58
views
0
recommends
+1 Recommend
1 collections
    0
    shares
       
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      DHANWANTARI : AN ANDROID APPLICATION TO INCREASE SERVICE DELIVERY IN HEALTHCARE INDUSTRY

      Preprint
      In review
      research-article
        1 ,
      ScienceOpen Preprints
      ScienceOpen
      Remote Patient Monitoring, Machine Learning Algorithm, Online Prescription Management, Online Medical Management
      Bookmark

            Abstract

            When patients receive care or services, inefficiency can be defined as using more inputs (or resources) than is necessary, and it is associated with unnecessary variation in operational and clinical processes. Among the 8.6 million preventable deaths in 2016, more than 1 million were caused by neonatal problems and tuberculosis in those who accessed the health system but received poor quality of care.

            Content

            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            5 May 2022
            Affiliations
            [1 ] Computer Science, University of Delhi, Benito Juarez Marg, South Campus, South Moti Bagh, New Delhi, Delhi 110021
            Author notes
            Article
            10.14293/S2199-1006.1.SOR-.PPWFVIU.v1
            0c66af64-efb1-48a0-9cb9-f46a385762b7

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            The data that support the findings of this study are available from https://impact.dbmi.columbia.edu/~friedma/Projects/DiseaseSymptomKB/index.html but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of https://impact.dbmi.columbia.edu/~friedma/Projects/DiseaseSymptomKB/index.html.
            Software engineering,Artificial intelligence,Human-computer-interaction
            Remote Patient Monitoring,Machine Learning Algorithm,Online Prescription Management,Online Medical Management

            References

            1. Das Jishnu, Hammer Jeffrey. Money for nothing: The dire straits of medical practice in Delhi, India. Journal of Development Economics. Vol. 83(1):1–36. 2007. Elsevier BV. [Cross Ref]

            2. Sime Arin W.. WebRTC: delivering telehealth in the browser. mHealth. Vol. 2:2016. AME Publishing Company. [Cross Ref]

            3. Dean B. What is a prescribing error? Quality in Health Care. Vol. 9(4):232–237. 2000. BMJ. [Cross Ref]

            4. Ferner Robin E, Aronson Jeffrey K. Clarification of Terminology in Medication Errors. Drug Safety. Vol. 29(11):1011–1022. 2006. Springer Science and Business Media LLC. [Cross Ref]

            5. Cornish Patricia L., Knowles Sandra R., Marchesano Romina, Tam Vincent, Shadowitz Steven, Juurlink David N., Etchells Edward E.. Unintended Medication Discrepancies at the Time of Hospital Admission. Archives of Internal Medicine. Vol. 165(4)2005. American Medical Association (AMA). [Cross Ref]

            6. Malasinghe Lakmini P., Ramzan Naeem, Dahal Keshav. Remote patient monitoring: a comprehensive study. Journal of Ambient Intelligence and Humanized Computing. Vol. 10(1):57–76. 2019. Springer Science and Business Media LLC. [Cross Ref]

            7. Lee Sandra, Huang Hui, Zelen Marvin. Early detection of disease and scheduling of screening examinations. Statistical Methods in Medical Research. Vol. 13(6):443–456. 2004. SAGE Publications. [Cross Ref]

            Comments

            Comment on this article