Conference paper Open Access

Treating End-User Feedback Seriously

Kowalski, Radoslaw; Mikhaylov, Slava; Esteve, Marc


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Customer reviews</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">healthcare</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">natural language processing</subfield>
  </datafield>
  <controlfield tag="005">20190410033903.0</controlfield>
  <controlfield tag="001">556506</controlfield>
  <datafield tag="711" ind1=" " ind2=" ">
    <subfield code="d">15-16 September 2016</subfield>
    <subfield code="g">Data for Policy</subfield>
    <subfield code="a">Data for Policy 2016 -  'Frontiers of Data Science for Government: Ideas, Practices and Projections'</subfield>
    <subfield code="c">Cambridge, United Kingdom</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">University College London</subfield>
    <subfield code="a">Mikhaylov, Slava</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">University College London</subfield>
    <subfield code="a">Esteve, Marc</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">558986</subfield>
    <subfield code="z">md5:5703c185dccd160a96eb08d315d65525</subfield>
    <subfield code="u">https://zenodo.org/record/556506/files/26_kowalski.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="y">Conference website</subfield>
    <subfield code="u">http://dataforpolicy.org/about/</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2017-04-21</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="p">user-dfp17</subfield>
    <subfield code="o">oai:zenodo.org:556506</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">University College London</subfield>
    <subfield code="a">Kowalski, Radoslaw</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Treating End-User Feedback Seriously</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-dfp17</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">http://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;Currently, patient satisfaction from NHS services is estimated with measures that may hardly relate to selfreported needs of patients or that use old data. Nonetheless, healthcare institutions depend on funding that is decided in part with the help of the ill-calculated patient satisfaction. As a result, patients’ actual best interest may be in conflict with the best interest of the evaluated NHS health organisations. Patients may receive suboptimal health services and lose trust in professionalism and intentions of doctors and health organisations that try to stick to performance targets. The reputation of medical professions may also drop and prompt health professionals to seek work elsewhere. Development of new organisational performance measurement tools from text data, the motive behind this study, can break the vicious cycle of distrust and improve the quality of healthcare by more accurately measuring patient satisfaction. The study involves processing of free-text online reviews of NHS GP services in England with deep learning to obtain a numeric representation of text data. Once text is transformed into numbers, two-ways fixed-effects regressions were carried out to see if there is a statistically significant correlation between how patients write about individual GP practices and their numeric evaluations of the GP services. Initial findings indicate that written reviews can be used as a predictor of patient satisfaction, and may be used to obtain real-time insights about whether and why patients are happy and/or unhappy about their GP service experience.&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.603166</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.556506</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">conferencepaper</subfield>
  </datafield>
</record>
40
35
views
downloads
All versions This version
Views 4040
Downloads 3535
Data volume 19.6 MB19.6 MB
Unique views 3939
Unique downloads 3434

Share

Cite as