Last week we found out how George’s tax position was improved by the diligent data delivery in Bigdataland, whilst his boss’s avoidance schemes were foiled. In this week’s fifth and final FiO visit to Bigdataland, we see what happens when George’s wife, Susan, is called into hospital.
Having a twin brother who lived in the UK, George was often able to really appreciate the benefits of living in Bigdataland – a place where numbers had real influence. But perhaps the occasion where this was most the case was when Susan received a worrying letter from her local doctor.
The letter informed them that a combination of factors that had been analysed revealed that she was at particular risk of a disease that if captured early, could be easily treated, but if not could prove fatal. The health system in Bigdataland was legendary throughout the world – not only were they the most efficient, but they also had the lowest rates of death from preventable disease.
Within a week, Susan was able to visit the local hospital for a test, which revealed that she did in fact have the disease. However a doctor reassured her that they had caught it at the earliest possible stage and that she had a 99.87% chance of having no serious effects from it. However there would need to be treatment involving a week in hospital.
Susan’s hospital date was scheduled for a few weeks later. She had not been in hospital since Rita was born, and was amazed by the efficiency shown. When she went in she was interviewed about personal preferences. Based on these she was placed in a ward with people who she felt comfortable having conversations with – never an easy thing to do in a worrying situation.
She was given a wrist band with a bar code. This was used time and time again to ensure that she was receiving the right treatment. A nurse who had been educated in the UK told her that in Bigdataland, she had never seen a case of someone receiving the wrong treatment – something that was sadly not true in the rest of the world.
Everything else about her stay in hospital was superb. The food was tailored to her liking, and despite the treatment being unpleasant, by the time she left, she confessed to George that she almost felt like she had been in a spa hotel for the week. George was temporarily worried about the cost to him as a taxpayer, but a later drink with his friend who worked in the Health Department calmed his fears. “Because we catch most diseases early, or treat them effectively with vaccines, we actually have lower costs than all other advanced health services in the world”, he said.
Susan was left on a regimen of drugs. In general this was not a nuisance – the only problem arriving when they went on holiday, the summer after, and Susan forgot to get her prescription repeated. When they got home, there was a letter on their doorstep from their Doctor telling them the dangers of not keeping up the drugs which prompted Susan to go in the day after to rectify the situation. But other than that, the danger had passed for Susan, before it had ever been realised.
Later that year, when George was offered a promotion that involved moving to the companies offices in the UK, George did not have to think very long about his response. “It’s just not worth the money” he replied…
In the global race to be leaner and more efficient using the number concept and mathematical models, Healthcare is not left behind. The profileration of numbers in the Healthcare system has always been prevalent. This piece by one of our consultants – The Numbers Behind My Hospital Stay lends credence to that argument. There was a numerical angle to virtually every step during his stay in hospital.
Analytics will not just be useful to improve patient care but will also be an important tool in assessing provider performance. An article – Analytics and the Future of Healthcare predicts that in the not too distant future, emphasis on Healthcare will shift from volume to value. Currently, the more patients a doctor sees, the more money the hospital makes and though patient outcomes and quality of care have always been a concern, they rarely impact fees earned.
In the future, healthcare providers who do not meet a certain standard of care may become liable to financial penalties. One yardstick will be re-admission rates - making patient outcomes a part of the fee structure. The approach to arriving at this structure will be through analytics, aggregating patient info on admissions and standard of care.
Health care providers are creating and collecting more health data than ever before. The challenge is making use of that data to improve patient care. Susan in Bigdataland was amazed at how her care was tailored to her preferences. That is the power of data and using the data to develop insight. Analytics can help hospitals access feedback in real time and quickly make changes to patient care. As a direct consequence of investing in analytics, a hospital in Florida, according to a blog on Providers turning to data analytics to improve health care, reported a 25 percent increase in room cleanliness, an 11 percent increase in nurse communications with patients and better staff responsiveness over the past year. Susan will be amazed to know that all her likes and dislikes were being analyzed realtime to give her a better experience.
Furthermore, the use of analytics has benefits to healthcare and efficiency. It does also have the potential to prevent mistakes that could prove fatal. Sarah had a wristband with a barcode which ensures she gets the right treatment. The wristband also does something fairly basic – it identifies her. This article where the wrong patient was wheeled off for an X-Ray after an identity blunder describes a situation where wristbands were not in use. Thankfully it was only an X-Ray, it could have been a transplant or a non reversible surgical procedure.
The use of healthcare analytics is credited with saving money and most importantly saving lives. Susan may have had an excellent time at hospital with tailored fit for purpose care. But what her hospital will struggle to control is her medication adherence post discharge. In fact a study in the United States suggests that nearly half of all medication prescriptions are not followed - leading to 125,000 deaths annually and costing more than $290 billion per year in avoidable costs.
Predictive analytics can have an enormous impact on adherence. By alerting doctors, pharmacists and health plans to patients who are most vulnerable to non-adherence, preventative measures can be taken before patients experience negative health outcomes. This is a scoring system similar in many respects to how banks and credit card companies fight fraud.
Helping to improve adherence on medication is not the only way analytics is credited with saving lives. There are numerous examples of lives being saved, This article gives a number of examples, some of which are sumarrised below:
- Cleveland Clinic has developed a program where staff from compliance anonymously watches workers in different departments and record whether they do in fact follow hand hygiene guidelines. Their findings are uploaded into Cleveland Clinic’s enterprise analytics system and are accessible via a dashboard tab. From a 40% compliance rate, it is now 90%.
- By carefully collecting and analyzing data, Cleveland Clinic has been able to reduce infection rates, spend less on equipment, and avoid costs of up to $30,000 per affected patient.
- A blood transfusion dashboard helps identify physicians that haven’t kept up with the latest information in health best practice, improve the supply of blood, and reduces costs.
This is the last story from George and Bigdataland. We hope that you have enjoyed journeying with us to a land which, whilst some of its use of analytics is fictional, may one day become reality.