Health and medicine sociology

Personalized medicine and big data: what are the social issues raised by these medical and technological advances? (session 2 of 2)

June 28, 2021 15:00
June 28, 2021 16:30

Monica Aceti, University of Basel; Maria Caiata Zufferey, SUPSI


Monica Aceti, University of Basel; Maria Caiata Zufferey, SUPSI

Or Cohen-Sasson, Tel Aviv University

Selin Köksal, Bocconi University; Luca Maria Pesando, McGill University; Valentina Rotondi, University of Applied Sciences and Arts of Southern Switzerland & University of Oxford; Ebru Sanhtürk, Bocconi University

Anja Köngeter¹, Martin Jungkunz¹, Eva Winkler¹, Christoph Schickhardt², Katja Mehlis¹

¹Nationales Centrum für Tumorerkrankungen (NCT), Sektion für Translationale Medizinethik, Universitätsklinikum Heidelberg

²Nationales Centrum für Tumorerkrankungen (NCT), Deutsches Krebsforschungszentrum (DKFZ), Heidelberg

Considered the "medicine of the future", personalized medicine (Guchet, 2016) has made significant advances, particularly in precision oncology thanks to the advent of high-throughput sequencing. The acceleration of diagnostics, as well as "tailor-made" therapies, have improved the treatment of hereditary cancers. These advances have also raised hope for curing chronic, mental and/or orphan diseases. Nonetheless, they also invoke a number of fears (Aceti et al., 2020). In order to understand these tensions, three themes seem to be of major interest from a sociological perspective. 

Firstly, predictive medicine is not focused on symptoms but on predispositions to develop a disease. In this sense, it is applied to anticipate, monitor or accompany pathogenic risks. While it is promising because it offers previously unimaginable care opportunities, it also raises questions concerning the health injunctions that may accompany it (Caiata Zufferey, 2015). Analyzing the effects of predictive and probabilistic health care is, thus, a crucial issue, especially since it raises the problem of the unequal disposition of different social strata to comply with preventive practices and to benefit from them afterwards. 

Second, personalized health integrates individual data (such as diet, physical activity, mobility) with health data. It is a growing field that relies more on preventive behaviors than on curative instruments. In this approach, patients are actors of their health and will collect and manage their personal health data in a proactive way, often participating in online databases. The collection of these data raises issues related to big data, to their management and to the various uses of them, whether these uses are scientific, commercial, recreational or abusive. 

Thirdly, from a broader point of view, this "revolutionary" medicine is based on genome editing techniques and more recently on the "molecular scissors" of the geneticists Charpentier and Doudna (Nobel Prize in Chemistry 2020). Over and above the potential benefits, the possibility to modify our genome raises questions about our intangible genetic heritage, either human or non-human. The innocuity of these modifications, which are transmissible to human offspring, is currently not assured and calls for caution. 

Based on these considerations, we welcome proposals for contributions addressing the issues of social equality and inequities related to personalized medicine Additional themes are the social consequences of scientific, genetic and technological advances oriented towards health prediction and disease prevention. 

The following list of topics (non-exhaustive) would be welcome: 

  • Predictive medicine and health moralization 
  • (Un-)certainties generated by genetic knowledge 
  • Protection of personal health data and confidentiality 
  • Unequal access to gene therapies 
  • Perverse effects of unrealistic promises of healing 
  • Genetic traceability 
  • Deviations of genetic uses

The continuous disclosure doctrine: how to turn secret data to open data

Or Cohen-Sasson, Tel Aviv University

At times, the changing technological environment poses challenges to the law. Such challenges often arise due to technological blind-spots of lawmakers, which prevent the law's capacity to cope with emerging technologies properly. This research project deals with such a case: the challenges that big data pose to the patent system, and particularly to the patent disclosure requirement. 

The purpose of the disclosure requirement is to bring to the public full technical knowledge regarding a patented invention so that others (e.g., competitors) can utilize the invention as well once the patent expires. To ensure that no patent is granted without full disclosure, patent law requires a patent applicant to submit the disclosure documents with his/her patent application; otherwise, the Patent Office will reject the application. 

The disclosure requirement seems to achieve its expected goal in relation to inventions in traditional or ‘classical’ technology fields, such as mechanics. However, when it comes to modern inventions – particularly when it comes to inventions that heavily rely on big data – the disclosure requirement reveals only parts of the sought-for knowledge, originally designed to be covered under the scope of the disclosure requirement. 

The main reasons for this failure lie in the structural characteristics of the disclosure requirements: The Temporal Dimension and the Static Dimension. These characteristics are the outcome of an obsolete technological paradigm, originated in the historical times of patent law, which shaped the patent system in a certain way and, currently, limits its capability to deal with emerging technologies properly. 

After discussing the said structural factors, the paper will analyze the ramifications of partial disclosure on the public, specifically in two fields: firstly, free market and competition law, and secondly, the advancement of science and technology in modern society. 

The general problem with partial disclosure is that it allows ex-patent holders to exploit practices that concentrate commercial and technological power under their hands; the scope of such power goes much beyond the usual borders of patent rights. Thus, patents for inventions in technological areas correlated with big data affect the public not only during the patent period but even in the post-patent period, i.e., after the patent expires. 

The last part of the paper will suggest a solution: I propose to adopt a new doctrine (or more precisely, to expand an existing one) to patent law – the Doctrine of Continuous Disclosure. According to this doctrine, inventive knowledge related to an invention will be deemed part of the knowledge required to be disclosed, even if such knowledge is revealed to the patentee after the date of the patent application.

Harnessing the Potential of Online Searches for Understanding the Impact of COVID-19 on Intimate Partner Violence in Italy

Selin Köksal, Bocconi University; Luca Maria Pesando, McGill University; Valentina Rotondi, University of Applied Sciences and Arts of Southern Switzerland & University of Oxford; Ebru Sanhtürk, Bocconi University

Despite the volume of studies leveraging big data to explore socio-demographic phenomena, we still know little about the intersection of digital information and the social problem of intimate partner violence (IPV). This is an important knowledge gap, as IPV remains a pressing public-health concern worldwide, with 35% of women having experienced it over their lifetime and cases rising dramatically in the wake of global crises such as the current COVID-19 pandemic. This study addresses the question of whether online data from Google Trends might help to reach \hard-to-reach" populations such as victims of IPV using Italy as a case-study. We ask the following questions: Can digital traces help predict instances of IPV | both potential threat and actual violent cases | in Italy? Is their predictive power weaker or stronger in the aftermath of crises such as COVID-19? Our results combined suggest that online Google searches using selected keywords measuring different aspects of IPV are a powerful tool to track potential threats of IPV before and after

global-level crises such as the current COVID-19 pandemic - with stronger predictive power post-crisis - while online searches help to predict actual violence only in post-crises scenarios.

Keywords:  Digital data, Google Trends, Intimate Partner Violence, Italy, COVID-19 

Erwartungen und Einstellung von Krebspatient*innen gegenüber der Nutzung ihrer klinischen Daten für Forschungszwecke - Eine quantitative Untersuchung

Anja Köngetera, Martin Jungkunza, Eva Winklera, Christoph Schickhardtb, Katja Mehlisa
ᵃNationales Centrum für Tumorerkrankungen (NCT), Sektion für Translationale Medizinethik, Universitätsklinikum Heidelberg
ᵇNationales Centrum für Tumorerkrankungen (NCT), Deutsches Krebsforschungszentrum (DKFZ), Heidelberg

Der Sekundärnutzung klinischer Routinedaten wird bei der Entwicklung des Gesundheitswesens der Zukunft großes Potenzial für die Verbesserung der biomedizinischen Forschung und letztlich der medizinischen Versorgung zugeschrieben. Jedoch äußerten in einer vorbereitenden, hypothesengenerierenden Interview-Studie (leitfadengestützte Experteninterviews; purposive sample; n=21 aus den Bereichen Forschung, Versorgung, Medizininformatik, Patientenvertretung und Politik) im Rahmen einer Ethik-Folgenabschätzung einige Expert*innen Bedenken. Diese bezogen sich unter anderem auf eine nicht ausreichende Beachtung der Patientenautonomie im Kontext des Einwilligungsprozesses oder Risiken bei der Verwendung klinischer Daten durch privatwirtschaftlich tätige Datennutzer. Dies geschehe bei gleichzeitig hoher Unsicherheit in Hinblick auf technologische Entwicklungen, die zu einem erhöhten Re-identifikationsrisiko der Datensätze führen könnten. Aus der Sicht der Expert*innen sei zudem das Wissen um die Erwartungen und die Risikowahrnehmung von Patienten*innen hinsichtlich dieser Datennutzung in Deutschland wesentlich, um in den kommenden Jahren gesellschaftlich akzeptable Lösungen zu entwickeln. Allerdings liegen für den deutschen Forschungskontext hierzu bisher keine systematischen Analysen vor. Insbesondere die Einstellungen von besonders vulnerablen Personengruppen mit schweren Erkrankungen wie Krebs, deren klinische Daten als besonders sensibel eingestuft werden können, wurden in Deutschland bisher nicht erforscht.

Ziel des Beitrags ist es daher, die Erwartungen und Präferenzen von Patient*innen zu untersuchen, die akut oder in der Vergangenheit an einer hämatologisch/onkologischen Erkrankung leiden bzw. litten. Dem Konzept der empirischen Ethik folgend, sollen die empirischen Ergebnisse abschließend normativ eingeordnet werden.

Die quantitative Studie in Form einer schriftlichen Befragung (angestrebt: n>700) wird in Kooperation mit dem Krebsregister Baden-Württemberg durchgeführt. Hierfür soll eine repräsentative Stichprobe aus der Grundgesamtheit aller akut erkrankten und genesenen hämatologisch/onkologischen Patient*innen in Baden-Württemberg gezogen werden.

Dieser Beitrag präsentiert die Ergebnisse der quantitativen Befragung, i.e. i) die Sichtweise der Patient*innen gegenüber wahrgenommenen Risiken durch die Nutzung ihrer klinischen Daten (z.B. durch kommerzielle Datennutzer), ii) Erwartungen an den Einwilligungsprozess vor dem Hintergrund der Patientenautonomie, iii) konkrete Befunde, unter welchen Bedingungen die Befragten ihre klinischen Daten für Forschungszwecke zur Verfügung stellen.

Diese Analyse soll grundlegende Erkenntnisse zur Bereitschaft einer besonders vulnerablen Patientengruppe beitragen. Hierdurch können Hinweise für eine ethisch fundierten Technikfolgenabschätzung sowie die Ausarbeitung von gesellschaftlich akzeptablen Empfehlungen für Entscheidungsträger aus Politik und Praxis herausgearbeitet werden um die Integration einer individualisierten Risikowahrnehmung sowie die technologische Unsicherheit zu ermöglichen. Konkret können diese Ergebnisse in angemessene Datennutzungsmodelle, Einwilligungskonzepte, Partizipationsstrategien (Public and Patient Involvement) und Unterstützungsangebote für Patient*innen einfließen.