What does big data mean for personalized medicine?

Research output: Contribution to conferenceAbstractOther research output

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Abstract

Background
The rapid and ongoing digitalization of society leads to an exponential growth of both structured and unstructured data, so-called Big Data. This wealth of information opens the door to the development of more sophisticated personalized health technologies. The analysis of log data from such applications and wearables provide the opportunity to personalize and to improve their persuasiveness and long-term use.
However, aren’t there any boundaries when using this data as input for data-driven patient-centered feedback systems? If not technologically, then perhaps ethically? Can we simply gather and connect all the information we can find on the Internet and the patients’ health records without question, in order to increase the match between the system, its users and the context?

Methods
In our current research, we use big data sets from digital platforms and wearable technologies to support self-care, used by patients with chronic diseases like diabetes, heart failure, COPD and mental health problems and their caregivers. We apply machine learning techniques (algorithms) to identify patterns and user-profiles in the log data sets from real-time use of technologies across Europe.
To better understand the implications of big data for our healthcare system we will investigate stakeholders’ perceptions regarding factors that are crucial for using and managing big data to support personalized medicine. We will perform focus groups and Q-sort studies to get a broader picture of how to use and interpret data from large and complex datasets in an effective, efficient, secure and safe way to design real-time, accurate, persuasive and personalized feedback systems.

Results
Monitoring the use of health technologies so far provided us insight in how patients can benefit from IT, for example, how users explore new applications or which elements of a website are (hardly) used. We learned from our research that it is a challenge to find a balance between data utility (personalizing feedback) and data security (how to store, share and use anonymized data in such a way that individual patients can benefit from it?). At this moment, advanced machine learning analysis are conducted to identify usage patterns and predictors for return on the long term. First results of these analyses are expected in May 2015.
To better understand how big data impacts society, safety, healthcare and business, and what the critical factors are for using algorithms (machine learning) to personalize healthcare we plan to perform meta-level, boundary crossing research via focus groups and Q-sort studies. First results are expected in July 2015.

Conclusion
Data about IT usage and patient profiles provide new knowledge about how large and unstructured data sets can be used to improve the usability and persuasiveness of technologies and to personalize coaching of patients. Current findings in research indicate there is a gap between collecting big data and “interpreting and translating” this data into user-friendly, safe, unobtrusive and sense making feedback for patients. To estimate the relevance of the outcomes of data-analysis, a better understanding is needed of models that drive the algorithms to analyze big data.
Original languageEnglish
Pages-
Number of pages2
Publication statusPublished - 25 Sep 2015
EventStanford Medicine X 2015: The future of medical education - Stanford University, Stanford, United States
Duration: 23 Sep 201527 Sep 2015
https://medicinex.stanford.edu/2015-glance/

Conference

ConferenceStanford Medicine X 2015
CountryUnited States
CityStanford
Period23/09/1527/09/15
Internet address

Fingerprint

Medicine
Feedback
Learning systems
Health
Medical problems
Security of data
Big data
Websites
Internet
Industry

Keywords

  • IR-96231
  • METIS-310346

Cite this

Sieverink, F., Tjin-Kam-Jet-Siemons, L., Braakman-Jansen, L. M. A., & van Gemert-Pijnen, J. E. W. C. (2015). What does big data mean for personalized medicine?. -. Abstract from Stanford Medicine X 2015, Stanford, United States.
Sieverink, Floor ; Tjin-Kam-Jet-Siemons, Liseth ; Braakman-Jansen, Louise Marie Antoinette ; van Gemert-Pijnen, Julia E.W.C. / What does big data mean for personalized medicine?. Abstract from Stanford Medicine X 2015, Stanford, United States.2 p.
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title = "What does big data mean for personalized medicine?",
abstract = "BackgroundThe rapid and ongoing digitalization of society leads to an exponential growth of both structured and unstructured data, so-called Big Data. This wealth of information opens the door to the development of more sophisticated personalized health technologies. The analysis of log data from such applications and wearables provide the opportunity to personalize and to improve their persuasiveness and long-term use.However, aren’t there any boundaries when using this data as input for data-driven patient-centered feedback systems? If not technologically, then perhaps ethically? Can we simply gather and connect all the information we can find on the Internet and the patients’ health records without question, in order to increase the match between the system, its users and the context? MethodsIn our current research, we use big data sets from digital platforms and wearable technologies to support self-care, used by patients with chronic diseases like diabetes, heart failure, COPD and mental health problems and their caregivers. We apply machine learning techniques (algorithms) to identify patterns and user-profiles in the log data sets from real-time use of technologies across Europe. To better understand the implications of big data for our healthcare system we will investigate stakeholders’ perceptions regarding factors that are crucial for using and managing big data to support personalized medicine. We will perform focus groups and Q-sort studies to get a broader picture of how to use and interpret data from large and complex datasets in an effective, efficient, secure and safe way to design real-time, accurate, persuasive and personalized feedback systems.ResultsMonitoring the use of health technologies so far provided us insight in how patients can benefit from IT, for example, how users explore new applications or which elements of a website are (hardly) used. We learned from our research that it is a challenge to find a balance between data utility (personalizing feedback) and data security (how to store, share and use anonymized data in such a way that individual patients can benefit from it?). At this moment, advanced machine learning analysis are conducted to identify usage patterns and predictors for return on the long term. First results of these analyses are expected in May 2015. To better understand how big data impacts society, safety, healthcare and business, and what the critical factors are for using algorithms (machine learning) to personalize healthcare we plan to perform meta-level, boundary crossing research via focus groups and Q-sort studies. First results are expected in July 2015.ConclusionData about IT usage and patient profiles provide new knowledge about how large and unstructured data sets can be used to improve the usability and persuasiveness of technologies and to personalize coaching of patients. Current findings in research indicate there is a gap between collecting big data and “interpreting and translating” this data into user-friendly, safe, unobtrusive and sense making feedback for patients. To estimate the relevance of the outcomes of data-analysis, a better understanding is needed of models that drive the algorithms to analyze big data.",
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author = "Floor Sieverink and Liseth Tjin-Kam-Jet-Siemons and Braakman-Jansen, {Louise Marie Antoinette} and {van Gemert-Pijnen}, {Julia E.W.C.}",
year = "2015",
month = "9",
day = "25",
language = "English",
pages = "--",
note = "Stanford Medicine X 2015 : The future of medical education ; Conference date: 23-09-2015 Through 27-09-2015",
url = "https://medicinex.stanford.edu/2015-glance/",

}

Sieverink, F, Tjin-Kam-Jet-Siemons, L, Braakman-Jansen, LMA & van Gemert-Pijnen, JEWC 2015, 'What does big data mean for personalized medicine?' Stanford Medicine X 2015, Stanford, United States, 23/09/15 - 27/09/15, pp. -.

What does big data mean for personalized medicine? / Sieverink, Floor; Tjin-Kam-Jet-Siemons, Liseth; Braakman-Jansen, Louise Marie Antoinette; van Gemert-Pijnen, Julia E.W.C.

2015. - Abstract from Stanford Medicine X 2015, Stanford, United States.

Research output: Contribution to conferenceAbstractOther research output

TY - CONF

T1 - What does big data mean for personalized medicine?

AU - Sieverink, Floor

AU - Tjin-Kam-Jet-Siemons, Liseth

AU - Braakman-Jansen, Louise Marie Antoinette

AU - van Gemert-Pijnen, Julia E.W.C.

PY - 2015/9/25

Y1 - 2015/9/25

N2 - BackgroundThe rapid and ongoing digitalization of society leads to an exponential growth of both structured and unstructured data, so-called Big Data. This wealth of information opens the door to the development of more sophisticated personalized health technologies. The analysis of log data from such applications and wearables provide the opportunity to personalize and to improve their persuasiveness and long-term use.However, aren’t there any boundaries when using this data as input for data-driven patient-centered feedback systems? If not technologically, then perhaps ethically? Can we simply gather and connect all the information we can find on the Internet and the patients’ health records without question, in order to increase the match between the system, its users and the context? MethodsIn our current research, we use big data sets from digital platforms and wearable technologies to support self-care, used by patients with chronic diseases like diabetes, heart failure, COPD and mental health problems and their caregivers. We apply machine learning techniques (algorithms) to identify patterns and user-profiles in the log data sets from real-time use of technologies across Europe. To better understand the implications of big data for our healthcare system we will investigate stakeholders’ perceptions regarding factors that are crucial for using and managing big data to support personalized medicine. We will perform focus groups and Q-sort studies to get a broader picture of how to use and interpret data from large and complex datasets in an effective, efficient, secure and safe way to design real-time, accurate, persuasive and personalized feedback systems.ResultsMonitoring the use of health technologies so far provided us insight in how patients can benefit from IT, for example, how users explore new applications or which elements of a website are (hardly) used. We learned from our research that it is a challenge to find a balance between data utility (personalizing feedback) and data security (how to store, share and use anonymized data in such a way that individual patients can benefit from it?). At this moment, advanced machine learning analysis are conducted to identify usage patterns and predictors for return on the long term. First results of these analyses are expected in May 2015. To better understand how big data impacts society, safety, healthcare and business, and what the critical factors are for using algorithms (machine learning) to personalize healthcare we plan to perform meta-level, boundary crossing research via focus groups and Q-sort studies. First results are expected in July 2015.ConclusionData about IT usage and patient profiles provide new knowledge about how large and unstructured data sets can be used to improve the usability and persuasiveness of technologies and to personalize coaching of patients. Current findings in research indicate there is a gap between collecting big data and “interpreting and translating” this data into user-friendly, safe, unobtrusive and sense making feedback for patients. To estimate the relevance of the outcomes of data-analysis, a better understanding is needed of models that drive the algorithms to analyze big data.

AB - BackgroundThe rapid and ongoing digitalization of society leads to an exponential growth of both structured and unstructured data, so-called Big Data. This wealth of information opens the door to the development of more sophisticated personalized health technologies. The analysis of log data from such applications and wearables provide the opportunity to personalize and to improve their persuasiveness and long-term use.However, aren’t there any boundaries when using this data as input for data-driven patient-centered feedback systems? If not technologically, then perhaps ethically? Can we simply gather and connect all the information we can find on the Internet and the patients’ health records without question, in order to increase the match between the system, its users and the context? MethodsIn our current research, we use big data sets from digital platforms and wearable technologies to support self-care, used by patients with chronic diseases like diabetes, heart failure, COPD and mental health problems and their caregivers. We apply machine learning techniques (algorithms) to identify patterns and user-profiles in the log data sets from real-time use of technologies across Europe. To better understand the implications of big data for our healthcare system we will investigate stakeholders’ perceptions regarding factors that are crucial for using and managing big data to support personalized medicine. We will perform focus groups and Q-sort studies to get a broader picture of how to use and interpret data from large and complex datasets in an effective, efficient, secure and safe way to design real-time, accurate, persuasive and personalized feedback systems.ResultsMonitoring the use of health technologies so far provided us insight in how patients can benefit from IT, for example, how users explore new applications or which elements of a website are (hardly) used. We learned from our research that it is a challenge to find a balance between data utility (personalizing feedback) and data security (how to store, share and use anonymized data in such a way that individual patients can benefit from it?). At this moment, advanced machine learning analysis are conducted to identify usage patterns and predictors for return on the long term. First results of these analyses are expected in May 2015. To better understand how big data impacts society, safety, healthcare and business, and what the critical factors are for using algorithms (machine learning) to personalize healthcare we plan to perform meta-level, boundary crossing research via focus groups and Q-sort studies. First results are expected in July 2015.ConclusionData about IT usage and patient profiles provide new knowledge about how large and unstructured data sets can be used to improve the usability and persuasiveness of technologies and to personalize coaching of patients. Current findings in research indicate there is a gap between collecting big data and “interpreting and translating” this data into user-friendly, safe, unobtrusive and sense making feedback for patients. To estimate the relevance of the outcomes of data-analysis, a better understanding is needed of models that drive the algorithms to analyze big data.

KW - IR-96231

KW - METIS-310346

M3 - Abstract

SP - -

ER -

Sieverink F, Tjin-Kam-Jet-Siemons L, Braakman-Jansen LMA, van Gemert-Pijnen JEWC. What does big data mean for personalized medicine?. 2015. Abstract from Stanford Medicine X 2015, Stanford, United States.