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Knowledge-Based Personal Health System to Empower Outpatients of Diabetes Mellitus by Means of P4 Medicine

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Data Mining in Clinical Medicine

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1246))

Abstract

Diabetes Mellitus (DM) affects hundreds of millions of people worldwide and it imposes a large economic burden on healthcare systems. We present a web patient empowering system (PHSP4) that ensures continuous monitoring and assessment of the health state of patients with DM (type I and II). PHSP4 is a Knowledge-Based Personal Health System (PHS) which follows the trend of P4 Medicine (Personalized, Predictive, Preventive, and Participative). It provides messages to outpatients and clinicians about the achievement of objectives, follow-up, and treatments adjusted to the patient condition. Additionally, it calculates a four-component risk vector of the associated pathologies with DM: Nephropathy, Diabetic retinopathy, Diabetic foot, and Cardiovascular event. The core of the system is a Rule-Based System which Knowledge Base is composed by a set of rules implementing the recommendations of the American Diabetes Association (ADA) (American Diabetes Association: http://www.diabetes.org/) clinical guideline. The PHSP4 is designed to be standardized and to facilitate its interoperability by means of terminologies (SNOMED-CT [The International Health Terminology Standards Development Organization: http://www.ihtsdo.org/snomed-ct/] and UCUM [The Unified Code for Units of Measure: http://unitsofmeasure.org/]), standardized clinical documents (HL7 CDA R2 [Health Level Seven International: http://www.hl7.org/index.cfm]) for managing Electronic Health Record (EHR). We have evaluated the functionality of the system and its users’ acceptance of the system using simulated and real data, and a questionnaire based in the Technology Acceptance Model methodology (TAM). Finally results show the reliability of the system and the high acceptance of clinicians.

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Notes

  1. 1.

    World Health Organization (WHO/OMS): http://www.who.int/diabetes/en/

  2. 2.

    Health Level Seven Clinical Document Architecture standard: http://www.hl7.org/implement/standards/cda.cfm

  3. 3.

    The International Health Terminology Standards Development Organization: http://www.ihtsdo.org/snomed-ct/

  4. 4.

    P4 Medicine Institute: http://p4mi.org/

  5. 5.

    http://www.w3.org/TR/soap/

  6. 6.

    http://www.oracle.com/technetwork/java/javaee/jsp/index.html

  7. 7.

    Microsoft Health Common User Interface guidance overview: http://www.mscui.net/DesignGuide/DesignGuide.aspx

  8. 8.

    http://www.fagor.com/web/es/home

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Acknowledgements

We thank Dr. Tomás Fuster and his team from Centro de Salud Gandia—Beniopa for their collaboration and clinical support during the development of the system. We thank the collaboration from Universidad de Mondragon, specially, Urtzi Markiegi. We also thank Fagor Electrodomésticos S.CoopFootnote 8 for their support and collaboration in the development of this work, especially to Juan Ramón Inurria and Jorge de Antonio Prieto. Regarding the clinical evaluation, we thank the collaboration of Dr. Pablo Díaz-Munio from Medicalquatro (Madrid); Dr. Alejandro Rodríguez from Hospital Virgen del Castillo (Yecla); Dr. Francisca Moreno from Hospital La Fe (Valencia); Dr. Llucia Palacios from Verge dels Lliris (Alcoy); Dr. Tomás Fuster and Dr. Pilar Alonso from Centro de Salud Gandia—Beniopa; and Dr. Inmaculada Ibáñez from Centro de Salud Laboral UPV (Valencia). This work was funded by the Ministerio de Ciencia e Innovación of Spain (INNPACTO 2011 ref. IPT-2011-1087-900000).

Author information

Authors and Affiliations

  1. Grupo de Informática Biomédica, Instituto ITACA, Universitat Politècnica de València, Edificio 8G, Camino de Vera, S/N, 46022, València, Spain

    Adrián Bresó, Carlos Sáez, Montserrat Robles & Juan Miguel García-Gómez

  2. Image Analysis, LTD., Victoria, SW1, London, UK

    Javier Vicente

  3. Elektronika eta Informatika saila, Mondragon Goi Eskola Politeknikoa, Arrasate-Mondragon, Spain

    Félix Larrinaga

Authors
  1. Adrián Bresó
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  2. Carlos Sáez
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  3. Javier Vicente
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  4. Félix Larrinaga
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  5. Montserrat Robles
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  6. Juan Miguel García-Gómez
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Corresponding author

Correspondence to Adrián Bresó .

Editor information

Editors and Affiliations

  1. Instituto Itaca, Universitat Politècnica de València, Valencia, Spain

    Carlos Fernández-Llatas

  2. Instituto Itaca, Universitat Politècnica de València, Valencia, Spain

    Juan Miguel García-Gómez

Appendices

Appendix 1: Clinical Profiles Defined by Expert Clinicians

Profile

Patient

Stages

1

Gender: Male

Age: 56

DM: Type II (1 year)

Stage 1: Good health.

Stage 2: He gains weight and the BMI rises to 25.

Stage 3: The lipid profile gets worse.

Stage 4: The lipid profile gets better.

2

Gender: Male

Age: 56

DM: Type II (1 year)

Stage 1: Smoking patient.

Stage 2: He starts a program to quit smoking.

Stage 3: The diabetic foot risk is 2.

Stage 4: He finally quits smoking.

3

Gender: Female

Age: 40

DM: Type II (1 year)

Stage 1: Good health.

Stage 2: The lipid profile gets worse.

Stage 3: The lipid profile gets worse.

Stage 4: The lipid profile improves, but not to desirable levels.

4

Gender: Female

Age: 40

DM: Type II (1 year)

Stage 1: Good health.

Stage 2: The blood pressure (BP) gets worse.

Stage 3: The BP gets worse until reaching hypertension.

5

Gender: Female

Age: 60

DM: Type II (10 years)

Stage 1: Good health.

Stage 2: The BP get worse.

Stage 3: The BP gets worse until reaching hypertension and albuminuria.

6

Gender: Female

Age: 60

DM: Type II (1 year)

Stage 1: Good health.

Stage 2: The glycemic targets gets worse.

Stage 3: The glycemic targets improve.

7

Gender: Male

Age: 31

DM: Type I (3 years)

Stage 1: Good health. He is smoker and he is in lifestyle therapy.

Stage 2: After 3 months he has hypoglycemia and high BP.

Stage 3: Hypoglycemia continues. Hypertension was resolved after administering medication treatment. He keeps smoking.

8

Gender: Male

Age: 45

DM: Type I (10 years)

Stage 1: He is in treatment. He suffers hypertension and he has risk of coronary artery disease and retinopathy.

Stage 2: After six months, he suffers myocardial infarction (MI).

Stage 3: After six months, the patient changes his current treatment (angiotensin II receptor antagonist treatment) by enzyme inhibitor of angiotensin converting treatment. Additionally, he is treated with metformin and beta blockers. The hypertension improves.

9

Gender: Female

Age: 55

DM: Type II (22 years)

Stage 1: She is in treatment. She is obese and she has hypertension. She has altered sensitivity on her feet, kidney damage and glomerular count below 90.

Stage 2: After six months, the glomerular count (nephropathy) gets worse. She develops calluses and deformities in one foot. Her vision also starts to be affected, which confirms that she has a diabetic retinopathy.

Stage 3: A year later, the lipids are not controlled. The patient has proliferative diabetic retinopathy. The glomerular count significantly dropped and finally requires dialysis. She develops foot ulcers.

10

Gender: Female

Age: 28

DM: Type I (5 years)

Stage 1: She is in treatment. She suffers microalbuminuria, hyperglycemia and hypertension.

Stage 2: The patient changes her current treatment (angiotensin II receptor antagonist treatment) by enzyme inhibitor of angiotensis converting treatment and hypertension improves. The microalbuminuria becomes macroalbuminuria with renal failure.

Stage 3: Treatment keeps desired lipid levels and BP. The macroalbuminuria becomes microalbuminuria.

Appendix 2: TAM Questionnaire

  1. Q1.

    The new tool makes my work of integral monitoring of diabetic patients easier.

  2. Q2.

    The new tool allows me to be productive.

  3. Q3.

    The new tool allows me to be effective in the integral monitoring of diabetic patients.

  4. Q4.

    The new tool allows me to accomplish my tasks quickly.

  5. Q5.

    The new tool allows me to provide a quality service of integral monitoring of diabetic patients.

  6. Q6.

    I consider useful the new tool in my work in order to monitor diabetic patients.

  7. Q7.

    Learning to use the tool was easy for me.

  8. Q8.

    I think that with the new tool is easy to get what I propose to do.

  9. Q9.

    My interaction with the new tool is clear and understand its operation.

  10. Q10.

    The interaction with the new tool is flexible.

  11. Q11.

    Currently I am skillful using the new tool.

  12. Q12.

    I believe that the new tool is easy to use.

  13. Q13.

    The new tool provides me access to clinical documentation of patient alerts.

  14. Q14.

    The new tool allows me to observe the causes and recommendations regarding the current status of the disease.

  15. Q15.

    The new tool allows me to quickly observe pathological risks associated with the patient's situation.

  16. Q16.

    Which improvements would you include in order to make the new tool more useful in your work of integral monitoring of diabetic patients?

  17. Q17.

    Which improvements would you include in order to make the new tool easier to use?

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Bresó, A., Sáez, C., Vicente, J., Larrinaga, F., Robles, M., García-Gómez, J.M. (2015). Knowledge-Based Personal Health System to Empower Outpatients of Diabetes Mellitus by Means of P4 Medicine. In: Fernández-Llatas, C., García-Gómez, J. (eds) Data Mining in Clinical Medicine. Methods in Molecular Biology, vol 1246. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1985-7_15

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  • DOI: https://doi.org/10.1007/978-1-4939-1985-7_15

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-1984-0

  • Online ISBN: 978-1-4939-1985-7

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