Table Of ContentIntelligent 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 ), Healthcare (P ), Testing
w h
(P), Financial service (P) and Supply chain (P );
t f s
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 : P call workflow management system → register C through a service contract on fair
w
healthcare and privacy policy; make a meeting plan for consultation.
2. Consulting : P call case based reasoning system → do health check-up and diagnosis; recommend
h
medication and testing. It may be a single or multiple iterations depending on the complexity of the case.
3. Testing (optional) : P make a testing plan for C and sends it to P; P do testing; send test data to P ;
w t t h
P call analytics → make decision and recommendations.
h
4. Sergical operations (optional) : P make operation plan; P perform operation on C.
w h
5. Supply chain coordination: P call ERP-SCM system → do demand and distribution planning by CPFR,
s
inventory control, sourcing, order management, warehousing and shipping of medicines and medicare
products.
6. Receivables management : P call ERP system → generate invoice; process payment for C in single or
f
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 : P issues discharge certificate to C. C may exit from the system at any stage by submitting a
w
bond to P .
w
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 real-
time 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 know-
how, 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 (P );
h
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 ), reservation point (p), indifference
a r
threshold (i ), strong preference threshold (s), weak preference threshold (w) and veto threshold
th t t
(v). DMAs communicate this to M.
t
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.
New query Healthcare Prediction Case
expert manager adaptation
Predicted
value k-most similar
New cases cases
Feature
weights
Online Artificial neural Memory based
learning network reasoning
Knowledge
creation Training Initial training Case
algorithm data retrieval
Healthcare Case Base
Case maintenance
Best Old
practices 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.
