Since the 1990s there has been a great interest in hospital readmission rates
there has been increasing interest in using admission rates as health outcome indicators to make comparisons over time and between health authorities. Rates of hospital admission for certain conditions and groups were considered to be useful performance indicators in five of the six areas outlined in the performance assessment framework document (Department of Health 2000). These five areas are health improvement, fair access, effective delivery of appropriate health care, efficiency, and health outcomes of NHS healthcare.
The performance indicators in question include:
• hospital admissions for serious accidental injury (health improvement)
• elective surgery rates (fair access)
• surgery rates (fair access)
• inappropriately used surgery (effective delivery)
• acute care management (effective delivery)
• chronic care management (effective delivery)
• day case rate (efficiency)
• emergency admissions of older people (health outcomes).
The national objective to ensure that everyone with health care needs (fair access) receives appropriate and effective health care (effective delivery) offering good value for money (efficiency) for services as sensitive and convenient as possible so that good clinical outcomes are achieved (health outcomes) to maximise the contribution to improved health (health improvement).
• What are the general factors affecting admission rates?
• What factors influence admission rates when they are being used specifically as outcome indicators for chronic medical conditions?
• How should admission rates be calculated when used as health outcome indicators?
LACE rule
LACE (length of stay, acuity of admission, comorbidity, emergency department use within six months of admission)
HOSPITAL (low hemoglobin level, discharge from oncology, low sodium level, procedure during hospitalization, nonelective index admission type, number of hospital admissions during the previous year, length of stay)
there has been increasing interest in using admission rates as health outcome indicators to make comparisons over time and between health authorities. Rates of hospital admission for certain conditions and groups were considered to be useful performance indicators in five of the six areas outlined in the performance assessment framework document (Department of Health 2000). These five areas are health improvement, fair access, effective delivery of appropriate health care, efficiency, and health outcomes of NHS healthcare.
The performance indicators in question include:
• hospital admissions for serious accidental injury (health improvement)
• elective surgery rates (fair access)
• surgery rates (fair access)
• inappropriately used surgery (effective delivery)
• acute care management (effective delivery)
• chronic care management (effective delivery)
• day case rate (efficiency)
• emergency admissions of older people (health outcomes).
The national objective to ensure that everyone with health care needs (fair access) receives appropriate and effective health care (effective delivery) offering good value for money (efficiency) for services as sensitive and convenient as possible so that good clinical outcomes are achieved (health outcomes) to maximise the contribution to improved health (health improvement).
• What are the general factors affecting admission rates?
• What factors influence admission rates when they are being used specifically as outcome indicators for chronic medical conditions?
• How should admission rates be calculated when used as health outcome indicators?
LACE rule
LACE (length of stay, acuity of admission, comorbidity, emergency department use within six months of admission)
HOSPITAL (low hemoglobin level, discharge from oncology, low sodium level, procedure during hospitalization, nonelective index admission type, number of hospital admissions during the previous year, length of stay)
Charlson Index Online Calculator - farmacologiaclinica.info
farmacologiaclinica.info/scales/Charlson_Comorbidity/
Clavien–Dindo classification of surgical complications
Prediction is not new to medicine. From risk scores to guide anticoagulation (CHADS2) and the use of cholesterol medications (ASCVD) to risk stratification of patients in the intensive care unit (APACHE), data-driven clinical predictions are routine in medical practice. In combination with modern machine learning, clinical data sources enable us to rapidly generate prediction models for thousands of similar clinical questions. From early-warning systems for sepsis to superhuman imaging diagnostics, the potential applicability of these approaches is substantial
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