Graph Story 3

An article in the British Medical Journal (BMJ 2013;346;f1563) reported, “With more patients being treated but fewer beds, there’s no doubt that beds are being used more efficiently. But more intensive use could be a problem”.

The trend in fewer beds in use in hospitals can be seen in most countries in the Organisation for Economic Cooperation and Development (Fig 2).

fig 2

Image credit: British Medical Journal (BMJ 2013;346;f1563)

fig 1

Image credit: British Medical Journal (BMJ 2013;346;f1563)

In England this trend in the fewer number of hospital beds in use – by 59% for all beds since 1979 till 2012 can be viewed from Fig. 1, also shows sharp reduction in beds used for acute care, for maternity, geriatric care, mental illness and learning disability.This trend is the direct result of concerns (BMJ 2013;346;f1563) about  “the need to save money and improve labour efficiency in the light of a shortage of nurses and general pressures on health service budgets.”

Since the number of beds in use has reduced but with increases in population, thus with more patients being treated but fewer beds, the time patients spend in hospital i.e the average length of stay (ALOS) a patient spends in hospital also needs to be reduced (shortened). John in (BMJ 2013;346;f1563) gives the example of ALOS for an acute case in England that “has shrunk from around 9.4 days in 1979 to about three days in 2011.”

John (BMJ 2013;346;f1563) adds some reasons in his article as he examined these trends and found new changes as follows that “have helped shift care from the ward to the outpatient department and beyond the walls of hospitals” :

  1. medical practice
  2. drugs
  3. diagnostic procedures
  4. policies which deliberately moved mental health, learning disability, and
    geriatric services out of hospital and 
    into the community
fig 3

Image credit: British Medical Journal (BMJ 2013;346;f1563)

Fewer beds but more patients being treated means the daily bed occupancy (BOR) rises. For example, daily average BOR data across all hospitals for England reached over 90% on several days (Fig. 3). John (BMJ 2013;346;f1563) infers that “Such high occupancy rates reduce the time available for cleaning between patients and increase the chances of infection.”, although he believes and I believe too that “there’s no doubt that beds are being used more efficiently.”

References:

  1. John, A 2013, The Hospital Bed: On Its Way Out?, March 2013, vol. 346, British Medical Journal, BMJ Publishing Group Ltd, London

Graph Story 2

Patients in any public or private hospital usually want to go back home quickly, as no one I believe really wants to stay in a hospital unless as Suresh Soni, chairman of the Bengaluru-based Nova Medical Centers, India said “you have a troublesome mother-in-law back home”.

“Patients stay on for longer not because they have to, but because the processes aren’t efficient enough”, says Avnish Bajaj, MD, Matrix Partners India.

A key metric often used by the healthcare industry to measure efficiency is average length of stay (ALOS). ALOS refers to the average number of days that patients spend in hospital. Hospitals calculate length of stay (LOS) data, which represents the number of calendar days that a patient was an inpatient. The total length of stay for all discharged patients is calculated for a given time period.  The ALOS is calculated by dividing the total LOS by the number of patients discharged. Day cases are excluded. In the calculation of ALOS, days and discharges of healthy babies born in hospitals are excluded in several countries (e.g. Australia, Austria, Canada, Chile, Estonia, Finland, Greece, Ireland, Israel, Japan, Korea, Luxembourg, Mexico, Spain, Sweden, Turkey).

Discharging patients as soon as possible is not so common in public hospitals in Malaysia unlike in most private hospitals in Malaysia and around the world.  But both public and private hospitals are just as keen on reducing overhead costs by discharging their patients as soon as possible.

In the rush to discharge patients as soon as possible means too short a length of stay which could  cause adverse effects on their health outcomes, or reduce the comfort and recovery of the patient. If this leads to a greater readmission rate, costs per episode of illness may fall only slightly, or even rise.

Although most hospitals would be keen on reducing overhead costs by discharging their patients as soon as possible, the more the revenue for the hospital when a patient stays longer in hospital,. Thus, a hospital’s revenue is proportionate to the average number of days a patient stays in it. Since services rendered vary each day of stay, the revenue may not be equal on every day of the stay of the patient; it will be high during the first few days and will wane gradually. According to Avnish Bajaj, a “hospital makes the most in the first 24 hours.”

It takes longer time for hospitals to start making profits as compared to other industries. You know that the longer you stay at a hotel, the more money the hotel makes. This is not the case in the healthcare business, it is the other way around.

Profit is the difference between the revenue from billing and cost incurred to render the service. A hospital’s revenue and costs are affected by various factors, such ALOS, occupancy level, in-patient and out-patient mix, and efficiency of personnel.

So hospitals aim to reduce the ALOS to a minimum of six days or less. However, shorter stays tend to be more service intensive and more costly per day. Since the revenue is higher during the first few days, reducing the ALOS to such a target may result in the effective utilisation of resources—the hospital can achieve higher occupancy levels with a reduced number of beds, collecting more revenue per patient per day than before. All other things being equal, a shorter stay will reduce the cost per discharge and shift care from inpatient to less expensive post-acute settings like to day care centres which “make so much business sense” also according to Avnish Bajaj.

Let’s get an impression from ALOS data of how we in Malaysia fare among the Organisation for Economic Co-operation and Development (OECD) countries. The graph below (click on the graph to view a large view of the graph in a new tab of your current window) shows the ALOS in hospital for all causes, 2000 and 2009 among OECD countries. Malaysia is a member country of OECD.

At the exteme right of the bar graph, ALOS was lowest in Mexico, Turkey and Israel. It was also low in Norway and Denmark, as well as in the United States, all at less than five days. At the exteme left of the bar graph, the ALOS was highest in Japan, followed by Korea. In the center of the graph, notice that the OECD average is shown to be about 7 days.

The Ministry of Health Malaysia ALOS statistics for Malaysia in 2003 was 4.61 days and 4.25 days in 2009. So Malaysia is in the same space as Mexico, Turkey and Israel.

OECD reports that ALOS in Japan is higher than all other OECD countries because of the abundant supply of beds and the structure of hospital payments which provide hospitals with incentives to keep patients longer.

The use of less invasive surgical procedures, financial incentives inherent in hospital payment methods, and the expansion of early discharge programmes which enable patients to return to their home to receive follow-up care are some of the factors according to OECD that explain the decline in ALOS in e.g. Japan, Switzerland and the United Kingdom that had relatively high levels in 2000.

High levels of ALOS exceeding 12 days is indicative (Srinivasan  2008) of the presence of chronic or incurable patients in a hospital intended for acute care, poor medical care, hospital-acquired infection, lack of interdepartmental consultants, or bottlenecks in investigative procedures.

Health Information Management (HIM) / Medical Records (MR) practitioners are responsible for overseeing the process of collecting and verifying the statistics generated at all hospitals and compile statistics regarding admission, discharge, and LOS of patients.

ALOS of patients, death rates, autopsy rates, infection rates, and consultation rates are some of the commonly computed rates based on discharge statistics which is a group of hospital statistics calculated from data accumulated when patients are discharged.

Discharge statistics are used to analyse and monitor the hospital’s operations. Discharge statistics provide a benchmark upon which decisions are made to operate and manage the hospital.

References:
Health at a Glance 2011, OECD Indicators, Average length of stay in hospitals, viewed 15 October 2012 <http://www.oecd-ilibrary.org/docserver/download/fulltext/8111101ec033.pdf?expires=1350662961&id=id&accname=guest&checksum=8A568585D320449DE1B01658D38287AD&gt;

Kripa M 2012, Outlook Business, The Specialists, 13 October 2012, Outlook Publishing (India) Pvt. Ltd., Mumbai, India

Ministry of Health Malaysia, Main MOH Publications, viewed 15 October 2012 <http://www.moh.gov.my/v/mmh&gt;

Michelle, AG & Mary, JB 2011, Essentials of Health Information Management: Principles and Practices, 2nd edn, Delmar, Cengage Learning, NY, USA

Srinivasan AV 2008, Managing a Modern Hospital,  2nd edn, SAGE Publications, New Delhi, India

Wager, KA, Frances WL & John PG 2005, Managing health care information systems : a practical approach for health care executives, 1st edn, Jossey-Bass,  San Francisco, CA, USA

Graph Story 1

A good graph can often tell a story better than words. I think graphs are great to explain and demonstrate things in a colorful, easy to understand fashion.

This post is something I like to start off postings from time to time, a series I shall call ‘Graph Story’, about stories of patients who come and go in our hospitals through graphs from all that data you as Health Information Management (HIM) / Medical Records (MR) practitioners so routinely churn out – through graphs I can source from the kind Internet.

The following Graph Story 1 kicks off this series of Graph Story, and this Graph Story is a graph story about Average Length of Stay and Average Charges.

Graph credit : Healthcare Cost & Utilization Project (HCUP) Facts and Figures: Statistics on Hospital-Based Care in the United States, 2005

The solid line in the graph represents mean expected charges for hospitalisation

  • Generally longer lengths of stay will be associated with higher average charges but mental health conditions (impulse control disorders, schizophrenia, and pre-adult mental disorders) proved the exception rather than the rule as these kind of patients stayed longer in hospital but were charged the lowest charges
  • Infants with infant respiratory distress syndrome and premature birth and low birth weight consumed most of the hospital’s resources
  • Most conditions stayed close to the mean charges but in the lower rungs of the straight line (notice the crowding around this line near the bottom of the line)
  • Conditions treated with expensive technology or requiring intensive care including spinal cord injuries, heart valve disorders, cardiac and circulatory disorders, and leukemia hovered above the mean expected charges for hospitalisation, but such patients did not stay too long in hospital