Conference paper Open Access

Treating End-User Feedback Seriously

Kowalski, Radoslaw; Mikhaylov, Slava; Esteve, Marc


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/7bac0c2b-794f-4d30-bb2b-d693b7f49b79/26_kowalski.pdf"
      }, 
      "checksum": "md5:5703c185dccd160a96eb08d315d65525", 
      "bucket": "7bac0c2b-794f-4d30-bb2b-d693b7f49b79", 
      "key": "26_kowalski.pdf", 
      "type": "pdf", 
      "size": 558986
    }
  ], 
  "owners": [
    30201
  ], 
  "doi": "10.5281/zenodo.556506", 
  "stats": {
    "version_unique_downloads": 34.0, 
    "unique_views": 39.0, 
    "views": 40.0, 
    "downloads": 35.0, 
    "unique_downloads": 34.0, 
    "version_unique_views": 39.0, 
    "volume": 19564510.0, 
    "version_downloads": 35.0, 
    "version_views": 40.0, 
    "version_volume": 19564510.0
  }, 
  "links": {
    "doi": "https://doi.org/10.5281/zenodo.556506", 
    "conceptdoi": "https://doi.org/10.5281/zenodo.603166", 
    "bucket": "https://zenodo.org/api/files/7bac0c2b-794f-4d30-bb2b-d693b7f49b79", 
    "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.603166.svg", 
    "html": "https://zenodo.org/record/556506", 
    "latest_html": "https://zenodo.org/record/556506", 
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.556506.svg", 
    "latest": "https://zenodo.org/api/records/556506"
  }, 
  "conceptdoi": "10.5281/zenodo.603166", 
  "created": "2017-06-15T13:40:09.577518+00:00", 
  "updated": "2019-04-10T03:39:03.080447+00:00", 
  "conceptrecid": "603166", 
  "revision": 6, 
  "id": 556506, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.5281/zenodo.556506", 
    "description": "<p>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\u2019 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.</p>", 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "title": "Treating End-User Feedback Seriously", 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "603166"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "556506"
          }
        }
      ]
    }, 
    "communities": [
      {
        "id": "dfp17"
      }
    ], 
    "keywords": [
      "Customer reviews", 
      "healthcare", 
      "natural language processing"
    ], 
    "publication_date": "2017-04-21", 
    "creators": [
      {
        "affiliation": "University College London", 
        "name": "Kowalski, Radoslaw"
      }, 
      {
        "affiliation": "University College London", 
        "name": "Mikhaylov, Slava"
      }, 
      {
        "affiliation": "University College London", 
        "name": "Esteve, Marc"
      }
    ], 
    "meeting": {
      "acronym": "Data for Policy", 
      "url": "http://dataforpolicy.org/about/", 
      "dates": "15-16 September 2016", 
      "place": "Cambridge, United Kingdom", 
      "title": "Data for Policy 2016 -  'Frontiers of Data Science for Government: Ideas, Practices and Projections'"
    }, 
    "access_right": "open", 
    "resource_type": {
      "subtype": "conferencepaper", 
      "type": "publication", 
      "title": "Conference paper"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "identifier": "10.5281/zenodo.603166", 
        "relation": "isVersionOf"
      }
    ]
  }
}
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