This article describes the development, testing, and

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1 Bridging the Gap Between Public and Private Healthcare: Influenza-like Illness Surveillance in a Practice-based Research Network Zsolt Nagykaldi, James W. Mold, Kristy K. Bradley, and John E. Bos This article describes the development, testing, and implementation of the OKAlert-ILI System, a bidirectional, dual-use influenza-like illness surveillance and messaging system, during the influenza seasons of and in the Oklahoma Physicians Resource/Research Network, a primary care practice-based research network. We describe how the Oklahoma Physicians Resource/Research Network connected 30 primary care providers to the Oklahoma State Department of Health and how surveillance results were analyzed and fed back to the clinicians on a weekly basis. We demonstrate the timeliness, sensitivity, specificity, acceptability, validity, flexibility, and cost of the system. Finally, we describe upgrades and enhancements to the system based on user evaluation and feedback. KEY WORDS: influenza-like illness, practice-based research, public health, surveillance Enhancing the sensitivity and timeliness of infectious disease surveillance has become a priority for public health to rapidly identify illness patterns or trends that may signify a naturally occurring disease outbreak or a bioterrorism event. Traditional public health surveillance relies primarily on passive reports from hospitals, private healthcare providers, and laboratories. By tapping into an electronic sentinel physician reporting system, the public health sector has the potential to increase the timeliness, sensitivity, and granularity of conventional disease surveillance while participating healthcare providers benefit from timely aggregated information about infectious diseases affecting their communities. The Oklahoma Physicians Resource/Research Network (OKPRN) includes 235 primary care physicians scattered throughout Oklahoma. It is affiliated with the Department of Family and Preventive Medicine at the University of Oklahoma Health Sciences Center and Practice-based research networks (PBRNs) play a vital role in translating research findings into practice by interfacing research and quality improvement in a learning community. 1 At the time of this publication, there were more than 110 PBRNs in the United States. 2 In addition, PBRNs are proving grounds for development, testing, and implementation of new ideas and technologies that enhance primary care. 3 Most PBRNs are affiliated with academic institutions (eg, medical schools and family medicine departments), and some also collaborate with local or state public health entities (eg, health departments and public health clinics). PBRNs offer unique opportunities for bridging the gap between the public and private healthcare sectors. J Public Health Management Practice, 2006, 12(4), C 2006 Lippincott Williams & Wilkins, Inc. This study was partially supported by the Centers for Disease Control and Prevention Cooperative Agreement Number U90/CCU for Public Health Preparedness and Response for Bioterrorism. The authors thank Dr Jim Cacy for his support at the University of Oklahoma Health Sciences Center, Department of Family and Preventive Medicine, Dan Hollacher and Wendy Zhou at Medical Data Solutions for assisting us with their expertise in system programming, and Dr Mike Crutcher, Commissioner of Health, Oklahoma State Department of Health, for providing support to this project. Corresponding author: Zsolt Nagykaldi, PhD, Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, 900 NE 10th St, Oklahoma City, OK ( zsolt-nagykaldi@ouhsc.edu). Zsolt Nagykaldi, PhD, is Assistant Professor at the University of Oklahoma Health Sciences Center, Department of Family & Prevention Medicine, Oklahoma City. James W. Mold, MD, MPH, is Professor and Director of Research Division at the University of Oklahoma Health Sciences Center, Department of Family & Prevention Medicine, Oklahoma City. Kristy K. Bradley, DVM, MPH, is Deputy State Epidemiologist, Oklahoma State Department of Health, Oklahoma City. John E. Bos, MPH, is an Epidemiologist, Oklahoma State Department of Health, Oklahoma City. 356

2 Bridging the Gap Between Public and Private Healthcare 357 FIGURE 1. OKAlert-ILI surveillance system. ILI indicates influenza-like illness; PDA, personal digital assistant; PC, personal computer. with the Oklahoma Academy of Family Physicians. The network has been involved in a number of projects focused on improving chronic disease management and delivery of preventive services. Some have been done in collaboration with the Oklahoma State Department of Health (OSDH), including initiatives to increase the delivery of primary and secondary preventive services, diagnosis and treatment of the dysmetabolic syndrome in primary care, and development of a cancer-reporting system. In the spring of 2003, the OSDH partnered with researchers in the Department of Family and Preventive Medicine and OKPRN to develop a bidirectional influenza-like illness (ILI) surveillance and messaging system. During the summer of 2003, the OKPRN Health Information Technology team in cooperation with programmers in a private company (Medical Data Solutions) developed the OKAlert-ILI System. System Description The OKAlert-ILI System has been developed in an open-source environment utilizing Apache Tomcat server and Java Struts technology on SuSE Linux Professional 9.1. ILI data are captured in a PostgreSQL database via a secure Web interface using OpenSSL 0.9.7b. The secure Web interface is utilized by the OSDH epidemiology staff to send OKAlert messages directly to OKPRN providers, to review and analyze ILI report data, and to provide feedback to participating sites on local and statewide influenza activity. Alternatively, the system is accessible via Palm OS based handheld devices. The OKPRN Health Information Technology team designed a Palm client application in NSBasic for Palm, a Visual Basic like rapid development environment. The personal digital assistants (PDAs) are connected to an intermediate Microsoft SQL server 2000 database supporting our general research data collection system. This database is then ported to the open source PostgeSQL database to capture and store ILI messages synced from the handheld devices. The PDA client includes a basic error-checking algorithm to ensure ILI data integrity. The client is able to track PDA users actions within the ILI system to determine daily handheld usage, options users selected to share health alerts with others, and possible errors users encountered. The PDA and the Web client prompt providers to answer three simple questions each day: (1) the number of patients with ILI symptoms seen that day, (2) the number of patients with ILI symptoms hospitalized that day, and (3) total number of patients seen that day. Information Flow and System Operation The information flow in the OKAlert-ILI System is shown in Figure 1. The OSDH epidemiology team sends a brief text message each week to the central database containing the surveillance summary for influenza and other infectious diseases. The server then pushes out these messages to the OKAlert-ILI Web site and also to handheld devices located in OKPRN practices. When

3 358 Journal of Public Health Management and Practice FIGURE 2. Sample personal digital assistant screen showing influenzalike illness criteria. handheld users sync their PDAs, they receive the last three messages and are prompted to review and disseminate them in their practices. At a time set by the user, the PDA prompts the user to enter and sync ILI reports back to the central server. Consequently, incoming ILI data are populated and stored in the PostgreSQL database. ILI criteria is given according to the Centers for Disease Control and Prevention definition: a fever of 100 o F or above PLUS cough and/or sore throat PLUS myalgia in the absence of a known cause other than influenza (Figure 2). Researchers at the Department of Family Medicine at the University of Oklahoma Health Sciences Center and OSDH epidemiologists continuously monitor the database and generate a report for further analysis, feedback, and dissemination. The OKAlert-ILI s Web site provides various extraction filters to populate ILI data, including clinic/user name and date range of reports. Data extraction and analysis is quick and convenient, because the flat file format allows a wide range of data analysis tools to receive and present ILI data. Analyzed system data are used by the OSDH to enhance its existing influenza surveillance system, which is based on weekly sentinel physician and laboratory reports. Surveillance results are then propagated to OKPRN practices via both OKAlert messages and listserv messages containing additional information, links, Centers for Disease Control and Prevention health alerts, and circulating respiratory virus activity reports Influenza Season Results (October 2003 May 2004) During the influenza season, the OKAlert- ILI System received 15,428 individual patient encounter reports from 30 volunteer OKPRN primary care clinicians located in 15 counties. The distribution of participating clinicians is shown in Figure 3. Six percent of patient encounters (927 cases) resulted in ILI reporting during the entire season. In 20 cases (0.1%), patients required hospitalization. The temporal distribution of ILI sentinel reporting during the peak influenza activity of this season is plotted in Figure 4. ILI reports were submitted daily from Monday through Friday, except for some days during the Christmas holidays. OKPRN sentinel ILI reports correlated with laboratory influenza test results reported to the OSDH. The OKAlert-ILI System contributed significantly to the timely detection of ILI cases and enhanced spatial resolution of the existing syndromic surveillance. Early analysis of the season data indicated ILI cases appearing first in the southern part of the state with a subsequent northward shift. This finding correlated with information from other sources. Timely recognition of this pattern made it possible for the OKAlert-ILI system manager and OSDH epidemiologists to alert providers whose practices had not yet been significantly affected, who were then able to prepare for a substantial increase in the number of acute patient visits. Primary care providers particularly appreciated this information. Figure 4 demonstrates the pattern of ILI activity during the season. Approximately 4 weeks after a significant increase in ILI reports, ILI activity had already peaked. The influenza season started significantly earlier than typical historical patterns. The first culture-confirmed case was reported on October 18, Testing of referred isolates confirmed influenza type A (H3N2) (both Fujian-like and Panamalike). No influenza type B was confirmed in Oklahoma in this season. The OKAlert-ILI System detected an unusually high percentage of patients presenting with ILI symptoms at the peak of the season (average of more than 12% of visits attributable to ILI) and a rapid decline in ILI activity after approximately 4 weeks. A streaming video demonstrating the development and decline of the GIS-mapped ILI activity during the season is available at Screen shots from the video are shown in Figure 5. Timeliness and enhanced granularity were especially demonstrated in one practice location where a nurse practitioner in a residency clinic, who had primarily been seeing pediatric patients, began consistently reporting ILI cases. However, other providers in the same clinic who were seeing mostly adults were not reporting a significant increase in ILI cases in the early stage of the epidemic (first 2 weeks of November 2003). The OSDH contacted the provider via the OKAlert system and inquired about the discrepancy.

4 Bridging the Gap Between Public and Private Healthcare 359 FIGURE 3. Distribution of participating Oklahoma Physicians Resource/Research Network practices in Oklahoma. They were able to determine that the significant reporting difference was due to distinct practice patterns of the providers with regard to seeing separate age groups at the same location. This finding suggested that in the early stage of the epidemic, circulating influenza strains were mostly affecting young children. It also underscored the importance of enrolling separate providers at a larger practice site without pooling their ILI report data Influenza Season Results (October 2004 April 2005) During the season, the OSDH received 33,437 individual patient encounter reports from 31 OKPRN primary care providers located in 15 counties. Three percent of patient encounters met the ILI criteria (1,114 cases) during the entire season, and 19 patients (0.06%) were hospitalized because of ILI. Sentinel reporting data for the season is plotted in Figure 6. Figure 6 demonstrates the distribution of sentinel ILI reporting. In contrast to the season data, the curve is somewhat multimodal and ILI activity peaks in mid-february, much later than during the season. Additional fluctuation before and after the peak is likely attributable to a changing mix of circulating respiratory viruses. Retrospectively, these included adenovirus, parainfluenza virus, influenza types A and B, and respiratory syncytial virus based on sentinel laboratory reports to the OSDH during the season. Timeliness of ILI Reporting Analysis of ILI data demonstrated the timeliness of ILI reporting. The average lag time between seeing and reporting ILI cases to the OSDH by providers was only 1.8 days (approximately 44 hours). Timeliness was due to a rigorous practice of daily reporting, offering multiple ways to report (via the PDA, a personal computer

5 360 Journal of Public Health Management and Practice FIGURE 4. Influenza-like illness sentinel reporting in the Oklahoma Physicians Resource/Research Network ( influenza season). FIGURE 5. Screen shots from the influenza-like illness movie. The size of dots is proportional to the percentage of influenza-like illness cases reported from that location. Each screen shot represents a specific day from the 46th to the 51st week in 2003.

6 Bridging the Gap Between Public and Private Healthcare 361 FIGURE 6. Influenza-like illness sentinel reporting in the Oklahoma Physicians Resource/Research Network ( influenza season). [PC] application, or a Web browser) and instantaneous registering of cases in the central database. Daily reporting of ILI cases took an average of 30 seconds to 1 minute depending on whether the PC or the PDA was used to enter and send data. ILI data were recorded and became available for analysis in the database immediately. The OSDH was able to run surveillance reports with a click of a button. Reports could be easily populated by multiple parameters that included sentinel sites, ILI report time frame, ILI cases seen in the office, hospitalizations, and total number of patients seen by the particular provider from which a ratio of ILI visits was calculated. Reports generated from real-time data could be downloaded into a flat file for further analysis, if it was necessary. Report results could be instantaneously fed back to users via short text messages from the same Web site that all sentinel providers received within an hour. Interestingly, providers could also run some of these reports and see the distribution of cases in the state based on real-time data. This feature enhanced specificity of the information feedback to the area where the provider was located. Sensitivity and Specificity of ILI Reporting There has been a very strong correlation between OKPRN surveillance reports via the OKAlert-ILI System and reports from independent laboratories incorporated into the OSDH conventional influenza surveillance system. We plotted the percentage of culturepositive influenza isolates interlaced with OKPRN sentinel network ILI reports in Figure 7 to demonstrate the sensitivity and specificity of the system. During the season, 75.5 percent of influenza isolates were type A H3N2 (Fujian-like) viruses in Oklahoma. Analysis of the two datasets in Figure 7 showed a 7-day lag time between OKPRN and OSDH reports in FIGURE 7. Percentage of culturepositive influenza isolates from sentinel laboratories by specimen collection date, Oklahoma (Oklahoma State Department of Health data). ILI indicates influenzalike illness.

7 362 Journal of Public Health Management and Practice favor of the sentinel surveillance. When respective data pairs were analyzed in the part of the curve corresponding to the peak season, the Pearson correlation analysis yielded a coefficient of 0.827, indicating a strong correlation between the two datasets representing laboratory and sentinel reports (Statistix for Windows, Analytical Software, Tallahassee, Fla). Acceptability and Simplicity of the OKAlert-ILI System Regular feedback received from OKPRN providers throughout the two influenza seasons indicated a high level of satisfaction with the system. Users found all versions of the system (PDA, PC, and Web-based) easy to use and efficient. The Web-based solution especially required little time and effort. The system was selfexplanatory and no additional tutorial was necessary. Analysis of reporting patterns of the season showed that 85 percent of providers reported consistently (at least three times every 5 days) during the peak season. Only three providers dropped out before or during peak ILI activity. Representativeness, Usefulness, and Importance of the OKAlert-ILI System Participating primary care providers were located in 15 counties representing all regions in Oklahoma, with the exception of the Panhandle of the state. The OSDH received regular ILI reports from both private and public healthcare providers, which included solo practitioners, small (2 5 physician) groups, large practices and medical centers, three residency clinics, two community health centers, and several Native American tribal healthcare providers. The OKAlert-ILI System is the first electronic sentinel ILI surveillance system in Oklahoma. Data generated from the OKAlert-ILI System have proven to be a beneficial complement to the statewide influenza surveillance program in Oklahoma that collates reports from multiple sources (eg, laboratories and metropolitan syndromic surveillance systems). Although it has not yet been tested in a public health emergency, the alert component of the system has various potential applications. For example, in the case of a bioterrorist attack, public health authorities could access the system to rapidly notify and disseminate medical guidance to frontline healthcare providers. The dual-use technology feature was a particularly important incentive for the development of the system. Everyday use ensures that end users, data monitoring and analyzing personnel, authorities, and technical staff are prepared to use the system effectively in emergency situations as well as for everyday purposes. In the case of emergency, prompt text and messages can be sent both from the OSDH and from the Department of Family and Preventive Medicine to 80 providers throughout the state. Immediate feedback via the same channels can be requested, and incoming messages can be accessed and analyzed in a timely manner. Usefulness of OKAlert messages has been evaluated during the two described influenza seasons via frequent personal feedback from users. Providers indicated repeatedly that OKAlert messages helped them provide a more timely and accurate response to ILI cases. Detailed knowledge of circulating virus strains and area-specific influenza activity reports made the selection of appropriate therapy significantly easier while a physician s confidence also increased. A listserv version of a representative OKAlert message is shown in Figure 8. A more concise version of this message was pushed out to PDAs and PCs each week. During the second ( ) influenza season, OKAlert messages significantly improved by focusing more on information that was immediately relevant for practicing clinicians. Completeness and Validation of ILI Report Data The OKAlert-ILI System incorporated client-side and server-side validation algorithms for ILI surveillance data entered by the providers. Validation included warnings or denial of entry for unusual or unrealistic values, incomplete dataset, wrong dates, and mathematical nonsense entries (eg, inputting number for ILI patients seen that exceeds total number of patients or more patients hospitalized with ILI than all patients seen with ILI ). When possible, data entry was assisted with automatic user controls (eg, JavaScript calendars) to ensure correct data format and accelerate data entry. In addition, human review of the raw incoming data was also performed frequently. Less than 0.7 percent of the reports had to be excluded from the final analysis because of confirmed data entry errors, and 0.08 percent of reports were corrected or deleted by providers. These changes were logged in the SQL database. Flexibility of the OKAlert-ILI System Although the OKAlert-ILI System has completed its specific mission, due to technical limitations and the variety of supported communication channels, tracked parameters could not be easily modified or added to the system. Developers are currently working on a more

8 Bridging the Gap Between Public and Private Healthcare 363 FIGURE 8. Representative OKAlert listserv message. ILI indicates influenza-like activity. flexible system that is able to switch to collecting alternative disease surveillance data during the summer. The system was designed to be able to shift from outbreak detection to management via a bidirectional messaging solution that provides multiple channels for communication. Information flow from the OSDH to the providers (feedback) is just as important as submission of ILI data by the providers. The dual-use technology makes the system capable of communicating information rapidly in both directions. System scalability has also been an important feature. The current setup requires no technical alterations to significantly increase the number of providers in the system. Geographical location of providers has not been an issue either, provided that Internet connection is available at participating sites. System Costs Development of the OKAlert-ILI System was funded by a $50,000 OSDH contract, and the system has been implemented in OKPRN, a nonprofit PBRN using opensource standards and technologies. As a result, the system has been made available to participating clinicians at no cost. Additional system upgrade and maintenance costs during the season, including programming, salaries, technical support, and infrastructural costs, have also been covered by the OSDH contract. When PCs were used by practitioners, no additional investment in hardware was necessary. All participants had computers and adequate Internet connection in their offices prior to starting the project. Some clinicians utilized handheld devices as a personal preference. These devices were distributed before the ILI project and were funded by grants targeted at collecting research data and enhancing the quality of care. We were able to use the existing handheld network for the OKAlert-ILI surveillance project. Limitations During the and seasons, ILI data were not classified by patient age groups. Therefore, we have not been able to analyze how different age groups were impacted by influenza. Furthermore, it has been difficult to maintain the same level of sentinel participation year round. Although some clinicians have been reporting baseline ILI activity during the summer, most of them stopped reporting temporarily due to lack of interest (very low or no ILI activity). However, the OKAlert-ILI System has been used to provide information on animal or human West Nile virus surveillance findings and other West Nile virus related topics during summer time. Conclusion The success of the OKAlert-ILI System demonstrates that PBRNs have a significant potential for bridging the communication gap between the public and private healthcare sectors. Their unique position and affiliation with an array of healthcare entities empowers them to develop and implement viable disease surveillance solutions that are accepted and utilized by all parties. They are able to understand and approach all stakeholders and bring together professionals with a variety of expertise to develop and implement a complex public health solution cost effectively.

9 364 Journal of Public Health Management and Practice REFERENCES 1. Mold JW, Peterson KA. Primary care practice-based research networks: working at the interface between research and quality improvement. Ann Fam Med. 2005;3(suppl 1):S12 S Green LA, White LL, Barry HC, Nease DE Jr, Hudson BL. Infrastructure requirements for practice-based research networks. Ann Fam Med. 2005;3(suppl 1):S5 S Nagykaldi Z, Mold JW. Diabetes patient tracker, a personal digital assistant-based diabetes management system for primary care practices in Oklahoma. Diab Technol Ther. 2003;5: Smithee L, Bos J. Influenza season Epidemiol Bull. 2004;36:1.