Predictive Model Development: Identifying high-risk patients in primary care attendance

1. Antonio Sarría Santamera Mª Auxiliadora Martín Martínez Mª del Rocío Carmona Alférez Pilar Gallego Berciano Predictive Model Development: Identifying high-risk patients in primary care attendance Financed with the help PI 06/1122 (FIS) of ISCIII AGENCY EVALUATION OF HEALTH TECHNOLOGIES INSTITUTO DE SALUD CARLOS III

2. Sandín Vázquez M Conde Espejo P de Bustos Guadaño M Asunsolo del Barco A Riesgo Fuertes R Garrido Elustondo S Cabello Ballesteros ML Escortell Mayor ME Sanz Cuesta T Utilization of Primary Health CareResearch Group Calvo Parra I Villaitodo Villén P Bartolomé Casado MS Jiménez Carramiñana J Casado López M Parralejo Buendía M Basanta López M Bonache Blay M Martínez-Toledano Olaya P Rico Blázquez M

3. INTRODUCTION • High frequency of attendance is one of the main problems in PC. • Excessive use has been linked to a limited number of patients who have been called frequent user. • Who are they? How many are they?

4. INTRODUCTION • There is no agreement on the definition of frequent users. • We propose that the level of use should be linked to the patient risk profile of patients. • Overuse will suppose that patient have a higher number of visits than expected given their risk profile.

5. OBJECTIVES 1. To identify factors associated with the use of visits to primary care physicians. 2. To assess the frequency of over-use.

6. Health areas: 1, 3, 7, 8, 9 y 10 METHODOLOGY • Design. location and sources of information • Design: transversal. observational and ecological. • Location: 6 health areas in the Community of Madrid. • Sources of information: - Electronic medical records of Primary Care (OMI-AP). - Institute of Statistics of the Community of Madrid.

7. METHODOLOGY • Study patients: • INCLUSION CRITERIA: • Registration in one of the 6 Health Areas • > 24 years in 2006 • At least one visit to the clinic in 2006 • EXCLUSION CRITERIA: • Physician assigned to the “traditional model” • Study period: January 1, 2006 - December 3, 2007

8. PATIENTS OVER 24 YEARS WITH AT LEAST ONE VISIT TO THE CENTRE OF HEALTH IN 2006 IN 6 HEALTH AREAS (1.325.327) DUPLICATE PATIENTS (7.307) PATIENTS INCLUDED IN STUDY (1.318.020) METHODOLOGY • Flowchart of patient selection

9. METHODOLOGY

10. SOCIO-ECONOMIC DATA Patient Per Capita Gross Disposable Income in 2000 of the basic health area METHODOLOGY

11. METHODOLOGY • DATA ANALYSIS • DESCRIPTIVE ANALYSIS • Frequency analysis • Correlation analysis • Detecting Multicollinearity • Contingency tables

12. DATA ANALYSIS • DEVELOPMENT OF RISK ADJUSTMENT MODELS: MULTILEVEL REGRESSION Characterized by: hierarchical clustering of variables • LEVEL 1: PATIENTS • LEVEL 2: PC Teams • DEPENDENT VARIABLE: • Total number of visits to PC physicians in 2007 • The variability in total of visits to PC physicians are due to differences between patients and differences between PC Teams METHODOLOGY

13. METHODOLOGY MULTILEVEL MODEL: • INDEPENDENT VARIABLES OF PATIENT

14. Use of Health Service • Total of visits to PC physicians in 2006 • Total nursing consultations in 2006 • Analytical • Use of Health Service • Total of visits to PC physicians in 2006 • Total nursing consultations in 2006 • Analytical • Radiology tests • Referrals to other specialists • Radiology tests • Referrals to other specialists • Morbidity • Nonspecific problems • Digestive disorders • Ophthalmic conditions • ENT conditions • HBP • Phlebitis • Heart failure / arrhythmias • Ischemic heart disease / Stroke • Osteoarthritis / osteoporosis • Another bone-joint conditions • Headache • Vertigo/ dizziness • Anxiety / depression • COPD / Asthma • Allergic rhinitis • Other respiratory disease • Skin / appendages • Diabetes • Obesity • Lipid disorder • Cancer • Anemia • HIV • Pulmonary embolism • Peripheral neuropathy • Schizophrenia • Temporary Disability METHODOLOGY • Sociodemográficas • Edad 2. Sexo 3. Problemas Sociales • Sociodemográficas • Edad 2. Sexo 3. Problemas Sociales • Sociodemográficas • Edad 2. Sexo 3. Problemas Sociales • Sociodemographic • Age 2. Sex 3. Social Problems

15. METHODOLOGY MULTILEVEL MODEL : • INDEPENDENT VARIABLES OF PC Team

16. METHODOLOGY • Características y Capacidad Organizativa del Sistema Sanitario • Tipo de EAP: Rural/ Urbano • Turno del EAP: Mañana y Tarde/ Mañana o Tarde • PAMED • PAENF • % de pacientes con edad ≥ 65 años del EAP • Organizational Characteristics and Health System Capacity • Type of PC Team: Rural / Urban • Time Team's primary care: Morning and Evening / Morning or Afternoon • Pressure care average PC physician PC Team • Pressure half of nursing care of PC Team • % of patients aged ≥ 65 years of PC Team Socio-economic Per capita disposable income 2000

17. METHODOLOGY • DATA ANALYSIS • RISK ADJUSTED ATTENDANCE RATIO • Estimated values were obtained for each patient according to the risk adjustment model.

18. METHODOLOGY • DATA ANALYSIS • RISK ADJUSTED ATTENDANCE RATIO • For each patient we calculated the difference between the actual number of visits to PC physician in 2007 and the value estimated by the model: Difference = OBSERVED - ESTIMATED OBSERVED – ESTIMATED > 0  PATIENT OVER-USER OBSERVED – ESTIMATED < 0  PATIENT INFRA-USER

19. METHODOLOGY • DATA ANALYSIS • 3. RISK ADJUSTED ATTENDANCE RATIO • This approach allowed to obtained the observed/expeced number of visits for each PC Team. The total number of visits to PC in 2007 of these patients and the estimated total number of consultations by the model.

20. METHODOLOGY • DATA ANALYSIS • 3. RISK ADJUSTED ATTENDANCE RATIO

21. RESULTS

22. RESULTS

23. RESULTS

24. RESULTS

25. RESULTS • 53% of patients visits their primary care physician more than estimated according to their risk. • 40% visits less primary care physicians than expected.

26. CONCLUSIONS • There is a significant amount of visits to PC physician which are not explained by the risk of patients or characteristics of PC Teams. • Overall, the number of visits of over-users exceeds that of infra-users.

27. THANK YOU