Prediction of the increase in health services demand based on the analysis of reasons of calls received by a customer relationship management.

Currently, customer relationship management (CRM) tools are very important in our society because they provide a comunication channel to the healthcare system for patients. Salud Responde is a CRM that provides many health services for the entire population of Andalusia, in southern Spain. The number and frequenzy of phone calls received change along the year. They depend on many factors, such as weekdays, seasons, vaccination campaigns, environmental factors, pandemic periods, etc. All these are the main reasons number of health calls changes along the year. This variability makes that the current management of resources for offering emergency services based on historical data is inefficient. The factors, which influence the phone calls along the year, are different from one period to another. Therefore, it is clear to demand an improved in the current management system. In this context, the main goal for this research is to develop an expert system able to identify and analyze, using different data mining algorithms, the most relevant factors to predict the variability of health service demand. Thus, here, it is proposed a methodology in which using reasons calls received in the CRM as input data, it is possible to predict in advance the healthcare resources demand.

Improvement of the Prediction of Drugs Demand Using Spatial Data Mining Tools.

The continued availability of products at any store is the major issue in order to provide good customer service. If the store is a drugstore this matter reaches a greater importance, as out of stock of a drug when there is high demand causes problems and tensions in the healthcare system. There are numerous studies of the impact this issue has on patients. The lack of any drug in a pharmacy in certain seasons is very common, especially when some external factors proliferate favoring the occurrence of certain diseases. This study focuses on a particular drug consumed in the city of Jaen, southern Andalucia, Spain. Our goal is to determine in advance the Salbutamol demand. Advanced data mining techniques have been used with spatial variables. These last have a key role to generate an effective model. In this research we have used the attributes that are associated with Salbutamol demand and it has been generated a very accurate prediction model of 5.78% of mean absolute error. This is a very encouraging data considering that the consumption of this drug in Jaen varies 500% from one period to another.

[Importance of health CRM in pandemics and health alerts]. / Importancia de los Customer Relationship Management (CRM) sanitarios en las pandemias y alertas sanitarias.

OBJECTIVE: The aim of this article is to demonstrate the importance of the role a health CRM can play in a pandemic or health alert. During the influenza-A pandemic, Salud Responde played a very important role. Its main objective was to establish protocols and citizens advice lines that would avoid patients with mild influenza-A symptoms going to health centre. DESIGN: A triage system was developed around the Siebel CRM (software tool) to achieve this objective. This allowed the Salud Responde staff to establish the severity of the patient depending on the symptoms and the risk factors of the patient, as well as being able to inform, give health advice or refer the patient to medical centres if necessary. SETTING: All patients (a total of 56,497) who were attended by Salud Responde within its influenza-A service portfolio have been included. PARTICIPANTS: Patients who were attended by Salud Responde. MAIN MEASUREMENTS: The data have been extracted from the Salud Responde data base. RESULTS: Salud Responde attended to 56,497 patients during the influenza-A pandemic, of whom 48,287 patients did not require health care. CONCLUSIONS: Salud Responde attended to 56,497 patients, of whom 48,287 patients did not require health care. Apart from any financial savings that this could entail, it contributed to minimising the pandemic, avoiding the patient having to go to a health centre to receive medical care or information, and prevented, to a great extent, the flooding of casualty departments.

An improvement in the appointment scheduling in primary health care centers using data mining.

An optimal resource management in health care centers implies the use of an appropriate timetabling scheme to schedule appointments. Timetables of health centers are usually divided into time slots whose duration is equal to time required for clinical attendance. However doctors perform a series of tasks that are not always clinical in nature: issuing prescriptions or prescribing sick leave certificates. In this sense the time spent in attending a clinical or an administrative matter is different. This last required less time to attend the patient. This study is focused in the administrative task. A predictive model is generated to provide daily information on how many patients will go to the health center for an administrative issue. The accuracy of the model is less than 4,6 % absolute error and the improvement in scheduling appointments is a time saving of 21,73 %.

[Use of customer relationship management to improve healthcare for citizens. The 24h Andalusian Health Service: Healthline]. / Uso de customer relationship management para mejorar la atención sanitaria de la ciudadanía. Servicio Salud Andalucía 24 horas. Salud Responde.

Salud Responde (in English: Healthline) is a Health Service and Information Centre of the taxpayer-funded Andalusian Health System (AHS) that offers a Telephone Health Advisory Service called SA24h, among other services. The main objective of SA24h is to inform and advise citizens on health issues and the available health resources of the AHS. SA24h has a Customer Relationship Management information technology tool that organises information at various levels of specialization. Depending on the difficulty of the query, the citizen is attended by professionals with distinct profiles, providing a consensual response within the professionals working within Salud Responde or within other healthcare levels of the AHS. SA24h provided responses to 757,168 patient queries from late 2008 to the end of 01/12/2015. A total of 9.38% of the consultations were resolved by the non-health professionals working at Salud Responde. The remaining 84.07% were resolved by health staff. A total of 6.5% of users were referred to accident and emergency facilities while 88.77% did not need to attend their general practitioner within the next 24hours, thus avoiding unnecessary visits to health care facilities.

Uso de customer relationship management para mejorar la atención sanitaria de la ciudadanía. Servicio Salud Andalucía 24 horas. Salud Responde / Use of customer relationship management to improve healthcare for citizens. The 24h Andalusian Health Service: Healthline

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Importancia de los Customer Relationship Management (CRM) sanitarios en las pandemias y alertas sanitarias / Importance of health CRM in pandemics and health alerts

OBJETIVO: El objetivo es demostrar la importancia que puede tener un CRM sanitario en una pandemia o alerta sanitaria. Durante la pandemia de la gripe A, Salud Responde jugó un papel muy importante; su principal objetivo era establecer unos protocolos y la atención al ciudadano que evitara que pacientes con sintomatología leve de gripe A se desplazaran a los centros de salud. DISEÑO: Para lograr este objetivo se desarrolló sobre el CRM de Siebel (herramienta informática) unos triajes que permitieron al personal de Salud Responde, en función de los síntomas del paciente y sus factores de riesgo, establecer la gravedad de este, y así poder informar, dar consejos sanitarios o derivar al paciente a los centros médicos en caso de necesidad. Emplazamiento: En este estudio se ha tenido en cuenta a todos los pacientes que fueron atendidos por Salud Responde en su cartera de servicios de gripe A. En total fueron atendidos 56.497 pacientes. PARTICIPANTES: Pacientes que fueron atendidos por Salud Responde. MEDICIONES PRINCIPALES: Los datos han sido extraídos de las bases de datos de Salud Responde. RESULTADOS: En el caso de la pandemia de la gripe A, Salud Responde atendió a 56.497 pacientes, de los que 48.287 no requirieron atención sanitaria. CONCLUSIONES: Salud Responde atendió a 56.497 pacientes, de los cuales 48.287 no requirieron atención sanitaria. Aparte del posible ahorro económico que esto pudo suponer, contribuyó a minimizar la pandemia, evitando que pacientes con sintomatología leve fueran a su centro de salud para recibir atención médica o información, y evitó en gran medida el desbordamiento de las urgencias

OBJECTIVE: The aim of this article is to demonstrate the importance of the role a health CRM can play in a pandemic or health alert. During the influenza-A pandemic, Salud Responde played a very important role. Its main objective was to establish protocols and citizens advice lines that would avoid patients with mild influenza-A symptoms going to health centre. DESIGN: A triage system was developed around the Siebel CRM (software tool) to achieve this OBJECTIVE: This allowed the Salud Responde staff to establish the severity of the patient depending on the symptoms and the risk factors of the patient, as well as being able to inform, give health advice or refer the patient to medical centres if necessary. SETTING: All patients (a total of 56,497) who were attended by Salud Responde within its influenza-A service portfolio have been included. PARTICIPANTS: Patients who were attended by Salud Responde. MAIN MEASUREMENTS: The data have been extracted from the Salud Responde data base. RESULTS: Salud Responde attended to 56,497 patients during the influenza-A pandemic, of whom 48,287 patients did not require health care. CONCLUSIONS: Salud Responde attended to 56,497 patients, of whom 48,287 patients did not require health care. Apart from any financial savings that this could entail, it contributed to minimising the pandemic, avoiding the patient having to go to a health centre to receive medical care or information, and prevented, to a great extent, the flooding of casualty departments