Document Type : Research Article


1 Assistant Professor, School of Management, Sri Krishna College of Engineering and Technology, Coimbatore, India

2 PG Student, School of Management, Sri Krishna College of Engineering and Technology, Coimbatore, India


In this project, we describe some of the key observations resulting from our work on using Bajaj Finserv Health DoctorRX platform to help and detect effectiveness of PMS (Practice Management system) processes and usage of platform among doctors. In many ways, medical processes are similar to distributed systems in their complexity and proneness to contain errors. We have been investigating the application of a DoctorRX platform for improvement and to make approach the medical processes in much easier way and so that it is subjected to rigorous analyses. The technologies we applied helped improve understanding about the processes and led to the detection of errors and subsequent improvements of the platform. This work is still preliminary, but is suggesting new research directions for improvement, and effectiveness of platform along with, and the applicability of this available system. Doctors have one of the most demanding jobs in the world – structuring up treatment plans, attending to patients, charting the job plan, clerical work, and keeping updated with the most recent advancements in the field of medical technology. Doctors are our marvel workers on this planet Earth and sure seem to have their hands full at all given times. However, with the coming up of modern technology encircling all aspects of life, it is no revelation that there are a numerous time-saving expedient software for doctors like Practo, pMD, Prescription pad, etc., In this project, Doctor RX platform has been taken into consideration to understand in depth the process that is carried out.


Main Subjects

  1. Anand V, Carroll AE, Downs SM. Automated primary care screening in pediatric waiting rooms. Pediatrics. 2012;129:e1275–81. 
  2.  Arora P. Is the doctor on. In search for users of rural medical diagnostic software in central himalayas? Dev Pract. 2012;22:180–9.
  3. Avery T, Barber N, Ghaleb M, Franklin BD, Armstrong S, Crowe S. Investigating the Prevalence and Causes of Prescribing Errors in General Practice: The Practice Study (Prevalence and Causes of Practicing Errors in General Practice) A Report for the GMC. 2012 May;:1–259.
  4. Prof. Shweta Jain. (2017). Design and Analysis of Low Power Hybrid Braun Multiplier using Ladner Fischer Adder. International Journal of New Practices in Management and Engineering, 6(03), 07 - 12.
  5. Prof. Bhushan Thakre, Dr. R.M Thakre. (2017). Analysis of Modified Current Controller and its Implementation in Automotive LED. International Journal of New Practices in Management and Engineering, 6(04), 01 - 06.
  6. Prof. Deepanita Mondal. (2018). Analysis and Evaluation of MAC Operators for Fast Fourier Transformation. International Journal of New Practices in Management and Engineering, 7(01), 01 - 07.
  7. Prof. Arun Pawar, Mr. Dharmesh Dhabliya. (2018). Intelligent Modulation Recognition System and its Implementation using MATLAB. International Journal of New Practices in Management and Engineering, 7(01), 08 - 14.
  8. Bhambhani R, Bhattacharya J, Sen SK. Digitization and its futuristic approach in prosthodontics. J Indian Prosthodont Soc. 2013;13:165–74. 
  9.  Ferrara FM. The standard ‘healthcare information systems architecture’ and the DHE middleware. Int J Med Inform. 1998;52:39–51. 
  10. Ganesh A, Al-Mujaini A. Electronic medical record system: Have we bitten off more than we can chew? Oman Med J. 2009;24:1–3. 
  11. Gil, D.; Ferrández, A.; Mora-Mora, H.; Peral, J. Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services. Sensors 2016, 16, 1069. Kalem, G.; Turhan, Ç. Mobile Technology Applications in the Healthcare Industry for Disease Management and Wellness. Procedia Soc. Behav. Sci. 2015, 195, 2014–2018.
  12. Hong, Y.-J.; Kim, I.-J.; Chul Ahn, S.; Kim, H.-G. Mobile health monitoring system based on activity recognition using accelerometer. Simul. Model. Pract. Theor. 2010, 18, 446–455.
  13. Hovenga E, Garde S, Heard S. Nursing constraint models for electronic health records: A vision for domain knowledge governance. Int J Med Inform. 2005;74:886–98. 
  14. Lyons JP, Klasko S. Introduction of an electronic medical record system into physician practice offices: Why is it so #%! and -ing hard for everybody?-Part II. J Med Pract Manage. 2011;26:342–5. 
  15. Maimbolwa MC, Yamba B, Diwan V, Ransjö-Arvidson AB. Cultural childbirth practices and beliefs in Zambia. J Adv Nurs. 2003;43:263–74. 
  16. Mandl, K.D.; Mandel, J.C.; Kohane, I.S. Driving Innovation in Health Systems through an Apps-Based Information Economy. Cell Syst. 2015, 1, 8–13. Fafoutis, X.; Janko, B.; Mellios, E.; Hilton, G.; Sherratt, S.; Piechocki, R.; Craddock, I. SPW-1: A Low-Maintenance Wearable Activity Tracker for Residential Monitoring and Healthcare Applications. In Proceedings of the EAI International Conference on Wearables in Healthcare, Budapest, Hungary, 14–15 June 2016.
  17. Masud, M.M.; Serhani, M.A.; Navaz, A.N. Resource-Aware Mobile-Based Health Monitoring. IEEE J. Biomed. Health Inform. 2017, 21, 349–360.
  18. Salehi, S.A.; Razzaque, M.A.; Tomeo-Reyes, I.; Hussain, N. IEEE 802.15.6 standard in wireless body area networks from a healthcare point of view. In Proceedings of the Asia-Pacific Conference on Communications (APCC), Yogyakarta, Indonesia, 25–27 August 2016.
  19. Terroso, M.; Freitas, R.; Gabriel, J. Active assistance for senior healthcare: A wearable system for fall detection. In Proceedings of the Iberian Conference on Information Systems and Technologies (CISTI), Lisboa, Portugal, 19–22 October 2013. 31.
  20. Varshney, U. Pervasive healthcare and wireless health monitoring. J. Mob. Netw. Appl. 2007, 12, 113–127.
  21. Yang, Z.; Zhou, Q.; Lei, L.; Zheng, K.; Xiang, W. An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare. J. Med. Syst. 2016, 40, 286.
  22. Yoo, J.; Yoo, H.-J. Emerging low energy Wearable Body Sensor Networks using patch sensors for continuous healthcare applications. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2010.
  23. Zhang, F.; Cao, J.; Khan, S.U.; Li, K.; Hwang, K. A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications. Future Gener. Comput. Syst. 2015, 43–44, 149–160.