105-107 Forsyth Street
132G Nightingale Hall
Boston, MA 02115
ATTN: Clark Freifeld, 202 WVH
360 Huntington Avenue
Boston, MA 02115
- Health Data Analytics
- Health Informatics
- PhD in Biomedical Engineering, Boston University
- MS, MIT Media Lab
- BS in Computer Science and Mathematics, Yale University
Professor Clark Freifeld is a lecturer in computer science at Northeastern University. His research focuses on applications of computing technology and artificial intelligence to the improvement of population health. He also serves as an affiliate faculty member at the Innovation and Digital Health Accelerator at Boston Children’s Hospital. Professor Freifeld has co-authored over twenty journal articles and co-founded and overseen a range of health informatics projects. His projects include: HealthMap, a global disease surveillance platform; MedWatcher, a medical product safety monitoring system; and StreetRx, a crowdsourcing tool for understanding black market pharmaceutical transactions.
Professor Freifeld’s work has been used by millions of people and supported by public health agencies including CDC, WHO, DHS, DOD, HHS, and FDA, as well as being recognized by the Smithsonian and Library of Congress. Before joining the faculty at Northeastern, Freifeld was co-founder and chief technology officer of Epidemico, a health informatics spinout from Boston Children’s and MIT, now a division of Booz Allen Hamilton. He holds a Bachelor of Science in Computer Science and Mathematics from Yale University, a master’s from the MIT Media Lab, and a PhD in Biomedical Engineering from Boston University.
Field of research/teaching
Health Informatics / Computer Science
What are the specifics of your educational background?
My educational background has been interdisciplinary throughout my career: my undergraduate major was in computer science and mathematics combined; I earned my master’s at the MIT Media Lab in a group focused on applications of technology for health and wellness; and my PhD in biomedical engineering synthesized elements of computer science and epidemiology.
What is your research focus in more detail? Is your current research path what you always had in mind for yourself or has it evolved?
My research applies information technology and computer science techniques to problems in health, epidemiology, and wellness. As a child, I had set my sights on becoming a doctor, but as I started to understand the distinction between health and medicine, the population-based, preventative approach to health just made more sense to me and attracted my interest. Meanwhile, I started programming and caught the “bug” for computer science. After I graduated from college, I worked as a software developer, first in mobile, and then in finance. I had no idea there could be a career in health informatics until I stumbled into a job as part of a research group at Boston Children’s Hospital.
Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts
Pierce CE, Bouri K, Pamer C, Proestel S, Rodriguez HW, Van Le H, Freifeld CC, Brownstein JS, Walderhaug M, Edwards IR, Dasgupta N. Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts. Drug Saf. 2017 Apr;40(4):317-331. doi: 10.1007/s40264-016-0491-0. PubMed PMID: 28044249; PubMed Central PMCID: PMC5362648
The rapid expansion of the Internet and computing power in recent years has opened up the possibility of using social media for pharmacovigilance. While this general concept has been proposed by many, central questions remain as to whether social media can provide earlier warnings for rare and serious events than traditional signal detection from spontaneous report data.
Our objective was to examine whether specific product-adverse event pairs were reported via social media before being reported to the US FDA Adverse Event Reporting System (FAERS).
A retrospective analysis of public Facebook and Twitter data was conducted for 10 recent FDA postmarketing safety signals at the drug-event pair level with six negative controls. Social media data corresponding to two years prior to signal detection of each product-event pair were compiled. Automated classifiers were used to identify each ‘post with resemblance to an adverse event’ (Proto-AE), among English language posts. A custom dictionary was used to translate Internet vernacular into Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Terms. Drug safety physicians conducted a manual review to determine causality using World Health Organization-Uppsala Monitoring Centre (WHO-UMC) assessment criteria. Cases were also compared with those reported in FAERS.
A total of 935,246 posts were harvested from Facebook and Twitter, from March 2009 through October 2014. The automated classifier identified 98,252 Proto-AEs. Of these, 13 posts were selected for causality assessment of product-event pairs. Clinical assessment revealed that posts had sufficient information to warrant further investigation for two possible product-event associations: dronedarone-vasculitis and Banana Boat Sunscreen–skin burns. No product-event associations were found among the negative controls. In one of the positive cases, the first report occurred in social media prior to signal detection from FAERS, whereas the other case occurred first in FAERS.
An efficient semi-automated approach to social media monitoring may provide earlier insights into certain adverse events. More work is needed to elaborate additional uses for social media data in pharmacovigilance and to determine how they can be applied by regulatory agencies.
Dart RC, Surratt HL, Le Lait MC, Stivers Y, Bebarta VS, Freifeld CC, Brownstein JS, Burke JJ, Kurtz SP, Dasgupta N. Diversion and Illicit Sale of Extended Release Tapentadol in the United States. Pain Med. 2016 Aug;17(8):1490-6. doi: 10.1093/pm/pnv032. Epub 2015 Dec 14. PubMed PMID: 26814267; PubMed Central PMCID: PMC4975014
Prescription opioid analgesics are commonly prescribed for moderate to severe pain. An unintended consequence of prescribing opioid analgesics is the abuse and diversion of these medications. Tapentadol ER is a recently approved centrally acting analgesic with synergistic mechanisms of action: μ-opioid receptor agonism and inhibition of norepinephrine reuptake. We assessed the amount of diversion and related cost of obtaining tapentadol IR (Nucynta®) and tapentadol ER (Nucynta ER®) as well as other Schedule II opioid medications in street transactions in the United States using the Researched Abuse, Diversion and Addiction-Related Surveillance (RADARS®) System.
The Drug Diversion Program measures the number of cases opened by 260 drug diversion investigators in 49 states. StreetRx(TM) uses a crowd-sourcing Website to collect the prices paid for licit or illicit drugs.
The population-based rates of diversion were 0.003 (tapentadol IR), 0.001 (tapentadol ER), and 1.495 (other Schedule II opioid tablets) reports per 100,000 population. The tapentadol ER rate was lower than the other Schedule II opioid tablets (P < 0.001) and tapentadol IR (P= 0.004). Diversion rates based on drug availability were 0.03 (tapentadol IR), 0.016 (tapentadol ER), and 0.172 (other Schedule II opioid tablets) per 1,000 prescriptions dispensed. The median street price per milligram was $0.18 (tapentadol IR), $0.10 (tapentadol ER), and $1.00 (other Schedule II opioid tablets).
Our results indicate that tapentadol ER is rarely sold illicitly in the United States. When sold illicitly, tapentadol ER costs less than other Schedule II opioid products.
: Powell GE, Seifert HA, Reblin T, Burstein PJ, Blowers J, Menius JA, Painter JL, Thomas M, Pierce CE, Rodriguez HW, Brownstein JS, Freifeld CC, Bell HG, Dasgupta N. Social Media Listening for Routine Post-Marketing Safety Surveillance. Drug Saf. 2016 May;39(5):443-54. doi: 10.1007/s40264-015-0385-6. PubMed PMID: 26798054
Increasing Patient Engagement in Pharmacovigilance Through Online Community Outreach and Mobile Reporting Applications: An Analysis of Adverse Event Reporting for the Essure Device in the US
Bahk CY, Goshgarian M, Donahue K, Freifeld CC, Menone CM, Pierce CE, Rodriguez H, Brownstein JS, Furberg R, Dasgupta N. Increasing Patient Engagement in Pharmacovigilance Through Online Community Outreach and Mobile Reporting Applications: An Analysis of Adverse Event Reporting for the Essure Device in the US. Pharmaceut Med. 2015;29(6):331-340. Epub 2015 Aug 5. PubMed PMID: 26635479; PubMed Central PMCID: PMC4656696
Preparing and submitting a voluntary adverse event (AE) report to the US Food and Drug Administration (FDA) for a medical device typically takes 40 min. User-friendly Web and mobile reporting apps may increase efficiency. Further, coupled with strategies for direct patient involvement, patient engagement in AE reporting may be improved. In 2012, the FDA Center for Devices and Radiologic Health (CDRH) launched a free, public mobile AE reporting app, MedWatcher, for patients and clinicians. During the same year, a patient community on Facebook adopted the app to submit reports involving a hysteroscopic sterilization device, brand name Essure®.
Patient community outreach was conducted to administrators of the group “Essure Problems” (approximately 18,000 members as of June 2015) to gather individual case safety reports (ICSRs). After agreeing on key reporting principles, group administrators encouraged members to report via the app. Semi-structured forms in the app mirrored fields of the MedWatch 3500 form. ICSRs were transmitted to CDRH via an electronic gateway, and anonymized versions were posted in the app. Data collected from May 11, 2013 to December 7, 2014 were analyzed. Narrative texts were coded by trained and certified MedDRA coders (version 17). Descriptive statistics and metrics, including VigiGrade completeness scores, were analyzed. Various incentives and motivations to report in the Facebook group were observed.
The average Essure AE report took 11.4 min (±10) to complete. Submissions from 1349 women, average age 34 years, were analyzed. Serious events, including hospitalization, disability, and permanent damage after implantation, were reported by 1047 women (77.6 %). A total of 13,135 product–event pairs were reported, comprising 327 unique preferred terms, most frequently fatigue (n = 491), back pain (468), and pelvic pain (459). Important medical events (IMEs), most frequently mental impairment (142), device dislocation (108), and salpingectomy (62), were reported by 598 women (44.3 %). Other events of interest included loss of libido (n = 115); allergy to metals (109), primarily nickel; and alopecia (252). VigiGrade completeness scores were high, averaging 0.80 (±0.15). Reports received via the mobile app were considered “well documented” 55.9 % of the time, compared with an international average of 13 % for all medical products. On average, there were 15 times more reports submitted per month via the app with patient community support versus traditional pharmacovigilance portals.
Outreach via an online patient community, coupled with an easy-to-use app, allowed for rapid and detailed ICSRs to be submitted, with gains in efficiency. Two-way communication and public posting of narratives led to successful engagement within a Motivation-Incentive-Activation-Behavior framework, a conceptual model for successful crowdsourcing. Reports submitted by patients were considerably more complete than those submitted by physicians in routine spontaneous reports. Further research is needed to understand how biases operate differently from those of traditional pharmacovigilance.
Surveillance for Neisseria meningitidis Disease Activity and Transmission Using Information Technology
Ahmed SS, Oviedo-Orta E, Mekaru SR, Freifeld CC, Tougas G, Brownstein JS. Surveillance for Neisseria meningitidis Disease Activity and Transmission Using Information Technology. PLoS One. 2015 May 20;10(5):e0127406. doi: 10.1371/journal.pone.0127406. eCollection 2015. PubMed PMID: 25992552; PubMed Central PMCID: PMC4439021.
While formal reporting, surveillance, and response structures remain essential to protecting public health, a new generation of freely accessible, online, and real-time informatics tools for disease tracking are expanding the ability to raise earlier public awareness of emerging disease threats. The rationale for this study is to test the hypothesis that the HealthMap informatics tools can complement epidemiological data captured by traditional surveillance monitoring systems for meningitis due to Neisseria meningitides (N. meningitides) by highlighting severe transmissible disease activity and outbreaks in the United States.
Comparing timeliness, content, and disease severity of formal and informal source outbreak reporting
Bahk CY, Scales DA, Mekaru SR, Brownstein JS, Freifeld CC. Comparing timeliness, content, and disease severity of formal and informal source outbreak reporting. BMC Infect Dis. 2015 Mar 20;15:135. doi: 10.1186/s12879-015-0885-0. PubMed PMID: 25887692; PubMed Central PMCID: PMC4369067
Infectious disease surveillance has recently seen many changes including rapid growth of informal surveillance, acting both as competitor and a facilitator to traditional surveillance, as well as the implementation of the revised International Health Regulations. The present study aims to compare outbreak reporting by formal and informal sources given such changes in the field.
111 outbreaks identified from June to December 2012 were studied using first formal source report and first informal source report collected by HealthMap, an automated and curated aggregator of data sources for infectious disease surveillance. The outbreak reports were compared for timeliness, reported content, and disease severity.
Formal source reports lagged behind informal source reports by a median of 1.26 days (p = 0.002). In 61% of the outbreaks studied, the same information was reported in the initial formal and informal reports. Disease severity had no significant effect on timeliness of reporting.
The findings suggest that recent changes in the field of surveillance improved formal source reporting, particularly in the dimension of timeliness. Still, informal sources were found to report slightly faster and with accurate information. This study emphasizes the importance of utilizing both formal and informal sources for timely and accurate infectious disease outbreak surveillance.
A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives. J Med Internet Res
Nagar R, Yuan Q, Freifeld CC, Santillana M, Nojima A, Chunara R, Brownstein JS. A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives. J Med Internet Res. 2014 Oct 20;16(10):e236. doi: 10.2196/jmir.3416. PubMed PMID: 25331122; PubMed Central PMCID: PMC4259880
Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter’s relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches.
The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases.
From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords “flu”, “influenza”, “gripe”, and “high fever”. The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis.
Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay’s Center and the Atlantic Avenue Terminal.
While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter’s strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable.
Freifeld CC, Brownstein JS, Menone CM, Bao W, Filice R, Kass-Hout T, Dasgupta N. Digital drug safety surveillance: monitoring pharmaceutical products in twitter. Drug Saf. 2014 May;37(5):343-50. doi: 10.1007/s40264-014-0155-x. Erratum in: Drug Saf. 2014 Jul;37(7):555.
Traditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients, relying instead on gatekeepers, such as clinicians and drug safety groups, to verify each potential event. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines.
The aim of the study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency.
We collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA®). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC).
Of the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 72 % recall and 86 % precision. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 (p < 0.0001) between Proto-AEs reported in Twitter and FAERS by SOC.
Patients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation.
Freifeld CC, Chunara R, Mekaru SR, Chan EH, Kass-Hout T, Ayala Iacucci A, Brownstein JS. Participatory epidemiology: use of mobile phones for community-based health reporting. PLoS Med. 2010 Dec 7;7(12):e1000376.
Traditional health systems serve a key role in protecting populations, but are typically hierarchical, and information often travels slowly.
Novel Internet-based collaborative systems can have an important role in gathering information quickly and improving coverage and accessibility.
Mobile Internet usage is growing rapidly worldwide, making real-time information tools more readily available to both clinicians and the general public.
We present a brief summary of some promising mobile applications for health monitoring and information sharing, together with preliminary results from a study of our deployment of a smartphone application which enabled the general public to report infectious disease events.
These early efforts at tapping the power of mobile software tools illustrate potentially important steps in improving health systems as well as engaging the public as participants in the public health process.
Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project
Brownstein JS, Freifeld CC, Reis BY, Mandl KD. Surveillance Sans Frontières: Internet-based emerging infectious disease intelligence and the HealthMap project. PLoS Med. 2008 Jul 8;5(7):e151.
Valuable information about infectious diseases is found in Web-accessible information sources such as discussion forums, mailing lists, government Web sites, and news outlets.
Web-based electronic information sources can play an important role in early event detection and support situational awareness by providing current, highly local information about outbreaks, even from areas relatively invisible to traditional global public health efforts.
While these sources are potentially useful, information overload and difficulties in distinguishing “signal from noise” pose substantial barriers to fully utilizing this information.
HealthMap is a freely accessible, automated real-time system that monitors, organizes, integrates, filters, visualizes, and disseminates online information about emerging diseases.
The goal of HealthMap is to deliver real-time intelligence on a broad range of emerging infectious diseases for a diverse audience, from public health officials to international travelers.
Ultimately, the use of news media and other nontraditional sources of surveillance data can facilitate early outbreak detection, increase public awareness of disease outbreaks prior to their formal recognition, and provide an integrated and contextualized view of global health information.
HealthMap: Global Infectious Disease Monitoring through Automated Classification and Visualization of Internet Media Reports
Freifeld CC, Mandl KD, Reis BY, Brownstein JS. HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J Am Med Inform Assoc. 2008 Mar-Apr;15(2):150
Unstructured electronic information sources, such as news reports, are proving to be valuable inputs for public health surveillance. However, staying abreast of current disease outbreaks requires scouring a continually growing number of disparate news sources and alert services, resulting in information overload. Our objective is to address this challenge through the HealthMap.org Web application, an automated system for querying, filtering, integrating and visualizing unstructured reports on disease outbreaks.
This report describes the design principles, software architecture and implementation of HealthMap and discusses key challenges and future plans.
We describe the process by which HealthMap collects and integrates outbreak data from a variety of sources, including news media (e.g., Google News), expert-curated accounts (e.g., ProMED Mail), and validated official alerts. Through the use of text processing algorithms, the system classifies alerts by location and disease and then overlays them on an interactive geographic map. We measure the accuracy of the classification algorithms based on the level of human curation necessary to correct misclassifications, and examine geographic coverage.
As part of the evaluation of the system, we analyzed 778 reports with HealthMap, representing 87 disease categories and 89 countries. The automated classifier performed with 84% accuracy, demonstrating significant usefulness in managing the large volume of information processed by the system. Accuracy for ProMED alerts is 91% compared to Google News reports at 81%, as ProMED messages follow a more regular structure.
HealthMap is a useful free and open resource employing text-processing algorithms to identify important disease outbreak information through a user-friendly interface.