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Health Systems and Policy Research

  • ISSN: 2254-9137
  • Journal h-index: 10
  • Journal CiteScore: 1.70
  • Journal Impact Factor: 1.84
  • Average acceptance to publication time (5-7 days)
  • Average article processing time (30-45 days) Less than 5 volumes 30 days
    8 - 9 volumes 40 days
    10 and more volumes 45 days
Awards Nomination 20+ Million Readerbase
PMC/PubMed Indexed Articles

Challenges Facing Successful Scaling Up of Effective Screening for Cardiovascular Disease by Community Health Workers in Mexico and South Africa: Policy Implications

Acceptability and Utilization of Community Health Workers after the Adoption of the Integrated Community Case Management Policy in Kabarole District in Uganda

Google Scholar citation report
Citations : 461

Health Systems and Policy Research received 461 citations as per Google Scholar report

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Indexed In
  • China National Knowledge Infrastructure (CNKI)
  • Cosmos IF
  • Scimago
  • Directory of Research Journal Indexing (DRJI)
  • OCLC- WorldCat
  • Publons
  • Geneva Foundation for Medical Education and Research
  • Euro Pub
  • Google Scholar
  • J-Gate
  • SHERPA ROMEO
  • International Committee of Medical Journal Editors (ICMJE)
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Venkat Lellapalli

Mississippi State University, USA

Publications

  • Special Issue Article   
    Machine Learning Analysis of Readmission of Patients Diagnosed with Ischemic and Pulmonary Heart Diseases
    Author(s): Venkat Lellapalli

    Hospital readmissions are indicators of the quality of service offered by hospitals and give an insight into the performance measures on the cost at the hospital. A readmission event occurs when a patient that has been discharged from a hospital after diagnosis and procedure is again readmitted to the hospital within a certain period. The Nationwide Readmissions Database (NRD) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). For this research, the data for the year 2016 from the National Readmission Database (NRD) will be studied and machine learning models built to model the relationship between readmission and various factors related to the patient. The models built in this research study will be used to ease the prediction of hospital readmission which is very important in healthcare management. Ischemic and Pulmo.. View More»

    Abstract PDF