Hospital analytics can be used to track the performance of a hospital over time and to identify areas where improvement is needed. Every hospital has to have a system which can provide management with information necessary to plan and control efficient patient care and efficiently manage the hospital. Such information will includes trends and data that will improve decision making. Hospital utilisation statistics are a set of measurements used to assess how efficiently a hospital is using its resources. These metrics can help hospital managers make informed decisions about staffing, budget, and resource allocation. It is crucial that you understand the fundamental formulas for hospital utilization statistics if you want to create a hospital analytics dashboard that is both uncomplicated and efficient. This article will provide you with the necessary information to accomplish this.

Importance of Hospital Analytics

A hospital analytics dashboard provides you with very important pieces of information and utilisation statistics. These statistics can be used to track the performance of a hospital over time and to identify areas where improvement is needed. Hospital utilisation statistics can also be used to compare the performance of different hospitals. This information can be used to identify hospitals that are doing well and to learn from the practices of those hospitals.

Overall, hospital utilisation statistics are an important tool for hospital managers. By understanding these metrics, managers can make informed decisions about staffing, budget, and resource allocation. This can help to ensure that the hospital is using its resources efficiently and providing high-quality care to its patients.

Hospital Analytics

Here are some of the specific benefits of using hospital utilisation statistics:

  • They can help to identify areas where resources are being underutilised.
  • They can help to forecast demand for resources.
  • They can help to improve efficiency and productivity.
  • They can help to reduce costs.
  • They can help to improve quality of care.

Types of Hospital Utilisation Statistics

Some of the most important hospital utilisation statistics include:

  • 1. Hospital Admission and Discharge Statistics
    • 1.1 Admission Rate (AR)
    • 1.2 Average Daily Inpatient Census (ADIC)
    • 1.3 Average Daily Discharge Census (ADDC)
  • 2. Hospital Bed Utilisation Statistics
    • 2.1 Total Bed Count Days (BCD)
    • 2.2 Bed Occupancy Rate (BOR)
    • 2.3 Bed Turnover Rate (BTR)
    • 2.4 Bed Turnover Interval (BTI)
    • 2.5 Average Length of Stay (ALS)

1. Hospital Admission and Discharge Statistics

Hospital admission and discharge statistics track the number of patients admitted to and discharged from hospitals each day, week, month, or year. They can be used to measure the utilisation of hospital beds, identify trends in patient care, and plan for future staffing needs.

Hospital Analytics - Hospital Admission and Discharge Statistics

1.1 Admission Rate (AR)

Hospital admission rate is a measure of the number of people admitted to a hospital in a given period of time, divided by the total population in that area. It is expressed as a percentage.

Formula

Admission Rate = (Number of hospital admissions in a given period of time) / (Total population in that area) × 100%

For example, if a hospital had 100 admissions in a year and the total population of the area was 10,000, then the hospital admission rate would be 1%.

Hospital admission rate can be used to measure the utilisation of hospital beds, identify trends in patient care, and plan for future staffing needs. It can also be used to compare the performance of different hospitals.

There are a number of factors that can affect hospital admission rate, including:

  • The age of the population
  • The prevalence of chronic diseases
  • The availability of primary care
  • The quality of care provided by hospitals
  • The cost of care

1.2 Average Daily Inpatient Census (ADIC)

Average daily inpatient census is the average number of inpatients present each day for a given period of time. It is calculated by dividing the total number of inpatient days in a period by the number of days in that period.

Formula

Average Daily Inpatient Census = (Total number of inpatients in a month) / (Total number of days in the Month)

For example, if a hospital has 3000 bed count days in a month, and there are 30 days in the month, the average daily inpatient census would be 100.

The ADIC may be calculated based on a yearly or monthly scale. If calculated for the year, the total number of inpatients for the year is divided by 365.

Why is ADIC important in hospital analytics?

The ADC can provide important performance information for hospitals, which can be used to improve and maximise operational efficiency.

For example, ADC can help hospitals see which departments are overburdened or which times of year are the busiest. The ADC can also help hospitals see which departments are experiencing a decline in the number of patients.

Overall, the ADC allows hospitals to see their capacity for inpatient care and where resources and finances should be allocated based on the needs of the patients visiting the hospital1.

1.3 Average Daily Discharge Census (ADDC)

The average daily discharge census (ADDC) is the average number of patients discharged from a hospital in a given period of time. It is calculated by dividing the total number of discharges by the number of days in the period.

Formula

Average Daily Discharge Census = (Total Number of Discharged Patients in the Month / Total Number of Days in the Month)

For example, if a hospital has 1000 discharges in a month, and there are 30 days in the month, the average daily discharge census would be 33.

The ADDC can be affected by a number of factors, including:

  • The type of hospital
  • The size of the hospital
  • The severity of the patients’ conditions
  • The availability of resources
  • The efficiency of the discharge process

Why is ADDC important in hospital analytics?

Here are some of the reasons why ADC is important:

  • It can help hospitals to identify areas where they can improve their discharge process. For example, if a hospital has a high ADC, it may need to improve its discharge planning or coordination with post-acute care providers.
  • It can help hospitals to plan for future capacity needs. If a hospital has a growing number of discharges, it will need to make sure that it has enough staff and resources to accommodate the increased demand.
  • It can help hospitals to compare their performance to other hospitals. This can help them to identify areas where they can improve.

Overall, ADC is a valuable metric that can help hospitals to improve the efficiency and quality of their discharge process.

Hospital Analytics - Hospital Bed Utilisation Statistics

2 – Hospital Bed Utilisation Statistics

Hospital bed utilisation statistics measure the occupancy rate of hospital beds, which can be used to assess the efficiency of hospital operations and identify areas for improvement. Hospital bed utilisation statistics track the number of beds that are occupied by patients, the number of beds that are available, and the average length of stay for patients. – These statistics can be used to calculate the occupancy rate, which is the percentage of beds that are occupied on a given day. A high occupancy rate can indicate that the hospital is operating efficiently, while a low occupancy rate can indicate that there are excess beds that could be used to treat more patients. Hospital bed utilisation statistics can also be used to identify trends in patient care, such as the types of conditions that are being treated in hospitals and the length of time patients are staying in the hospital. This information can be used to improve the quality of care and make better use of hospital resources.

Bed which is staffed and equipped for round the clock care of patients is called hospital bed. It includes observation beds equipped and staffed for overnight use, and beds used for sick and premature infants. The following are not counted as hospital beds2:

  • Bassinets used for healthy newborn in labour room
  • Beds in labour room
  • Recovery room beds
  • Any other beds which are not equipped and staffed for overnight use.

2.1 Total Bed Count Days (BCD)

The maximum number of inpatient days of care that would have been provided if all beds were filled during the year. If 50 beds were available for use each day during a month, bed days available would be 50 × 30 = 1500. If the number of beds fluctuated throughout the year, bed days available should reflect this and the calculation would be more complicated. Other terms used for bed days available include “potential days,” “maximum patient days,” or “total inpatient bed count days”3.

Formula

Total Bed Count Days = Total Number of Beds × Total Number of Days in a Month

The BCD may be calculated based on a yearly or monthly scale. If calculated for the year, the total number of inpatients for the year is multiplied by 365.

2.2 Bed Occupancy Rate (BOR)

Bed occupancy rate is the proportion of beds occupied during the period of interest. BOR indicates the relationship between availability and utilisation of hospital beds.

It is calculated by dividing the total number of inpatient (= Total length of stay) days in a period by the total number of bed count days (= Total beds × Total Number of Days in a Period) in that period and multiplying by 100.

Formula

Bed Occupancy Rate = [Total Length of Stay During the Month] / [(Total Number of Beds) × (Total Number of Days in The Month)]

For example, if a hospital has 3000 bed count days in a month, and there are 2000 inpatient days in the month, the bed occupancy rate would be 66.67%.

Optimum bed occupancy rate for most hospitals is considered to be between 85 to 95%, wherein the remaining 15-5% beds are available for undergoing maintenance, change of linen and being generally prepared for the incoming patients.

Interpretation

A high occupancy rate indicates stretching and over-utilisation of services resulting in a probable dilution of the quality care, while as a low rate is indicative of underutilisation of facilities. Usually, smaller hospitals have lower occupancy than large hospitals. In many public hospitals, because of the perpetual shortage of beds, patients are put on the floor when a regular bed is not available in which case the occupancy rate goes even up to 110 or 120 percent.

To find out the load of work in different areas, occupancy rates should be worked out wardwise, specialitywise and unitwise4.

BOR can be affected by a number of factors, including:

  • The type of hospital
  • The size of the hospital
  • The severity of the patients’ conditions
  • The availability of resources
  • The efficiency of the discharge process

Why is BOR important in hospital analytics?

A high BOR can indicate that the hospital is operating efficiently, while a low BOR can indicate that there are excess beds that could be used to treat more patients.

Here are some of the reasons why BOR is important:

  • It can help hospitals to identify areas where they can improve their efficiency. For example, if a hospital has a low BOR, it may need to improve its discharge planning or coordination with post-acute care providers.
  • It can help hospitals to plan for future capacity needs. If a hospital has a growing number of patients, it will need to make sure that it has enough beds to accommodate the increased demand.
  • It can help hospitals to compare their performance to other hospitals. This can help them to identify areas where they can improve.

Overall, BOR is a valuable metric that can help hospitals to improve the efficiency and quality of their care.

2.3 Bed Turnover Rate (BTR)

The number of times a bed, on average, change occupants during a given period of time. It is a measure of how efficiently the hospital’s beds are being used. It give the number of discharges per hospital bed over a given period, i.e. how many times a bed was “turned over” during the period, say a month. It is directly related to the Average Length of Stay (ALS) and Bed Turnover Interval (BTI). It is calculated by dividing the total number of discharges in a period by the total number of beds in the hospital.

Formula

Bed Turnover Rate = (Total Number of Discharged Patients During the Month / Total Number of Beds)

For example, if a hospital has 1000 discharges in a month, and there are 100 beds in the hospital, the bed turnover rate would be 10.

Interpretation

A high bed turnover rate can indicate that the hospital is operating efficiently and that beds are being used effectively. However, a high bed turnover rate can also indicate that patients are not staying in the hospital long enough to receive the care they need.

A low bed turnover rate can indicate that the hospital is not operating efficiently or that beds are not being used effectively. However, a low bed turnover rate can also indicate that patients are staying in the hospital for the amount of time they need to receive the care they need.

The ideal bed turnover rate will vary depending on the specific circumstances of the hospital. However, a general guideline is that a bed turnover rate of 10 to 15 is considered good.

Why is BTR important in hospital analytics?

Here are some of the reasons why bed turnover rate is important:

  • It can help hospitals to identify areas where they can improve their efficiency. For example, if a hospital has a low bed turnover rate, it may need to improve its discharge planning or coordination with post-acute care providers.
  • It can help hospitals to plan for future capacity needs. If a hospital has a growing number of patients, it will need to make sure that it has enough beds to accommodate the increased demand.
  • It can help hospitals to compare their performance to other hospitals. This can help them to identify areas where they can improve.

Overall, bed turnover rate is a valuable metric that can help hospitals to improve the efficiency and quality of their care.

2.4 Bed Turnover Interval (BTI)

Bed Turnover Interval is the period where a bed is unoccupied between patients. The interval between one occupant and the next is ‘n’ day. It denotes the average time in days elapsing between the discharge of one patient and the admission of the next on that bed, i.e. the time a bed remains vacant between admission.

Formula

Bed Turnover Interval = [ (Number of Beds) × (Number of Days in the Month) – (Length of Stay) ] / [Total Number of Discharged Patients During the Month]

Length of Stay is calculated by deducting Total Hospitalisation Days from Total Bed Count Days (BCD).

Interpretation

The ideal BTI will vary depending on the specific circumstances of the hospital. However, a general guideline is that a BTI of 1 to 3 days is considered good.

A high BTI can indicate that the hospital is not operating efficiently or that beds are not being used effectively. However, a high BTI can also indicate that the hospital is treating patients with complex conditions that require a longer stay in the hospital.

A low BTI can indicate that the hospital is operating efficiently and that beds are being used effectively. However, a low BTI can also indicate that patients are not staying in the hospital long enough to receive the care they need.

Why is BTI important in hospital analytics?

Here are some of the reasons why BTI is important:

  • It can help hospitals to identify areas where they can improve their efficiency. For example, if a hospital has a high BTI, it may need to improve its discharge planning or coordination with post-acute care providers.
  • It can help hospitals to plan for future capacity needs. If a hospital has a growing number of patients, it will need to make sure that it has enough beds to accommodate the increased demand.
  • It can help hospitals to compare their performance to other hospitals. This can help them to identify areas where they can improve.

Overall, BTI is a valuable metric that can help hospitals to improve the efficiency and quality of their care.

2.5 Average Length of Stay (ALS)

Average length of stay (ALOS) in hospital is the average number of days that patients spend in the hospital. This is the average number of days that a patient stays in a hospital. It is calculated by dividing the total number of days that patients stay in the hospital by the number of patients who are discharged during a specific period of time.

Formula

Average Length of Stay = (Total number of days that patients stay in the hospital) / (Number of patients who are discharged during a specific period of time)

For example, if 100 patients are discharged from a hospital in a year and they have a total of 1,000 days of stay, then the average length of stay would be 10 days.

ALOS can be affected by a number of factors, including:

  • The type of hospital
  • The severity of the patients’ conditions
  • The availability of resources
  • The efficiency of the discharge process

Interpretation

The ideal ALOS will vary depending on the specific circumstances of the hospital. However, a general guideline is that an ALOS of 5 to 7 days is considered good.

A short ALOS can indicate that the hospital is operating efficiently and that patients are being discharged as soon as they are ready. However, a short ALOS can also indicate that patients are being discharged before they are fully recovered.

A long ALOS can indicate that the hospital is not operating efficiently or that patients are not being discharged as soon as they are ready. However, a long ALOS can also indicate that patients are being treated for complex conditions that require a longer stay in the hospital.

Why is ALS important in hospital analytics?

Here are some of the reasons why ALS is important:

  • It can help hospitals to identify areas where they can improve their efficiency. For example, if a hospital has a long ALOS, it may need to improve its discharge planning or coordination with post-acute care providers.
  • It can help hospitals to plan for future capacity needs. If a hospital has a growing number of patients, it will need to make sure that it has enough beds to accommodate the increased demand.
  • It can help hospitals to compare their performance to other hospitals. This can help them to identify areas where they can improve.

Overall, ALOS is a valuable metric that can help hospitals to improve the efficiency and quality of their care.

References

  1. Definitive Health Care (2023). Definition of average daily census (ADC). Retrieved September 7, 2023, from https://www.definitivehc.com/resources/glossary/average-daily-census ↩︎
  2. Sakharkar, B. (2008). Principles of hospital administration and planning. Jaypee Brothers Medical Publishers Pvt. Limited. ↩︎
  3. https://www.health.pa.gov/topics/HealthStatistics/Statistical-Resources/UnderstandingHealthStats/Documents/Occupancy_Rates_in_Health_Facilities.pdf ↩︎
  4. Sakharkar, B. (2008). Principles of hospital administration and planning. Jaypee Brothers Medical Publishers Pvt. Limited. ↩︎

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