An information systems approach to the intervention and prevention of AIDS

Ihformarion Processing & Manugemenf Vol. 28. No. 2, pp. 269-280, Printed in Great Britain.

AN INFORMATION INTERVENTION

1992 Copyright 0

0306-4573/92 f3.M) + .OO 1992 Pergamon Press plc

SYSTEMS APPROACH TO THE AND PREVENTION OF AIDS LI D. xu

Department of Management Science & Information Systems, Wright State University, Dayton, OH 45435, U.S.A.

and LING X. LI AIDS Research Project, School of Medicine, P.O. Box 927, Wright State University, Dayton, OH 45401, U.S.A.

Abstract -According to the Second Annual Meeting of National AIDS Demonstration Research (1990) hosted by the National Institute on Drug Abuse (NIDA), a large amount of data is collected in AIDS intervention and prevention research. But so far those data are not substantially processed, analyzed, and utilized. By employing an information systems approach, available data can be used more effectively for AIDS epidemic research and for better intervention and prevention. The objectives of this paper are: (a) to provide a conceptual framework of information systems in AIDS intervention and prevention; (b) to show the applications of such information systems, which will inspire AIDS intervention and prevention researchers to take the lead in implementations of information systems technology; and (c) to present an exampte of such information systems that has been implemented in recent years.

1. INTRODUCTION

In AIDS intervention and prevention (AIP), an increasing amount of data is collected and presented to researchers for analysis and interpretation. However, the volume of data collected far exceeds researchers’ abilities to analyze without employing computers. In fact, many analytical tasks currently performed in AIP programs without using computers are actually appropriate for using computer information systems. Such tasks cover a broad spectrum including data analysis, information retrieval, follow-up scheduling, and many other tasks too numerous to list. It is obvious that the introduction of information systems has revolutionized many areas; however, the impact of information systems on AIP is just beginning. Currently the literature on information systems in AIDS intervention and prevention (ISAIP) is very limited, but it is likely to grow rapidly. The intent of this paper is to explore a framework for the development of a comprehensive ISAIP and the various applications of such systems. Outlined is an ISAIP approach in which data can be input, analyzed, and transformed to a variety of information needed in AIP. 2. SYSTEM OVERVIEW

The configuration of information systems depends on many factors, such as the size and the computing needs of the AIP program. Therefore, in many cases there will be a great variety of technology in both hardware and software. This section will concentrate on the general aspects of ISAIP development, such as systems components, software, and analytical models. 2.1 Systems components Information systems technology application in AIP mostly seems to fall within a spectrum consisted of database, decision support systems (DSS), and expert systems (ES). In Correspondence

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other words, the three main subsystems of an integrated ISAIP are a database processing system, DSS, and ES. Although each of these is at a different level of maturity in AIP applications, their usefulness in AIP makes it possible to label them collectively as ISAIP. We shall describe the use of these three information systems technologies to support AIP. 2.2 Database and its applications An AIP database is a collection of data logically organized to meet the information requirements of AIP. The use of a database mainly involves the storage of large amounts of data for subsequent retrieval and analysis. Because the data stored in the database can be of tremendous value to analysis, having a complete database is always an attractive feature of an ISAIP. Three steps might be taken to develop a database such as, (a) establish a database administrative function. The ISAIP Director develops standards and procedures for the creation, processing, and safeguarding of AIP data; (b) develop structured databases to replace the individual files; and (c) install the set of programs required to create and manipulate the databases. Factors that are important in developing a database include cost-benefit analysis of a database system, the requirements of database systems program, ease of programmability, user interface, etc. A typical AIP database (see Fig. 1) includes the following: AIDS-risky behavior sub-database. This sub-database contains AIDS Initial Assessment (AIA) and AIDS Follow-up Assessment (AFA) data. Such data are mostly derived from face-to-face, in-depth conversational interviews based on the questionnaire issued by NIDA and then stored in the database. AIA and AFA data stored in the database can be utilized in many different ways. For example, six-month AFA data can be compared with AIA data to examine the effect of the AIP project on human immunodeficiency virus(HIV) related risk behavior, such as drug use and sexual behavior. The results obtained from this analysis will indicate the change in frequency of drug injection, needle risk, sex risk among injection drug users (IDUs), etc.

Fig. 1. A typical

AIP database.

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HIV antibody blood test sub-database. The data from those who volunteered for HIV testing are stored in this sub-database and can be examined to identify demographic and AIDS risk characteristics that differentiate those who subsequently returned to obtain their HIV test results from those who did not return within a certain period following the initial interview. Locator information sub-database. The data stored in this sub-database include name, address, date of birth, and interview date of AIP project participants. Some of the participants are homeless or live in shelters. Others moved out of the region, or are incarcerated or deceased. These data are extremely important for providing AIP outreach workers with information they need to locate project participants. This sub-database is under constant change and is linked with other sub-databases to generate various reports. AIDS risk reduction education sub-database. The data stored in this sub-database cover pathology, addiction, and sex behavior education. Information from this subdatabase can be used to design AIDS intervention education programs for IDUs and their sex partners. For example, the data on IDUs with multiple sexual partners stored in this sub-database can be analyzed to direct educational efforts towards those that may provide the greatest potential benefits. AIP database. The database as a whole can be employed to perform a variety of analysis as listed above. For example, the data on IDUs and their sex partners stored in the AIP database can be used to generate indices to assess risk behavior. Such data may consist of data on education, income, current work situation, drug use behavior, sexual practices, drug treatment history, HIV status, etc. An effective AIP database needs database software for efficient data entry, storage, and retrieval. Database software can be either purchased or developed in-house. As an example of in-house development, some information of substantial analytical value is often included in marginal notes on a questionnaire (Flavin, 1990). An AIP data entry program should be designed to incorporate such information. The power of an AIP database also comes from its ability to handle natural language retrieval. Currently most database management systems are only able to handle exact retrieval from a database of precisely known values. However, in an AIP environment, both data and retrieval process may need to be handled in an approximate manner. In other words, data values may not be known exactly, and the query is to retrieve contents “close” to those requested. Suppose AIP researchers are interested in knowing the approximate number of high-risk behavors who are willing to receive risk-reduction education. Consider the following query in natural language: “List most of the names of the AIDS-risky behavors who have higher risk and would like to have risk-reduction education.” In this query, “most” is a linguistic quantifier, “higher” is a qualitative numeric descriptor, and “like” is a qualitative non-numeric descriptor; these data values are not precise and exactly known. In such situations, a special database program needs to be designed for natural language interface (Kamel & Hadfield, 1990; Xu, 1991). In an AIP database, some data such as participant name, sex, and drug behavior are highly confidential. AIP database security means allowing only authorized staffs to perform authorized actions on specified data, subject to a set of constraints. Some database users who are authorized to access the AIP database may not have complete access to all of the data. Some users have access to more data than other users, and some users have more extensive processing rights as well. Once the data security procedures have been established by the ISAIP director, many of these access and processing rights can be enforced by database management systems. Constraints that are not enforced by database management systems can be enforced by programs written by internal programmers. 2.3 Decision support systems and its applications Scott-Morton defined DSS as “interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems” (Scott-Morton, 1971). DSS can provide modeling capabilities using statistics, operations research, and artificial intelligence (Ayati, 1987). Two major characteristics of DSS in AIP are, first, to address some problems in AIP that are semi-structured or unstructured and second, to incorporate both AIP data and models. DSS is able to provide AIP researchers with new insights on

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complex problems by employing a variety of models. A substantial modeling effort is usually needed for developing AIP DSS applications. The applications of DSS range from helping AIP researchers to analyze the effectiveness of different AIP strategies to helping the AIP program improve performance of the program. Figure 2 shows how DSS is used to solve two semi-structured or ill-structured problems in AIP. Example 1 presents a hypothesis: intense risk reduction education is better than standard risk reduction education. By employing data from the AIP database, AIP researchers are able to build models to tackle the problem. Example 2 illustrates how DSS is used to select an AIP plan in order to target a certain segment of the population for better intervention and prevention. The information generated by the DSS enables AIP policy makers to direct resources and services in an effective manner, and AIP researchers to select relevant research topics. An AIP DSS also has some typical applications, as follows: Outreach program developing strategy. To develop a successful outreach program, a variety of factors must be taken into consideration, such as ethnographic factors, ethnic and cultural factors, dynamics of target population, community and client reactions to outreach, etc. Since the dynamics associated with developing and conducting outreach is changing, a dynamic simulation model can be developed, programmed and implemented in an AIP DSS environment to explore the ideal location for an outreach office, the appropriate target population to implement intervention/prevention, and utilization of the resources available to the outreach network. AZP program effectiveness evaluation. An AIP outreach program is supposed to perform functions such as decrease injected drug use and non-injected drug use, decrease risky needle use, decrease risky sexual behavior, and increase levels of AIDS knowledge. There are a variety of factors that affect the effectiveness of the program, such as socio-cultural factors, demographic factors, education level, employment status, the psychodynamics between participants and AIDS intervention workers, etc. A multiple regression model can

Fig. 2. AIP DSS applications.

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be developed and implemented in an AIP DSS environment to evaluate the performance of the program. The effects of personality on needle-use risk taking. Personality factors may play an important role in whether or not IDUs alter needle-use practices to reduce risk of HIV exposure (Gruenbaum, 1990). In order to gain an insight into the effects of personality on needle-use risk taking, a multidimensional model (i.e., in combination with general psychiatric classification) can be applied to such a personality study in an AIP DSS environment. 2.4 Expert systems and its applications ES has been used for complex decisions in areas ranging from medical diagnosis to geological exploration (Buchanan, 1978, 1984). An ES is a computer system that applies the knowledge of an expert to problem solving. The application areas of ES are usually domain-specific, and the ES usually presents the solutions in the form of judgment or recommendations. The typical problems that can be solved by using ES in AIP are screening AIDS-risky behaviors and scheduling AFA. Figure 3 presents the examples of employing an ES in AIP. Screening AIDS-risky behaviors. In this case, an ES replaces AIP researchers to identify who is AIDS-risky and most appropriate to target for intervention, based on factors such as whether a subject has injected intravenously or subcutaneously in the last six months and been out of any drug treatment program for at least 30 days prior to initiating project participation, has had sex with a partner/partners who injected drugs intravenously in the past six months and was not in treatment in the past month, and other factors. ES can be employed to (a) identify who is AIDS-risky and needs immediate intervention, and/or (b) learn how the inference process results in the identification. Scheduling AFA. One routine operation in an AIP program is to assist the AIDS atrisk population in developing means of enhancing AIDS risk-reduction techniques by dem-

Fig. 3. Employing an ES in AIP.

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onstrating the potential impact the illness could have on their lives, and by increasing their perception of personal vulnerability in regard to HIV infection. This is achieved by providing information, increasing awareness about AIDS/HIV, offering risk reduction strategies, and ultimately, by providing empowerment and assistance in developing personal goals, Follow-up strategies are designed to encourage high-risk subjects’ ongoing participation in intervention activities. Baseline AIA, &month, 12-month, and l&month AFA data are compared to determine the risk reduction. Some typical problems are (a} determining the ideal time for a participant to return for follow-up; (b) some high-risk subjects return and some not; and (c) some do not return on time, and the follow-up needs to be rescheduled. There are a variety of factors that determine the rescheduling, such as demographic, social, and psychological factors. ES can be used to do scheduling and rescheduling. Instead of using ES, the tasks discussed above can be performed by AIP researchers. However, there are a number of reasons why ES replaces human functions. For instance, AIP researchers’ expertise may be a scarce resource, and AIP researchers may not have enough time to comprehend and process ongoing updating data collected from a large number of participants. The ISAIP we propose is an integrative system (see Fig. 4) (i.e., the database is integrated with both DSS and ES, and DSS can also be integrated with ES). First, in DSS, a functional component called the data management component provides access to data that are used as inputs to the DSS for computation. In ISAIP, the AIP database can provide this component with data for DSS modeling. Second, the interaction of databases and ES is a topic receiving increasing attention (Alzobaidie, 1987). With the implementation of the AIP database, the data required by the ES can be made available by the AIP database. Third, the output of DSS can be the input to the ES. Similarly, the output of ES can be the input to the DSS as well. 2.5 Software Commercial software may not meet the unique needs of AIP programs. In fact, there is a whole spectrum of alternatives with respect to software acquisition, such as (a) purchasing software and installing it directly, (b) purchasing software and modifying the program to fit the needs of AIP, and (c) having internal programmers develop software. Purchasing software and modifying it is common in AIP research. For example, SAS and SPSS can be purchased and used as a statistical software. Very often, the scope of the variables in a study requires researchers to break the data down into subsets pertaining to content such as demographics, drug use, needle-cleaning practice, sex behavior, drug treatment history, etc. One solution is to develop a program that will allow researchers to specify a subset of variables from any or all of the data sets and create an SAS or SPSS sub-dataset containing only required variables (Teitelbaum, 1990). Similarly, alternative (a) or (c) may apply to many situations. As an example, it is known that analytical models are of fundamental importance for AIP data analysis. As a result, one of the important functions performed by an ISAIP is to analyze AIP data by employing analytical modeling software. Analytical modeling software can be either purchased or developed by internal programmers. Such software should be able to interface with the existing computing environment. 2.6 Analytical models and their applications As discussed above, analytical modeling software is a major application software in ISAIP. In the following sections, selected analytical models and their applications in AIP are discussed briefly. Interested AIP researchers may further acquire or develop software programs for their projects. Statistical analysis. Statistical methods and corresponding software are able to deal with problems consisting of large amounts of data. At present, statistical techniques are increasingly used to summarize large amounts of data in AIP. The most commonly used statistical methods in AIP include frequency distribution, chi-square testing, logistic regres-

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sion, hypothesis testing, analysis of variance, and multivariate anaiysis. Most statistical programs can be used in conjunction with the AIP database. AIP researchers find that statistics are useful for various reasons: 1. Frequency distribution analysis makes it possible to identify high-risk subjects needing intervention (Baumgartner, 1990). Intervention can then be provided in an efficient manner to accommodate the number of subjects needing it. 2. The chi-square test is by far the most common non-parametric method (Siegel, 1988). AIDS epidemic research is concerned with the practical issues of preventing spread of the AIDS virus. The objective of measurement is determining the presence or absence of HIV and estimating survival. Chi-square testing is very useful for examining the association between a single categorical independent variable and nominal or ordinal dependent variables (Jose, 1990; Siegal, 1991). 3. The logistic regression model is a useful method in AIP research (Iguchi, 1990; Jose, 1990). It models the relationship between a dichotomous outcome variable and a set of covariates. Suppose we are attempting to determine which of a group of women are most likely to pass HIV to newborns. Some factors that may influence the event are age, sex with IDUs, injecting drug, etc.; all are nominal or ordinal variables. 4. Analysis of variance (ANOVA) is a method for determining the existence of differences among several population means (Hogg & Craig, 1978). ANOVA can be applied to experimental designs of AIDS risk-reduction activities -for example, to explore which intervention strategy is more efficient, the standardized intervention or the intense intervention. ANOVA can help to provide a concise summary of the structure of the AIDS risk reduction data and a descriptive picture of the different sources of variation. 5. The hypothesis test can be used to test theoretical constructs. One example is that individual subject can be studied in terms of a social or group context. An examination of an AIDS patient’s change in a social setting will provide a better understanding of AIDS patient (Alperin, 1990). As another example, a study can be conducted to examine if differences in behavior change attributable to interventions exist between men and women (Anderson, 1990). If intervention results in different rates of modification between males and females, we may conclude that alternate program should be administered accordingly. 6. An AIP study involves the recording of a series of measurements over time on a number of different variables known to be important components of the special topic under investigation. Multivariate methods can be used for the simultaneous analysis of data on several variables (Farrell, 1985; Jose, 1990). As an example, the multivariate method can be used to analyze the overall differences among different AIDS-risky behavior groups. 7. Baseline AIA and AFA data can be analyzed in terms of regression analysis. The typical predictor variables can be demographics, high school graduation, drug usage patterns, AIDS knowledge at pretesting, etc. (Copher, 1990). 8. Weibull distribution analysis can be used to characterize the distribution of periods that elapse from the transmission of HIV to the manifestation of AIDS (Kotva & Zdenkova, 1990). Fuzq~ mathematics. In AIP research, a lot of the data are retrospective and obtained from behavioral observation such as interviews and questionnaires. As a result, these data are both vague and imprecise. Because fuzzy mathematics provides an appropriate framework for the representation of imprecise data and models, fuzzy mathematics is emerging as an important analytical tool in AIP. As an example, conventional cluster analysis, which is based on crisp set concepts, can be used to identify and characterize population groups with respect to AIDS. However, the characteristics exhibited by participants do not represent a precise numeric concept, and identification classes cannot be modeled exactly in terms of crisp sets. As an alternative to conventional cluster analysis, fuzzy cluster analysis can be used to identify at-risk groups in terms of a real number defined on the closed unit interval. As another example, in dynamic simulation of AIDS epidemics, the effectiveness of systems dynamics as a tool of modeling, simulating, and analyzing AIDS can be increased if it is extended to deal with imprecise and vague variables (Levary, 1990). Mathematicalprogramming. Mathematical programming is an optimization technique that solves a broad class of problems dealing with interactions of variables, subject to a set

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of constraints. Mathematical programming is the generalized programming discipline of which linear programming, nonlinear programming, dynamic programming, integer programming, and multiple objective programming are all subsets. In all applications of mathematical programming, the intent is either to maximize or minimize an objective function, given the constraints. In AIP, examples of minimization objective functions include minimizing risk to high-risk participants from delay in intervention or prevention, minimizing risk from prospective treatment, minimizing unnecessary intervention or prevention, and minimizing total costs to AIP programs. Conversely, maximization of objective functions encompass maximizing the utility of available resources to AIP, such as AIP personnel and facilities. Constraints related to AIP include availability of AIP personnel, financial restrictions to AIP programs, time-related constraints, etc. Learning curve. The learning curve model is a quantitative method which expresses the relationship between the cumulative units of production of an object and the average time or cost necessary to produce that object. A learning curve charts the learning effect of a doubling of production quantity and the resulting reduction in worker-hours required and/or cost incurred in the production of the quantity. This model can be used to describe the decrease in high-risk behaviors against the number of intervention/prevention of certain types of behavior. This model is also good for measuring the effectiveness of AIDS risk-reduction education programs, such as finding the best time to implement AIDS intervention. Simulation. AIP modeling using simulation has many advantages, such as creating realistic models that incorporate a greater level of detail. Currently, the simulation study of AIP is limited, but it is likely to grow in the next few years. Simulation has been used to study the spread of AIDS, the prevalence of HIV-infected persons, the deterministic or stochastic properties of partner selection, and the characterization of behavior changes (Ahlgren, 1990; Kotva & Zdenkova 1990; Virkkunen & Hamalainen 1990). Both dynamic and discrete simulations are useful in AIP. Systems dynamics is effective in dealing with time-varying interactions among components of an AIDS epidemic model. SIMAN has been used to analyze screening policies to reduce HIV transmission to newborns (Brandeau, 1990). In the above, the statistical methods, fuzzy mathematics, mathematical programming, learning curve, and simulation are illustrated to show their applicability as application software in ISAIP. The choice of method depends on the nature of the data and the objective of the AIP research. For example, an AIP researcher who has only time series data and wants to project the trends of AIDS spread would choose one of the time series techniques, On the other hand, if the objective is to establish a causal relationship between AIP variables, then a regression technique is appropriate. In selecting mathematical models, AIP researchers also need to ask questions concerning the underlying assumptions and biases inherent in the methods, and to compare alternative models.

3. POTENTIAL

APPLICATIONS

OF ISAIP

In the last section, we discuss model-based application of ISAIP. In addition to the applications discussed above, there are other numerous AIP topics amenable to information systems applications. Those applications yet to be explored include AIP program operations management, AIP resource allocation, AIP personnel training, and new AIP site selection. 3.1 AIP program operations management Currently, considerable resources have been devoted to operating and managing AIP programs. Operation planning is a function routinely performed by AIP program managers to schedule and allocate resources for program operations. Program management is oriented toward semi-structured and ill-structured decision making. ISAIP can help coordinate data, information, decisions, and actions. These include subsystems for operations scheduling and personnel assignment capable of incorporating all relevant information.

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3.2 AIP resource allocation This application would explore different resource allocation possibilities. ISAIP can allow AIP researchers (a) to conduct what-if analyses to generate diverse intervention/prevention strategies under the constraints of limited resources; and (b) to experiment dynamically with resource allocation scenarios. 3.3 AIP training AIDS intervention training is an ongoing activity sponsored by NIDA to intervene in the epidemic of HIV infection among IDUs and their sexual partners and offspring. Currently, there is increasing evidence regarding the use of ES as an intelligent tutoring system (Yazdani, 1986). In addition to classroom training, ES can be used as a tutor. Trainee AIDS workers could be trained in the identification of AIDS-risky behavior. ES can be designed to adapt to the requirements of AIDS training, such as the adult learning processes that shape the design and delivery of effective training, the impact of culture, ethnicity, and other specific factors on the design and delivery of training. 3 4 AIP site selection Both DSS and ES can be used to determine the optimal locations for a set of new AIP sites with respect to a set of existing sites. ES can combine judgmental rules of AIP researchers with quantitative tools in order to determine new sites. 4. APPLICATION EXAMPLE 4.1 System overview This section reports on an ISAIP implemented at Wright State University. The system is developed for use in a regional AIP program and used on 1,250 high-risk AIDS patients successfully. At present, in this ISAIP, database system and partial DSS and ES have been implemented as Fig. 4 shows; more DSS and ES software are under development. The existing database system, DSS, and ES provide a substantial information processing capability. The user is offered the options of database retrieval, analytical modeling, expert consulting, and others. The programs installed on the system are written in C, Prolog, SAS, dBASE III Plus, Foxbase, and other languages. Since commercial programs, which must meet the needs of heterogeneous markets, do not exactly meet our needs, we have developed various programs to meet the needs of AIP research. These needs include not only the generation of a variety of analytical reports, but also the maintenance of an accessible database system for AIP research, and many other systems. To our knowledge, this system represents one of the first-generation ISAIPs. The system appears to meet immediate needs. Acceptance by NIDA and AIP researchers has been excellent. 4.2 Data acquisition AIP data are gathered from sources such as baseline AIA and three AFA interviews, volunteer HIV-antibody test results, participants’ locator information, and AIDS riskreduction activities. AIDS-risky behavior data are collected through face-to-face interviews using AIA, a 54-item, 600-variable questionnaire surveying drug use, sexual practices, and other variables relevant to the transmission of HIV. The questionnaire is developed by NIDA. The follow-up interview using AFA is designed by NIDA as well. Participants’ locator information is also collected at these interviews. HIV test results are sent from the laboratory to the AIP office at Wright State University. AIDS reduction information is gathered after the AIDS intervention education is implemented. 4.3 Data entry The data entry programs consisted of separate programs for AIDS-risky behavior (AIA and AFA questionnaires), HIV-antibody blood test, locator information, and AIDS

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risk-reduction education. The programs for AIDS-risky behavior and the HIV-antibody test are written in C, whereas programs for locator information and AIDS risk reduction education are written in dBASE III Plus. The programs allow data to the be entered interactively at any time via a user-friendly menu screen and then verified. Prior to data entry, the questionnaires are coded by trained ISAIP personnel to make sure that the data are in the same format. The locator information, such as name, address, phone number, medical ID, mailing address, interview dates, etc., is verified; HIV test dates and results are reconciled by cross reference. 4.4 Database applications As an example of the database programs developed in this system, a dBASE III Plus program is written. The program menu offers the user insertion and retrieval routines as well as report generation. With the insertion routine, the items on the list are encountered interactively by the user, who is prompted to enter pertinent data into the computer. At the conclusion of the program, the user is offered the options of database retrieval, analysis, report generation, etc. The typical outputs from the system are as follows: l l l l l l l

outreach form for outreach workers (to locate participants), director form for the outreach office director (to keep a record of the participants), mailing label (to mail letters to the participants), cohort mailing list, interval information between interviews, ineligible list (who did not pass the screening process), deceased list (who died during the project).

4.5 DSS and ES applications The data stored in the database can be retrieved and then analyzed by a variety of DSS and ES software in the system including statistical software, decision analysis package, Prolog AIDS-risky behavior identification program, Prolog follow-up scheduling program, etc. The system provides various analytical reports on a demand basis. A&l scheduling example. Initially, the data collected at each interview is input into a database file that includes name, address, phone number, social security number, general physical description, description of a possible hangout location, AIA interview date, and AFA interview dates by 6, 12, and 18 months. These data are merged with basic demographic data collected at the time of AIA, including ethnicity, gender, and birth date. The addition and changes to these data are in a constant state of update. Then the data are input into the ES module to produce an estimate of the return date for participants for each follow-up point. 5. CONCLUSIONS

A comprehensive ISAIP would be an invaluable asset to AIP researchers, whose time could be spent interpreting analytical results and testing theories, rather than struggling with a huge volume of data that far exceeds their ability to manage. This paper proposed a framework for an ISAIP. For an ISAIP developer, the concepts presented here are generic enough to be adapted to the needs of different AIP programs. For ISAIP users, this paper discussed both existing applications and potential applications, so that the implementation of ISAIP could be expedited. In addition to this, an example of an existing ISAIP was presented. Acknowledgements-Great

appreciation is extended to the anonymous referees for their thoughtful comments.

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