Management of the Diabetic Patient in the Diagnostic Care Pathway | SpringerLink
Skip to main content

Management of the Diabetic Patient in the Diagnostic Care Pathway

  • Conference paper
  • First Online:
8th European Medical and Biological Engineering Conference (EMBEC 2020)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 80))

Included in the following conference series:

Abstract

Diabetes is a complex pathology both for the affected patients and for the medical specialists who follow them. Furthermore, since diabetes is a pathology with a high prevalence and incidence, it is essential to intervene effectively in therapeutic actions through the application of common guidelines. Therefore, in order to improve the management of the diabetic patient, the aim of the work is to define a Diagnostic Therapeutic Assistance Pathway (PDTA). A questionnaire-based approach is adopted for data collection from 136 patients at the Clinical Dermatology Unit of the University Hospital “Federico II”. In most cases (64%) the diagnosis was made by the General Practitioner, 15% of patients obtained the diagnosis at the ASL and 12% at the Polyclinic of Naples AOU “Federico II” and the remaining part from the diabetologist specialist. The second access is generally carried out at the “Federico II” AOU (66%), followed by the ASL (17%), by a doctor specialized in diabetology (12%) while no patient has turned to the General Practitioner for the treatment of diabetes. The final visit is carried out at the “Federico II” AOU in almost cases. The data obtained follow the Italian guidelines: the patients get the diagnosis from the Family Doctor and then they are addressed either to ASL or to diabetologists specialists. For the subsequent visits, most of them prefer to turn to the “Federico II” AOU, especially when they have complications associated with the diseases as they are followed in a more careful and satisfying manner.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 22879
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 28599
Price includes VAT (Japan)
  • 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. World Health Organization, et al.: Global health observatory (GHO) data. 2016. Child Mortal. Causes Death WHO Geneva (2016)

    Google Scholar 

  2. ISTAT: Annuario statistico italiano (Italian Statistical Yearbook). ISTAT Roma (2016)

    Google Scholar 

  3. European Association for Cardiovascular Prevention & Rehabilitation, Reiner, Z., Catapano, A.L., De Backer, G., Graham, I., Taskinen, M.-R., Wiklund, O., Agewall, S., Alegria, E., Chapman, M.J., Durrington, P., Erdine, S., Halcox, J., Hobbs, R., Kjekshus, J., Filardi, P.P., Riccardi, G., Storey, R.F., Wood, D.: ESC Committee for Practice Guidelines (CPG) 2008–2010 and 2010-2012 Committees: ESC/EAS Guidelines for the management of dyslipidaemias: the Task Force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and the European Atherosclerosis Society (EAS). Eur. Heart J. 32, 1769–1818 (2011). https://doi.org/10.1093/eurheartj/ehr158

  4. Montecucco, F., Mach, F.: Common inflammatory mediators orchestrate pathophysiological processes in rheumatoid arthritis and atherosclerosis. Rheumatol. Oxf. Engl. 48, 11–22 (2009). https://doi.org/10.1093/rheumatology/ken395

    Article  Google Scholar 

  5. Italiano, A.S.: ISTAT 2014 (2015)

    Google Scholar 

  6. Verrillo, A., de Teresa, A., Nunziata, G., Rucco, E.: Epidemiology of diabetes mellitus in an Italian rural community. Diabet. Metab. 9, 9–13 (1983)

    Google Scholar 

  7. Bianchi, C., Rossi, E., Miccoli, R.: Epidemiologia del diabete. In: Il diabete in Italia, pp. 13–19. Bononia University Press (2016)

    Google Scholar 

  8. Bonora, E., Kiechl, S., Willeit, J., Oberhollenzer, F., Egger, G., Meigs, J.B., Bonadonna, R.C., Muggeo, M.: Bruneck study: population-based incidence rates and risk factors for type 2 diabetes in white individuals: the Bruneck study. Diabetes 53, 1782–1789 (2004). https://doi.org/10.2337/diabetes.53.7.1782

    Article  Google Scholar 

  9. Bruno, G., Novelli, G., Panero, F., Perotto, M., Monasterolo, F., Bona, G., Perino, A., Rabbone, I., Cavallo-Perin, P., Cerutti, F.: Piedmont study group for diabetes epidemiology: the incidence of type 1 diabetes is increasing in both children and young adults in Northern Italy: 1984-2004 temporal trends. Diabetologia 52, 2531–2535 (2009). https://doi.org/10.1007/s00125-009-1538-x

    Article  Google Scholar 

  10. Improta, G., Ricciardi, C., Borrelli, A., D’alessandro, A., Verdoliva, C., Cesarelli, M.: The application of six sigma to reduce the pre-operative length of hospital stay at the hospital Antonio Cardarelli. Int. J. Lean Six Sigma (2019). https://doi.org/10.1108/IJLSS-02-2019-0014

  11. Montella, E., Cicco, M.V.D., Ferraro, A., Centobelli, P., Raiola, E., Triassi, M., Improta, G.: The application of Lean Six Sigma methodology to reduce the risk of healthcare–associated infections in surgery departments. J. Eval. Clin. Pract. 23, 530–539 (2017). https://doi.org/10.1111/jep.12662

    Article  Google Scholar 

  12. Improta, G., Balato, G., Romano, M., Ponsiglione, A.M., Raiola, E., Russo, M.A., Cuccaro, P., Santillo, L.C., Cesarelli, M.: Improving performances of the knee replacement surgery process by applying DMAIC principles. J. Eval. Clin. Pract. 23, 1401–1407 (2017). https://doi.org/10.1111/jep.12810

    Article  Google Scholar 

  13. Ricciardi, C., Fiorillo, A., Valente, A.S., Borrelli, A., Verdoliva, C., Triassi, M., Improta, G.: Lean Six Sigma approach to reduce LOS through a diagnostic-therapeutic-assistance path at A.O.R.N. A. Cardarelli. TQM J. 31, 657–672 (2019). https://doi.org/10.1108/TQM-02-2019-0065

  14. Ricciardi, C., Balato, G., Romano, M., Santalucia, I., Cesarelli, M., Improta, G.: Fast track surgery for knee replacement surgery: a lean six sigma approach. TQM J. (2020). https://doi.org/10.1108/TQM-06-2019-0159

    Article  Google Scholar 

  15. Improta, G., Balato, G., Ricciardi, C., Russo, M.A., Santalucia, I., Triassi, M., Cesarelli, M.: Lean Six Sigma in healthcare: fast track surgery for patients undergoing prosthetic hip replacement surgery. TQM J. (2019)

    Google Scholar 

  16. Converso, G., Improta, G., Mignano, M., Santillo, L.C.: A simulation approach for agile production logic implementation in a hospital emergency unit. In: International Conference on Intelligent Software Methodologies, Tools, and Techniques, pp. 623–634. Springer (2015)

    Google Scholar 

  17. Improta, G., Russo, M.A., Triassi, M., Converso, G., Murino, T., Santillo, L.C.: Use of the AHP methodology in system dynamics: modelling and simulation for health technology assessments to determine the correct prosthesis choice for hernia diseases. Math. Biosci. 299, 19–27 (2018). https://doi.org/10.1016/j.mbs.2018.03.004

    Article  MathSciNet  MATH  Google Scholar 

  18. Improta, G., Perrone, A., Russo, M.A., Triassi, M.: Health technology assessment (HTA) of optoelectronic biosensors for oncology by analytic hierarchy process (AHP) and Likert scale. BMC Med. Res. Methodol. 19, 140 (2019)

    Article  Google Scholar 

  19. Improta, G., Converso, G., Murino, T., Gallo, M., Perrone, A., Romano, M.: Analytic Hierarchy Process (AHP) in dynamic configuration as a tool for Health Technology Assessment (HTA): the Case of biosensing optoelectronics in oncology. Int. J. Inf. Technol. Decis. Mak. IJITDM. 18, 1533–1550 (2019)

    Article  Google Scholar 

  20. Ricciardi, C., Amboni, M., De Santis, C., Improta, G., Volpe, G., Iuppariello, L., Ricciardelli, G., D’Addio, G., Vitale, C., Barone, P., Cesarelli, M.: The motion analysis “Schola Medica Salernitana“ Group, the biomedical engineering unit: using gait analysis’ parameters to classify parkinsonism: a data mining approach. Comput. Methods Programs Biomed. 180 (2019). https://doi.org/10.1016/j.cmpb.2019.105033

  21. Improta, G., Mazzella, V., Vecchione, D., Santini, S., Triassi, M.: Fuzzy logic–based clinical decision support system for the evaluation of renal function in post-transplant patients. J. Eval. Clin. Pract. (2019). https://doi.org/10.1111/jep.13302

    Article  Google Scholar 

  22. Santini, S., Pescape, A., Valente, A.S., Abate, V., Improta, G., Triassi, M., Ricchi, P., Filosa, A.: Using fuzzy logic for improving clinical daily-care of β-thalassemia patients. In: IEEE International Conference on Fuzzy System Institute of Electrical and Electronics Engineers Inc. (2017)

    Google Scholar 

  23. Ricciardi, C., Cuocolo, R., Cesarelli, G., Ugga, L., Improta, G., Solari, D., Romeo, V., Guadagno, E., Zuluaga Velez, M.C.L., Cesarelli, M.: Distinguishing Functional from Non-functional Pituitary Macroadenomas with a Machine Learning Analysis. Springer, Heidelberg (2020)

    Google Scholar 

  24. Improta, G., Guizzi, G., Ricciardi, C., Giordano, V., Ponsiglione, A.M., Converso, G., Triassi, M.: Agile six sigma in healthcare: case study at Santobono pediatric hospital. Int. J. Environ. Res. Public. Health. 17 (2020). https://doi.org/10.3390/ijerph17031052

  25. Romano, M., Bifulco, P., Ponsiglione, A.M., Gargiulo, G.D., Amato, F., Cesarelli, M.: Evaluation of floatingline and foetal heart rate variability. Biomed. Signal Process. Control 39, 185–196 (2018). https://doi.org/10.1016/j.bspc.2017.07.018

    Article  Google Scholar 

  26. Romano, M., D’Addio, G., Clemente, F., Ponsiglione, A.M., Improta, G., Cesarelli, M.: Symbolic dynamic and frequency analysis in foetal monitoring. In: 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–5 (2014)

    Google Scholar 

  27. Improta, G., Ricciardi, C., Amato, F., D’Addio, G., Cesarelli, M., Romano, M.: Efficacy of machine learning in predicting the kind of delivery by cardiotocography. Springer (2020)

    Google Scholar 

  28. Stanzione, A., Ricciardi, C., Cuocolo, R., Romeo, V., Petrone, J., Sarnataro, M., Mainenti, P.P., Improta, G., De Rosa, F., Insabato, L., Brunetti, A., Maurea, S.: MRI Radiomics for the prediction of Fuhrman grade in clear cell renal cell carcinoma: a machine learning exploratory study. J. Digit. Imaging. (2020). https://doi.org/10.1007/s10278-020-00336-y

  29. Ricciardi, C., Cantoni, V., Improta, G., Iuppariello, L., Latessa, I., Cesarelli, M., Triassi, M., Cuocolo, A.: Application of data mining in a cohort of Italian subjects undergoing myocardial perfusion imaging at an academic medical center. Comput. Methods Programs Biomed. 189, 105343–105349 (2020). https://doi.org/10.1016/j.cmpb.2020.105343

    Article  Google Scholar 

  30. Improta, G., Ricciardi, C., Cesarelli, G., D’Addio, G., Bifulco, P., Cesarelli, M.: Machine learning models for the prediction of acuity and variability of eye-positioning using features extracted from oculography. Health Technol. 10, 961–968 (2020). https://doi.org/10.1007/s12553-020-00449-y

    Article  Google Scholar 

  31. Ricciardi, C., Improta, G., Amato, F., Cesarelli, G., Romano, M.: Classifying the type of delivery from cardiotocographic signals: a machine learning approach. Comput. Methods Programs Biomed. 196, 105712 (2020). https://doi.org/10.1016/j.cmpb.2020.105712

    Article  Google Scholar 

  32. Pintaudi, B.: Gli Standard Italiani 2018 Per La Terapia Del Diabete Mellito The 2018 Italian Standards for the treatment of diabetes mellitus. G. Ital. Farm. E Farm. 10, 5–14 (2018)

    Google Scholar 

  33. Zocchetti, C., Merlino, L., Agnello, M., Bragato, D.: Una nuova proposta per la cronicità: i CReG (Chronic Related Group). Tend. Nuove. 11, 377–398 (2011)

    Google Scholar 

  34. Bonora, E., Sesti, G.: Il diabete in Italia. Soc. Ital. Diabetol. (2016)

    Google Scholar 

  35. Baggiore, C., Calcaterra, F., Ciullo, I., Di Seclì, C., Falasca, P., Nogara, A., Baccetti, F.: Griglia per la valutazione di appropriatezza dei Percorsi Dia-gnostici Terapeutici Assistenziali (PDTA) per il diabete mellito

    Google Scholar 

  36. Sociosanitaria, P.S., Cecchi, A., Matteotti, E.A., Diabetici, R.P., Roberto Da Ros, A.: Linee Di Indirizzo Regionali Per La Gestione Dell’iperglicemia E Del Diabete In Ospedale (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ida Santalucia .

Editor information

Editors and Affiliations

Ethics declarations

The authors have no conflict of interest to declare.

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Improta, G. et al. (2021). Management of the Diabetic Patient in the Diagnostic Care Pathway. In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds) 8th European Medical and Biological Engineering Conference. EMBEC 2020. IFMBE Proceedings, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-64610-3_88

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-64610-3_88

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64609-7

  • Online ISBN: 978-3-030-64610-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics