ical material & consumables cost related to an event of specific DRG

Analysis on the pharmaceutical cost related to an event of specific DRG

Delays in payment and collection levels

Department related analysis:

• Number of treatment events / medical interventions per Department
• comparison of same specialty Departments

The star schema could also capture the involvement and performance of medical Professionals. In addition it could capture many more facts related to the event like manhours spent by the Doctor in charge, by the other professionals involved. The datamart can be linked to the datamart of another Hospital or Healthcare system, if the two use conformed dimensions and facts. Standardized codifications (like ICD) aim at achieving this conformance, in order to be able to consolidate information at the regional, national and international level. Why develop the star schema, instead of querying directly the Hospital Information SystemThe main goal of each Healthcare Institution in a highly controlled & competitive environment, is to reduce operating costs while maintaining a consistently acceptable level of patient treatment. Reduce operating costs at all levels:

• Cost of healthcare Professionals
• Cost of lab equipment & consumables
• Cost of pharmaceuticals / medical material
• Cost of a treatment per Diagnosis related grouping (DRG)
• Cost per type of medical intervention (e.g. specific medical operation)

On the other hand, an acceptable level of patient treatment involves:

• Evidence based medicine, accurate diagnosis and efficient treatment
• On time admittance in the Hospital and healthcare treatment
• Treatment with respect for the Patient- analysis of options
• Reduction of risks during treatment (e.g. related to the use of medicine, biomedical equipment, blood transfusions)
• Capture of medical history of the patient in order to support evidence based medicine

Moreover, goals of each Healthcare Institution are:

• Reduction of medical errors and exposure of the patient to medical hazards (e.g. inappropriate levels of radiation)
• Support medical research with patient & treatment data
• Participate and support a larger Healthcare system, with the exchange of medical information on a patient, as well as statistics on population morbidity & mortality.

In Private Healthcare, the excellent Patient service is critical to Customer retention & loyalty and business growth. In order to achieve these goals, each modern Healthcare system aims to enhance its Organisational capability with the introduction of standard business processes, standard healthcare treatment based on standardized healthcare interventions. Medical information management is supported by the introduction of information systems aligned to the standardized processes as well as the introduction of classifications or codifications of certain information types like diseases (based on ICD), medical interventions, lab exams, biomedical equipment & consumables. Hospital information systems (HIS) are used to capture and process all information related to the administrative as well as the medical aspects of a patient event. A patient record is created which stores all the patient history, structured per event. A datamart monitoring the inpatient and outpatient service could be based on data retrieved from the HIS database. In a dimensional model it would store the dimensions involved in the service: Patient, Date, Healthcare Unit and Department, exit DRG (diagnosis related grouping) group, Diagnosis (based on ICD), Medical intervention based on selected codification, pharmaceutical treatment, medical material used, and capture all cost related facts in the fact table. The star schema captures information related to an inpatient event, which normally may last few days. In order to capture the whole lifecycle of the event, an accumulating snapshot fact table is used, as shown in the figure (check link below). Based on this star schema, a wide range of analytical tasks can be carried out. Analysis related to medical treatment of a specific event (indicatively):

• Produce a medical history report on a patient
• Produce stats on morbidity by restricting on specific ICD codes (e.g. frequency of an ICD as a percentage of total events, ICD related to demographic info like age or educational level)
• Analyse the relation of medical interventions to ICD & DRGs, by restricting on specific DRGs
• Mortal events per ICD / DRG (this can be coded in a specific Exit_diagnosis value)

Analysis related to cost incurred by a specific event (indicatively):

• Analysis on the average length of stay (ALOS) per DRG as well as per Patient demographics.
• Analysis on the medical material & consumables cost related to a medical intervention
• Analysis on the medical material & consumables cost related to an event of specific DRG
• Analysis on the pharmaceutical cost related to an event of specific DRG
• Delays in payment and collection levels

Department related analysis:

• Number of treatment events / medical interventions per Department
• comparison of same specialty Departments

The star schema could also capture the involvement and performance of medical Professionals. In addition it could capture many more facts related to the event like manhours spent by the Doctor in charge, by the other professionals involved. The datamart can be linked to the datamart of another Hospital or Healthcare system, if the two use conformed dimensions and facts. Standardized codifications (like ICD) aim at achieving this conformance, in order to be able to consolidate information at the regional, national and international level. Why develop the star schema, instead of querying directly the Hospital Information System o support evidence based medicineMoreover, goals of each Healthcare Institution are:

• Reduction of medical errors and exposure of the patient to medical hazards (e.g. inappropriate levels of radiation)
• Support medical research with patient & treatment data
• Participate and support a larger Healthcare system, with the exchange of medical information on a patient, as well as statistics on population morbidity & mortality.

In Private Healthcare, the excellent Patient service is critical to Customer retention & loyalty and business growth. In order to achieve these goals, each modern Healthcare system aims to enhance its Organisational capability with the introduction of standard business processes, standard healthcare treatment based on standardized healthcare interventions. Medical information management is supported by the introduction of information systems aligned to the standardized processes as well as the introduction of classifications or codifications of certain information types like diseases (based on ICD), medical interventions, lab exams, biomedical equipment & consumables. Hospital information systems (HIS) are used to capture and process all information related to the administrative as well as the medical aspects of a patient event. A patient record is created which stores all the patient history, structured per event. A datamart monitoring the inpatient and outpatient service could be based on data retrieved from the HIS database. In a dimensional model it would store the dimensions involved in the service: Patient, Date, Healthcare Unit and Department, exit DRG (diagnosis related grouping) group, Diagnosis (based on ICD), Medical intervention based on selected codification, pharmaceutical treatment, medical material used, and capture all cost related facts in the fact table. The star schema captures information related to an inpatient event, which normally may last few days. In order to capture the whole lifecycle of the event, an accumulating snapshot fact table is used, as shown in the figure (check link below). Based on this star schema, a wide range of analytical tasks can be carried out. Analysis related to medical treatment of a specific event (indicatively):

• Produce a medical history report on a patient
• Produce stats on morbidity by restricting on specific ICD codes (e.g. frequency of an ICD as a percentage of total events, ICD related to demographic info like age or educational level)
• Analyse the relation of medical interventions to ICD & DRGs, by restricting on specific DRGs
• Mortal events per ICD / DRG (this can be coded in a specific Exit_diagnosis value)

Analysis related to cost incurred by a specific event (indicatively):

• Analysis on the average length of stay (ALOS) per DRG as well as per Patient demographics.
• Analysis on the medical material & consumables cost related to a medical intervention
• Analysis on the medical material & consumables cost related to an event of specific DRG
• Analysis on the pharmaceutical cost related to an event of specific DRG
• Delays in payment and collection levels

Department related analysis:

• Number of treatment events / medical interventions per Department
• comparison of same specialty Departments

The star schema could also capture the involvement and performance of medical Professionals. In addition it could capture many more facts related to the event like manhours spent by the Doctor in charge, by the other professionals involved. The datamart can be linked to the datamart of another Hospital or Healthcare system, if the two use conformed dimensions and facts. Standardized codifications (like ICD) aim at achieving this conformance, in order to be able to consolidate information at the regional, national and international level. Why develop the star schema, instead of querying directly the Hospital Information Systemor codifications of certain information types like diseases (based on ICD), medical interventions, lab exams, biomedical equipment & consumables. Hospital information systems (HIS) are used to capture and process all information related to the administrative as well as the medical aspects of a patient event. A patient record is created which stores all the patient history, structured per event. A datamart monitoring the inpatient and outpatient service could be based on data retrieved from the HIS database. In a dimensional model it would store the dimensions involved in the service: Patient, Date, Healthcare Unit and Department, exit DRG (diagnosis related grouping) group, Diagnosis (based on ICD), Medical intervention based on selected codification, pharmaceutical treatment, medical material used, and capture all cost related facts in the fact table. The star schema captures information related to an inpatient event, which normally may last few days. In order to capture the whole lifecycle of the event, an accumulating snapshot fact table is used, as shown in the figure (check link below). Based on this star schema, a wide range of analytical tasks can be carried out. Analysis related to medical treatment of a specific event (indicatively):

• Produce a medical history report on a patient
• Produce stats on morbidity by restricting on specific ICD codes (e.g. frequency of an ICD as a percentage of total events, ICD related to demographic info like age or educational level)
• Analyse the relation of medical interventions to ICD & DRGs, by restricting on specific DRGs
• Mortal events per ICD / DRG (this can be coded in a specific Exit_diagnosis value)

Analysis related to cost incurred by a specific event (indicatively):

• Analysis on the average length of stay (ALOS) per DRG as well as per Patient demographics.
• Analysis on the medical material & consumables cost related to a medical intervention
• Analysis on the medical material & consumables cost related to an event of specific DRG
• Analysis on the pharmaceutical cost related to an event of specific DRG
• Delays in payment and collection levels

Department related analysis:

• Number of treatment events / medical interventions per Department
• comparison of same specialty Departments

The star schema could also capture the involvement and performance of medical Professionals. In addition it could capture many more facts related to the event like manhours spent by the Doctor in charge, by the other professionals involved. The datamart can be linked to the datamart of another Hospital or Healthcare system, if the two use conformed dimensions and facts. Standardized codifications (like ICD) aim at achieving this conformance, in order to be able to consolidate information at the regional, national and international level. Why develop the star schema, instead of querying directly the Hospital Information Systemle of the event, an accumulating snapshot fact table is used, as shown in the figure (check link below). Based on this star schema, a wide range of analytical tasks can be carried out. Analysis related to medical treatment of a specific event (indicatively):

• Produce a medical history report on a patient
• Produce stats on morbidity by restricting on specific ICD codes (e.g. frequency of an ICD as a percentage of total events, ICD related to demographic info like age or educational level)
• Analyse the relation of medical interventions to ICD & DRGs, by restricting on specific DRGs
• Mortal events per ICD / DRG (this can be coded in a specific Exit_diagnosis value)

Analysis related to cost incurred by a specific event (indicatively):

• Analysis on the average length of stay (ALOS) per DRG as well as per Patient demographics.
• Analysis on the medical material & consumables cost related to a medical intervention
• Analysis on the medical material & consumables cost related to an event of specific DRG
• Analysis on the pharmaceutical cost related to an event of specific DRG
• Delays in payment and collection levels

Department related analysis:

• Number of treatment events / medical interventions per Department
• comparison of same specialty Departments

The star schema could also capture the involvement and performance of medical Professionals. In addition it could capture many more facts related to the event like manhours spent by the Doctor in charge, by the other professionals involved. The datamart can be linked to the datamart of another Hospital or Healthcare system, if the two use conformed dimensions and facts. Standardized codifications (like ICD) aim at achieving this conformance, in order to be able to consolidate information at the regional, national and international level. Why develop the star schema, instead of querying directly the Hospital Information Systemical material & consumables cost related to an event of specific DRG

Analysis on the pharmaceutical cost related to an event of specific DRG

Delays in payment and collection levels

Department related analysis:

• Number of treatment events / medical interventions per Department
• comparison of same specialty Departments

The star schema could also capture the involvement and performance of medical Professionals. In addition it could capture many more facts related to the event like manhours spent by the Doctor in charge, by the other professionals involved. The datamart can be linked to the datamart of another Hospital or Healthcare system, if the two use conformed dimensions and facts. Standardized codifications (like ICD) aim at achieving this conformance, in order to be able to consolidate information at the regional, national and international level. Why develop the star schema, instead of querying directly the Hospital Information System database. For two important reasons:

• The star schema model is easily understandable by the Business Analyst
• The analysis on the star schema is computationally more efficient (the symmetric structure’s simplicity allows for better query optimization)

Moreover the star schema incorporates enriched information at the dimensions as well as derived facts on the fact table (cost, duration). This additional information is produced and appended in the datamart staging area. Business intelligence infrastructures, like the one presented above can facilitate the analysis and continuous improvement in a Healthcare system.