Skip to main content

Enabling Cloud Computing to Facilitate Health Analytics Application from Local Hospitals in Thailand

  • Conference paper
  • First Online:
Proceedings of Sixth International Congress on Information and Communication Technology

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 235))

  • 1189 Accesses

Abstract

This study investigates patterns in electronic medical records (EMRs) in Thailand in terms of prescription and treatment cost from patient data with identification diagnosis for cancer, hypertension, and diabetes. This study developed a comparison model of implementing multiple cloud computing platforms in tracking and monitoring medical records for hospitals with limited database and analysis capacity. This study also suggested an application of health data analytics in identifying prescriptions that violated prescription guidelines for patients with chronic diseases.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Suraratdecha C, Saithanu S, Tangcharoensathien V (2005) Is universal coverage a solution for disparities in health care?: findings from three low-income provinces of Thailand. Health Policy 73(3):272–284

    Article  Google Scholar 

  2. Teerawattananon Y, Tantivess S, Yothasamut J, Kingkaew P, Chaisiri K (2009) Historical development of health technology assessment in Thailand. Int J Technol Assess Health Care 25(S1):241–252

    Article  Google Scholar 

  3. Tansitpong P, Chaovalitwongse W (2018) An investigation of differentiated prescription decision on profitability: a case study from Thailand. Int J Healthc Manag 11(1):44–51

    Article  Google Scholar 

  4. Ross SE, Moore LA, Earnest MA, Wittevrongel L, Lin CT (2004) Providing a web-based online medical record with electronic communication capabilities to patients with congestive heart failure: randomized trial. J med Internet Res 6(2)

    Google Scholar 

  5. Ralston JD, Rutter CM, Carrell D, Hecht J, Rubanowice D, Simon GE (2009) Patient use of secure electronic messaging within a shared medical record: a cross-sectional study. J Gen Intern Med 24(3):349–355

    Article  Google Scholar 

  6. Wald JS, Businger A, Gandhi TK, Grant RW, Poon EG, Schnipper JL, Middleton B (2010) Implementing practice-linked pre-visit electronic journals in primary care: patient and physician use and satisfaction. J Am Med Inform Assoc 17(5):502–506

    Article  Google Scholar 

  7. Simon SR, Kaushal R, Cleary PD, Jenter CA, Volk LA, Orav EJ, Burdick E, Poon EG, Bates DW (2007) Physicians and electronic health records: a statewide survey. Arch Intern Med 167(5):507–512

    Article  Google Scholar 

  8. King J, Patel V, Jamoom EW, Furukawa, MF (2014) Clinical benefits of electronic health record use: national findings. Health Servc Res 49(1pt2):392–404

    Google Scholar 

  9. Kohane IS (2011) Using electronic health records to drive discovery in disease genomics. Nat Rev Genet 12(6):417

    Article  Google Scholar 

  10. Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T, Jensen LJ (2011) Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol 7(8):e1002141

    Google Scholar 

  11. Wilke RA, Xu H, Denny JC, Roden DM, Krauss RM, McCarty CA, Savova G (2011) The emerging role of electronic medical records in pharmacogenomics. Clin Pharmacol Ther 89(3):379–386

    Article  Google Scholar 

  12. Shivade C, Raghavan P, Fosler-Lussier E, Embi PJ, Elhadad N, Johnson SB, Lai AM (2014) A review of approaches to identifying patient phenotype cohorts using electronic health records. J Am Med Inform Assoc 21(2):221–230

    Article  Google Scholar 

  13. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37–37

    Google Scholar 

  14. Piatetsky-Shapiro G, Brachman RJ, Khabaza T, Kloesgen W, Simoudis E (1996) An overview of issues in developing industrial data mining and knowledge discovery applications. In: KDD, vol 96, pp 89–95

    Google Scholar 

  15. Milovic B (2012) Prediction and decision making in health care using data mining. Kuwait Chap Arabian J Bus Manag Rev 33(848):1–11

    Google Scholar 

  16. Ţăranu I (2016) Data mining in healthcare: decision making and precision. Database Syst J 6(4):33–40

    Google Scholar 

  17. Hess LM, Michael D, Mytelka DS, Beyrer J, Liepa AM, Nicol S (2016) Chemotherapy treatment patterns, costs, and outcomes of patients with gastric cancer in the United States: a retrospective analysis of electronic medical record (EMR) and administrative claims data. Gastr Cancer 19(2):607–615

    Article  Google Scholar 

  18. Sprandio JD (2010) Oncology patient-centered medical home and accountable cancer care. Commun Oncol 7(12):565–572

    Article  Google Scholar 

  19. Lin HC, Wang Z, Boyd C, Simoni-Wastila L, Buu A (2018) Associations between statewide prescription drug monitoring program (PDMP) requirement and physician patterns of prescribing opioid analgesics for patients with non-cancer chronic pain. Addict Behav 76:348–354

    Article  Google Scholar 

  20. Sánchez-de-Madariaga R, Muñoz A, Lozano-Rubí R, Serrano-Balazote P, Castro AL, Moreno O, Pascual M (2017) Examining database persistence of ISO/EN 13606 standardized electronic health record extracts: relational vs. NoSQL approaches. BMC Med Inform Decis Mak 17(1):1–14

    Google Scholar 

  21. Chen R, Enberg G, Klein GO (2007) Julius–a template based supplementary electronic health record system. BMC Med Inform Decis Mak 7(1):10

    Article  Google Scholar 

  22. Brady TM, Neu AM, Miller ER III, Appel LJ, Siberry GK, Solomon BS (2015) Real-time electronic medical record alerts increase high blood pressure recognition in children. Clin Pediatr 54(7):667–675

    Article  Google Scholar 

  23. Ruiz P, Venegas-Samuels K, Alarcon RD (1995) The economics of pain: mental health care costs among minorities. Psychiatr Clin North Am 18(3):659–670

    Article  Google Scholar 

  24. Balsa AI, Cao Z, McGuire TG (2007) Does managed health care reduce health care disparities between minorities and Whites? J Health Econ 26(1):101–121

    Article  Google Scholar 

  25. Szczepura A (2005) Access to health care for ethnic minority populations. Postgrad Med J 81(953):141–147

    Article  Google Scholar 

  26. Hanchate A, Kronman AC, Young-Xu Y, Ash AS, Emanuel E (2009) Racial and ethnic differences in end-of-life costs: why do minorities cost more than whites? Arch Intern Med 169(5):493–501

    Article  Google Scholar 

  27. Roos NP, Shapiro E, Tate R (1989) Does a small minority of elderly account for a majority of health care expenditures?: a sixteen-year perspective. Milbank Q 347–369

    Google Scholar 

  28. Braddock CH, Fihn SD, Levinson W, Jonsen AR, Pearlman RA (1997) How doctors and patients discuss routine clinical decisions: informed decision making in the outpatient setting. J Gen Intern Med 12(6):339–345

    Google Scholar 

  29. Flynn KE, Smith MA, Vanness D (2006) A typology of preferences for participation in healthcare decision making. Soc Sci Med 63(5):1158–1169

    Article  Google Scholar 

  30. Fraenkel L, McGraw S (2007) What are the essential elements to enable patient participation in medical decision making? J Gen Intern Med 22(5):614–619

    Article  Google Scholar 

  31. Amjad H, Carmichael D, Austin AM, Chang CH, Bynum JP (2016) Continuity of care and health care utilization in older adults with dementia in fee-for-service Medicare. JAMA Intern Med 176(9):1371–1378

    Article  Google Scholar 

  32. Motheral B, Fairman KA (2001) Effect of a three-tier prescription copay on pharmaceutical and other medical utilization. Med Care 39(12):1293–1304

    Google Scholar 

  33. Ganther-Urmie JM, Nair KV, Valuck R, McCollum M, Lewis SJ, Turpin RS (2004) Consumer attitudes and factors related to prescription switching decisions in multitier copayment drug benefit plans. Am J Manag Care 10(3):201–208

    Google Scholar 

  34. Priya K, Joy N, Thottumkal AV, Warrier AR, Krishna SG, Joseph N (2018) Impact of electronic prescription audit process to reduce outpatient medication errors. Indian J Pharm Sci 79(6):1017–1022

    Google Scholar 

  35. Winter JD, Kerns JW, Winter KM, Sabo RT (2019) Increased reporting of exclusionary diagnoses inflate apparent reductions in long-stay antipsychotic prescribing. Clin Gerontol 42(3):297–301

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Praowpan Tansitpong .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tansitpong, P. (2022). Enabling Cloud Computing to Facilitate Health Analytics Application from Local Hospitals in Thailand. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_20

Download citation

Publish with us

Policies and ethics