P s inform the vendor rating to the vendors periodically; efficient vendors are rewarded and the inefficient agents get alert or blacklisted as per regulatory compliance policy. The data schema should support various transactions maintaining confidentiality, message integrity and nonrepudiation through credential based access control mechanism. Auditing is required to check fairness and correctness of computation and to validate security policies on periodic basis. Data plays a strategic role in healthcare information system and its protection against unauthorized disclosure (secrecy) and improper modifications (integrity), while ensuring its availability to legitimate users (no denial of service) is also very important.

3.4 Application Schema Theorem 9 : The mechanism (HM) requires efficient enterprise application integration among ERP,SCM,BI, WFMS and KMS for improved quality of service, coordination and resource utilization. The complexity of application schema depends on the architecture of healthcare chain, breadth and depth of service offering, scalability and process flows. A simple healthcare service model may require only a transaction processing system having patient registration, billing and payment processing modules. A complex healthcare chain may require an optimal mix of enterprise resource planning (ERP), supply chain management (SCM), business intelligence (BI), workflow management (WFMS) and knowledge management system (KMS). An web service oriented architecture should be able to integrate multiple enterprise applications properly to ensure improved coordination among different business units. Efficient enterprise application integration is particularly useful for payment processing, financial and cost accounting; workflow control for registration, consulting, testing and surgical operations and supply chain coordination in terms of demand, distribution, inventory planning, sourcing, order management, warehousing and shipping operations. A simple workflow management system should have registration, consulting, testing, surgical operation and discharge modules. The ERP system should have sales and distribution (SD), material management (MM), finance and cost control (FICO) and human resource (HR) management modules. The business intelligence system should have data warehousing, analytics, data visualization, data mining and performance measurement modules. The other important applications are related to information security, videoconferencing, telemedicine and regulatory compliance. The knowledge management system should support creation, storage, sharing and application of knowledge through case based reasoning. Case based reasoning offers different types of benefits to a healthcare KMS. Knowledge acquisition task becomes simple; the decision making agents can avoid repetiting mistakes made in the past. They can reason incomplete or imprecise data; they can explore a new domain efficiently. They can avoid repetition of all the steps that need to be taken to arrive at a solution. The recommender system can learn over time as it encounters more situations and create more solutions. CBR mechanism can be used in different ways to a broad range of domains.

5. Conclusion The healthcareflow mechanism (HM) assumes that the agents act rationally to achieve their objectives and follow the protocols correctly with correct inputs. The mechanism is effective in a trusted service oriented computing environment. It is an emerging cross-disciplinary paradigm of distributed computing

that is changing the design pattern and architecture of complex information system. A set of autonomous service components act in a collaborative computing environment. The mechanism does not study any malicious behavior of the trading agents and specific types of administrative inefficiencies which can disrupt normal healthcare service. It includes the collusion of the trading agents against regulatory compliance, financial fraud in e-transactions, quality problem in testing and sourcing, nonavailability and failure of medical equipments, malicious work culture, excessive work load, strikes and physical security problem of healthcare service provider. Globally healthcare organizations are undertaking massive business process reengineering initiatives and many of these reforms are supported by the strategic use of advanced information and communication technology. The proposed mechanism should provide better integration and improved coordination of flows of material, information and funds within and across healthcare firms, experts and patients. This results improved patient care, greater accuracy, cost efficiency, ease of processing, increased productivity and fast response time in healthcare service. Service oriented computing results improved interoperability, increased federation, and organizational agility through a standardized, flexible, reliable and scalable architecture. An intelligent healthcareflow mechanism should explore other strategic moves such as integrated healthcare networks, critical patient care, improved monitoring system, videoconferencing, virtual patient visit, telemedicine, secure transmission and storage of real-time medical data and human-computer interaction for improved quality of service at reasonable cost.

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