Innovative uses of electronic health records and social media for public health surveillance

Current Diabetes Reports

Volumn 14, Issue 3, 2014, Pages

  Eggleston, Emma M ^a,b   Weitzman, Elissa R ^a,c  

^a HARVARD MEDICAL SCHOOL   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c BOSTON CHILDREN S HOSPITAL   (United States)

Author keywords

Diabetes; Electronic health record; Innovative uses; Public health; Social media; Social networking; Surveillance

Indexed keywords

ALGORITHM; ARTICLE; BODY MASS; CLINICAL PRACTICE; CODING; COMPARATIVE EFFECTIVENESS; COMPUTER SECURITY; CURRENT PROCEDURAL TERMINOLOGY; DIABETES MELLITUS; DISEASE COURSE; ELECTRONIC MEDICAL RECORD; EMPLOYMENT STATUS; HEALTH CARE DELIVERY; HEALTH CARE SYSTEM; HEALTH INSURANCE; HEALTH SURVEY; HUMAN; INFORMATION TECHNOLOGY; LABORATORY TEST; MEDICAL INFORMATION SYSTEM; ONLINE SYSTEM; PATIENT MONITORING; PATIENT REFERRAL; PRESCRIPTION; PRIVACY; RACE; SELF CONCEPT; SOCIAL MEDIA; SOCIAL NETWORK; SOCIAL STIGMA; INVENTION; REVIEW; SOCIAL SUPPORT; STATISTICS;

DIABETES MELLITUS; ELECTRONIC HEALTH RECORDS; HUMANS; INVENTIONS; PUBLIC HEALTH SURVEILLANCE; SOCIAL MEDIA; SOCIAL SUPPORT;

EID: 84893205909     PISSN: 15344827     EISSN: 15390829     Source Type: Journal    
DOI: 10.1007/s11892-013-0468-7     Document Type: Article

References (95)

1. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329:977-86. doi: 10.1056/NEJM199309303291401. (Pubitemid 23307905)
2. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998;352:837-53.
3. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346:393-403. doi: 10.1056/NEJMoa012512. (Pubitemid 34437694)
4. Ratner RE, Christophi CA, Metzger BE, Dabelea D, Bennett PH, Pi-Sunyer X, et al. Prevention of diabetes in women with a history of gestational diabetes: effects of metformin and lifestyle interventions. J Clin Endocrinol Metabol. 2008;93:4774-9. doi: 10.1210/jc.2008-0772.
5. Retnakaran R, Qi Y, Sermer M, Connelly PW, Zinman B, Hanley AJG. Isolated hyperglycemia at 1 hour on oral glucose tolerance test in pregnancy resembles gestational diabetes mellitus in predicting postpartum metabolic dysfunction. Diabetes Care. 2008;31:1275-81. doi: 10.2337/dc08-0126.
6. Dabelea D, Pihoker C, Talton JW, D'Agostino Jr RB, Fujimoto W, Klingensmith GJ, et al. Etiological approach to characterization of diabetes type: the SEARCH for Diabetes in Youth Study. Diabetes Care. 2011;34:1628-33. doi: 10.2337/dc10-2324.
7. Klein R, Klein BE, Moss SE, Davis MD, DeMets DL. The Wisconsin epidemiologic study of diabetic retinopathy. III. Prevalence and risk of diabetic retinopathy when age at diagnosis is 30 or more years. Arch Opthalmol. 1984;102:527-32. (Pubitemid 14127839)
8. Institute of Medicine. Living well with chronic illness: a call for public health action. Consensus Report; National Academies Press; 2012.
9. Selby JV. The patient-centered outcomes research institute: a 2013 agenda for research done differently. Popul Health Manag. 2013;16:69-70. doi: 10.1089/pop.2013.1621.
10. Anastasio S, Tabbot P. Independent study rough draft: 1 December 2012. The Public Health Significance of Implementing Electronic Health Records in New Jersey. The Public. 2012.
11. Bailey LC, Milov DE, Kelleher K, Kahn MG, Del Beccaro M, Yu F, et al. Multi-institutional sharing of electronic health record data to assess childhood obesity. PloS One. 2013;8:e66192.
12. Klompas M, Murphy M, Lankiewicz J, McVetta J, Lazarus R, Eggleston E, et al. Harnessing electronic health records for public health surveillance. J Public Health, Inform (online). 2011;3.
13. Klompas M, Haney G, Church D, Lazarus R, Hou X, Platt R. Automated identification of acute hepatitis B using electronic medical record data to facilitate public health surveillance. PLoS One. 2008;3:e2626. doi: 10.1371/journal.pone.0002626.
14. Bradley CA, Rolka H, Walker D, Loonsk J. BioSense: implementation of a National Early Event Detection and Situational Awareness System. MMWR MMWR. 2005;54(Suppl):11-9.
15. Baker DW, Persell SD, Thompson JA, Soman NS, Burgner KM, Liss D, et al. Automated review of electronic health records to assess quality of care for outpatients with heart failure. Ann Intern Med. 2007;146:270-7. (Pubitemid 351650613)
16. Klompas M, McVetta J, Lazarus R, Eggleston E, Haney G, Kruskal BA, et al. Integrating clinical practice and public health surveillance using electronic medical record systems. Am J Prev Med. 2012;42:S154-62. Review of EHR use for public health surveillance and detailed description of the Electronic Medical Recor Support for Public Health (ESP) project.
17. Ahmad FS, Tsang T. Diabetes prevention, health information technology, and meaningful use: challenges and opportunities. Am J Prev Med. 2013;44:S357-63.
18. Friedman DJ, Parrish RG, Ross DA. Electronic health records and US public health: current realities and future promise. Am J Pub Health. 2013:e1-8. Comprehensive reivew of the potential of, and barriers to, the use of EHR data for public health surveillance and proposed steps to ensure that the use of EHR data to support public health reaches its full potential.
19. Adler-Milstein J, Jha AK. Sharing clinical data electronically a critical challenge for fixing the health care system. JAMA. 2012;307:1695-6.
20. Blumenthal D, Tavenner M. The meaningful use regulation for electronic health records. N Engl J Med. 2010;363:501-4. doi: 10.1056/NEJMp1006114.
21. Health IT Policy Committee. Meaningful use workgroup request for comments regarding meaningful use stage 2. In: Office of the National Coordinator for Health Information Technology, editor. Washington, DC; 2011.
22. CDC. Meaningful Use Fact Sheet: Syndromic Surveillance. Public Health Information Network. 2012. http://www.cdc.gov/phin/library/PHIN-Fact-Sheets/FS- MU-SS.pdf.
23. Klompas M, McVetta J, Eggleston E, et al. Automated surveillance and public health reporting for gestational diabetes incidence and care using electronic health record data (Abstract). J Emerg Health Threats. 2011;4:S34.
24. Klompas M, Eggleston E, McVetta J, Lazarus R, Li L, Platt R. Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data. Diabetes Care. 2013;36:914-21. Validation of public health diabetes surveillance algorithm to ascertain and distinguish type 1 from type 2 diabetes in the EHR.
25. Kudyakov R, Bowen J, Ewen E, West SL, Daoud Y, Fleming N, et al. Electronic health record use to classify patients with newly diagnosed vs preexisting type 2 diabetes: infrastructure for comparative effectiveness research and population health management. Popul Health Manage. 2012;15:3-11.
26. Rathmann W, Kowall B, Heier M, Herder C, Holle R, Thorand B, et al. Prediction models for incident type 2 diabetes mellitusin the older population: KORA S4/F4 cohort study. Diabet Med. 2010;27:1116-23. doi: 10.1111/j.1464-5491. 2010.03065.x.
27. Makam AN, Nguyen OK, Moore B, Ma Y, Amarasingham R. Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm. BMC Med Inform Decis Mak. 2013;13:1-7.
28. Benin AL, Fenick A, Herrin J, Vitkauskas G, Chen J, Brandt C. How good are the data? Feasible approach to validation of metrics of quality derived from an outpatient electronic health record. Am J Med Qual. 2011;26:441-51. doi: 10.1177/1062860611403136.
29. Botsis T, Hartvigsen G, Chen F, Weng C. Secondary use of EHR: data quality issues and informatics opportunities. AMIA Summits Transl Sci Proc AMIA Summit Transl Sci. 2010;2010:1-5.
30. Chan KS, Fowles JB, Weiner JP. Review: electronic health records and the reliability and validity of quality measures: a review of the literature. Med Care Res Rev. 2010;67:503-27. doi: 10.1177/1077558709359007.
31. Chiolero A, Santschi V, Paccaud F. Public health surveillance with electronic medical records: at risk of surveillance bias and overdiagnosis. Eur J Pub Health. 2013;23:350-1. doi: 10.1093/eurpub/ckt044.
32. Hoffman S, Podgurski A. Big bad data: law, public health, and biomedical databases. J Law Med Ethics. 2013;41(Suppl):56-60.
33. Fischer LR, Rush WA, Kluznik JC, O'Connor PJ, Hanson AM. Abstract C-C1-06: identifying depression among diabetes patients using natural language processing of office notes. Clin Med Res. 2008;6:125-6.
34. Hazlehurst B, Sittig DF, Stevens VJ, Smith KS, Hollis JF, Vogt TM, et al. Natural language processing in the electronic medical record: assessing clinician adherence to tobacco treatment guidelines. Am J Prev Med. 2005;29:434-9. doi: 10.1016/j.amepre.2005.08.007. (Pubitemid 41817395)
35. Rosenbloom ST, Denny JC, Xu H, Lorenzi N, Stead WW, Johnson KB. Data from clinical notes: a perspective on the tension between structure and flexible documentation. JAMIA. 2011;18:181-6. doi: 10.1136/jamia.2010.007237.
36. Kukafka R, Ancker JS, Chan C, Chelico J, Khan S, Mortoti S, et al. Redesigning electronic health record systems to support public health. J Biomed Inform. 2007;40:398-409. doi: 10.1016/j.jbi.2007.07.001. (Pubitemid 47193196)
37. Brown JS, Holmes JH, Shah K, Hall K, Lazarus R, Platt R. Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care. Med Care. 2010;48(6 Suppl):S45-51. doi: 10.1097/MLR.0b013e3181d9919f.
38. Maro JC, Platt R, Holmes JH, Strom BL, Hennessy S, Lazarus R, et al. Design of a national distributed health data network. Ann Intern Med. 2009;151:341-4.
39. Rea S, Pathak J, Savova G, Oniki TA, Westberg L, Beebe CE, et al. Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project. J Biomed Inform. 2012;45:763-71. doi: 10.1016/j.jbi.2012.01.009.
40. Lenert L, Sundwall DN. Public health surveillance and meaningful use regulations: a crisis of opportunity. J Inform. 2012;102:e1-7.
41. Cohen RM, Lindsell CJ. When the blood glucose and the HbA1c don't match: turning uncertainty into opportunity. Diabetes Care. 2012;35:2421-3. doi: 10.2337/dc12-1479.
42. Mann DM, Carson AP, Shimbo D, Fonseca V, Fox CS, Muntner P. Impact of A1C screening criterion on the diagnosis of pre-diabetes among U.S. adults. Diabetes Care. 2010;33:2190-5.
43. Picón MJ, Murri M, Muñoz A, Fernández-García JC, Gomez-Huelgas R, Tinahones FJ. Hemoglobin A1c versus oral glucose tolerance test in postpartum diabetes screening. Diabetes Care. 2012;35:1648-53. doi: 10.2337/dc11-2111.
44. Selvin E, Steffes MW, Gregg E, Brancati FL, Coresh J. Performance of A1C for the classification and prediction of diabetes. Diabetes Care. 2011;34:84-9.
45. Balion CM, Raina PS, Gerstein HC, Santaguida PL, Morrison KM, Booker L, et al. Reproducibility of impaired glucose tolerance (IGT) and impaired fasting glucose (IFG) classification: a systematic review. Clin Chem Lab Med. 2007;45:1180-5. doi: 10.1515/CCLM.2007.505. (Pubitemid 47396262)
46. Saukkonen T, Cederberg H, Jokelainen J, Laakso M, Härkönen P, Keinänen-Kiukaanniemi S, et al. Limited overlap between intermediate hyperglycemia as defined by A1C 5.7-6.4 %, impaired fasting glucose, and impaired glucose tolerance. Diabetes Care. 2011;34:2314-6.
47. Christensen DL, Witte DR, Kaduka L, Jørgensen ME, Borch-Johnsen K, Mohan V, et al. Moving to an A1C-based diagnosis of diabetes has a different impact on prevalence in different ethnic groups. Diabetes Care. 2010;33:580-2. doi: 10.2337/dc09-1843.
48. Herman WH, Ma Y, Uwaifo G, Haffner S, Kahn SE, Horton ES, et al. Differences in A1C by race and ethnicity among patients with impaired glucose tolerance in the Diabetes Prevention Program. Diabetes Care. 2007;30:2453-7. doi: 10.2337/dc06-2003. (Pubitemid 47547771)
49. Herranz L, Saez-de-Ibarra L, Grande C, Pallardo LF. Non-glycemic- dependent reduction of late pregnancy A1C levels in women with type 1 diabetes. Diabetes Care. 2007;30:1579-80. doi: 10.2337/dc06-2568. (Pubitemid 46871175)
50. Centers for Disease Control and Prevention. HIPAA Privacy Rule and Public Health: Guidance from CDC and the US Department of Health and Human Services. MMWR 2003;1-20. Report on the application and interpretation of HIPAA privacy rules for the use of clinical data in public health surveillance.
51. Madden M, Zickuhr K. 65 % of online adults use social networking sites. Washington: Pew Research Center; 2011.
52. Chou WY, Hunt YM, Beckjord EB, Moser RP, Hesse BW. Social media use in the United States: implications for health communication. J Med Internet Res. 2009;11:e48.
53. Fox S, Purcell K. Chronic disease and the internet. Washington: Pew Research Center; 2010.
54. De la Torre-Diez I, Diaz-Pernas FJ, Anton-Rodriguez M. A content analysis of chronic diseases social groups on Facebook and Twitter. Telemed J E Health. 2012;18:404-8. doi: 10.1089/tmj.2011.0227.
55. Weitzman ER, Cole E, Kaci L, Mandl KD. Social but safe? Quality and safety of diabetes-related online social networks. JAMIA. 2011;18:292-7. doi: 10.1136/jamia.2010.009712. Multi-site evaluation of the quality and safety of the 10 most frequently referenced online diabetes social networking sites. A structured measurement protocol was used to rate sites along 28 indicators of quality and safety across 4 domains.
56. Frost JH, Massagli MP. Social uses of personal health information within PatientsLikeMe, an online patient community: what can happen when patients have access to one another's data. J Med Internet Res. 2008;10:e15.
57. Seeman N. Web 2.0 and chronic illness: new horizons, new opportunities. Healthcare Q. 2008;11:104-8. 10, 4.
58. Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive Health Communication Applications for people with chronic disease. Cochrane Database Syst Rev. 2005:CD004274. doi: 10.1002/14651858.CD004274.pub4.
59. Yang HC, Thornton LM, Shapiro CL, Andersen BL. Surviving recurrence: psychological and quality-of-life recovery. Cancer. 2008;112:1178-87. doi: 10.1002/cncr.23272. (Pubitemid 351304605)
60. Bakitas M, Lyons KD, Hegel MT, Balan S, Brokaw FC, Seville J, et al. Effects of a palliative care intervention on clinical outcomes in patients with advanced cancer: the Project ENABLE II randomized controlled trial. JAMA. 2009;302:741-9. doi: 10.1001/jama.2009.1198.
61. Greene JA, Choudhry NK, Kilabuk E, Shrank WH. Online social networking by patients with diabetes: a qualitative evaluation of communication with Facebook. J Gen Intern Med. 2010;26(3):287-92.
62. Kemp V. Use of 'chronic disease self-management strategies' in mental healthcare. Curr Opin Psychiatry. 2011;24:144-8. doi: 10.1097/YCO. 0b013e3283438014.
63. Kuehn BM. Patients go online seeking support, practical advice on health conditions. JAMA. 2011;305:1644-5. doi: 10.1001/jama.2011.509.
64. Greene JA, Choudhry NK, Kilabuk E, Shrank WH. Online social networking by patients with diabetes: a qualitative evaluation of communication with Facebook. J Gen Intern Med. 2011;26:287-92. doi: 10.1007/s11606-010-1526-3.
65. Hughes SL, Weaver FM, Giobbie-Hurder A, Manheim L, Henderson W, Kubal JD, et al. Effectiveness of team-managed home-based primary care: a randomized multi-center trial. JAMA. 2000;284:2877-85.
66. Bousquet J, Bousquet PJ, Godard P, Daures JP. The public health implications of asthma. Bull World Health Organ. 2005;83:548-54. (Pubitemid 41003331)
67. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet. 2006;367:1747-57. doi: 10.1016/S0140-6736(06) 68770-9. (Pubitemid 43767064)
68. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3:e442. doi: 10.1371/journal.pmed. 0030442. (Pubitemid 44875937)
69. Bodenheimer T, Chen E, Bennett HD. Confronting the growing burden of chronic disease: can the U.S. health care workforce do the job? Health Aff. 2009;28:64-74.
70. Glassman A, Gaziano TA, Bouillon Buendia CP, Guanais de Aguiar FC. Confronting the chronic disease burden in Latin America and the Caribbean. Health Aff. 2010;29:2142-8. doi: 10.1377/hlthaff.2010.1038.
71. Kohane IS, Altman RB. Health-information altruists - a potentially critical resource. N Engl J Med. 2005;353:2074-7. (Pubitemid 41609103)
72. Hamm MP, Chisholm A, Shulhan J, Milne A, Scott SD, Given LM, et al. Social media use among patients and caregivers: a scoping review. BMJ Open. 2013;3. doi: 10.1136/bmjopen-2013-002819.
73. Bond CS, Ahmed OH, Hind M, Thomas B, Hewitt-Taylor J. The conceptual and practical ethical dilemmas of using health discussion board posts as research data. J Med Internet Res. 2013;15:e112. doi: 10.2196/jmir.2435.
74. Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PloS One. 2011;6:e19467.
75. Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PloS One. 2010;5:e14118. doi: 10.1371/journal.pone.0014118.
76. Cassa CA, Chunara R, Mandl K, Brownstein JS. Twitter as a sentinel in emergency situations: lessons from the Boston marathon explosions. PLoS Curr. 2013;5. doi: 10.1371/currents.dis.ad70cd1c8bc585e9470046cde334ee4b.
77. Chunara R, Bouton L, Ayers JW, Brownstein JS. Assessing the online social environment for surveillance of obesity prevalence. PLoS One. 2013;8:e61373. doi: 10.1371/journal.pone.0061373. This cross-sectional observational study among Facebook users examined the relationship between Facebook use and specific interests posted and prevalence of obesity and overweight in US as measured by the Behavioral Risk Factor Surveillance System (BRFSS) and across New York City neighborhoods as measured by NYC EpiQuery.
78. Hand E. Citizen science: people power. Nature. 2010;466:685-7.
79. van Noort SP, Muehlen M, Rebelo de Andrade H, Koppeschaar C, Lima Lourenco JM, Gomes MG. Gripenet: an internet-based system to monitor influenza-like illness uniformly across Europe. Eur Surveill. 2007;12:E5-6.
80. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection - harnessing the Web for public health surveillance. N Engl J Med. 2009;360:2153-5. 7.
81. Weitzman ER, Kelemen S, Mandl KD. Surveillance of an online social network to assess population-level diabetes health status and healthcare quality. J Pub Health, Inform (Online). 2011;3.
82. Weitzman ER, Kaci L, Mandl KD. Sharing medical data for health research: the early personal health record experience. J Med Internet Res. 2010;12:e14.
83. Zulman DM, Nazi KM, Turvey CL, Wagner TH, Woods SS, An LC. Patient interest in sharing personal health record information: a web-based survey. Ann Intern Med. 2011;155:805-10. doi: 10.1059/0003-4819-155-12-201112200-00002.
84. Kaye J, Curren L, Anderson N, Edwards K, Fullerton SM, Kanellopoulou N, et al. From patients to partners: participant-centric initiatives in biomedical research. Nat Rev Genet. 2012;13:371-6. doi: 10.1038/nrg3218.
85. Frost J, Okun S, Vaughan T, Heywood J, Wicks P. Patient-reported outcomes as a source of evidence in off-label prescribing: analysis of data from PatientsLikeMe. J Med Internet Res. 2011;13:e6.
86. Weitzman ER, Adida B, Kelemen S, Mandl KD. Sharing data for public health research by members of an international online diabetes social network. PloS One. 2011;6:e19256. doi: 10.1371/journal.pone.0019256.
87. Swan M. Crowd sourced health research studies: an important emerging complement to clinical trials in the public health research ecosystem. J Med Internet Res. 2012;14:e46. doi: 10.2196/jmir.1988.
88. Adida B, Sanyal A, Zabak S, Kohane IS, Mandl KD. Indivo x: developing a fully substitutable personally controlled health record platform. AMIA Ann Symp Proc / AMIA Symp. 2010;2010:6-10.
89. Weitzman ER, Kelemen S, Quinn M, Eggleston EM, Mandl KD. Participatory surveillance of hypoglycemia and harms in an online social network. JAMA Intern Med. 2013;173:345-51. doi: 10.1001/jamainternmed.2013.2512. This cross-sectional survey used a freely available software app to engage members of an online international diabetes social network in reporting about hypoglycemia frequency, severity, and impact.
90. Pogach L, Aron D. Balancing hypoglycemia and glycemic control: a public health approach for insulin safety. JAMA. 2010;303:2076-7. doi: 10.1001/jama.2010.655.
91. Redberg RF. Online network for adverse event reporting. JAMA Intern Med. 2013;173:352-3. doi: 10.1001/jamainternmed.2013.3445. Editorial on the importance of social media surveillance of hypoglycemia and harms as a novel means for post-marketing drug safety surveillance.
92. Rhodes SD, Bowie DA, Hergenrather KC. Collecting behavioral data using the world wide web: considerations for researchers. J Epidemiol Community Health. 2003;57:68-73. (Pubitemid 36054878)
93. Li J. Privacy policies for health social networking sites. JAMIA. 2013;20:704-7. doi: 10.1136/amiajnl-2012-001500.
94. Vayena E, Tasioulas J. Adapting standards: ethical oversight of participant-led health research. PLoS Med. 2013;10:e1001402. doi: 10.1371/journal.pmed.1001402. This policy forum report describes the ethical context for participant-led research and proposes a framework for consenting cohorts and providing ethical oversight.
95. Quinn E, Corrigan M, McHugh S, Murphy D, O'Mullane J, Hill A, et al. Who's talking about breast cancer? Analysis of daily breast cancer posts on the internet. Breast. 2013;22:24-7.