• There have been massive strides in the last 20 years in every field of healthcare. Cancers that were once a death sentence can now be treated with new drugs and therapies, resulting in remission or even a cure. Advances in ophthalmic surgery has made the removal of cataracts a routine low-risk procedure. Hips and knees can now be replaced with life-changing results. And yet, the costs of healthcare spiral upwards, misdiagnosis is rife and expensive drugs are prescribed which have little or no beneficial effect on the patient’s condition.

    If nothing is done to halt the situation, it is predicted that healthcare costs will top $5.2 trillion in 2020 in the United States alone, a sizeable chunk of the GDP.

    The problem lies with the fragmented data pertaining to a particular patient. Varying in data categories from manual notes written by a doctor to images from ultrasound and MRI machines, this data is generally not shared or aggregated, but sits in multiple silos belonging to each doctor, laboratory, insurer, hospital and public health authority. There is no holistic view of the patient and the pharmaceutical company that produces the drugs that are prescribed to him does not obtain any feedback on their efficacy or otherwise, unless it is a clinical trial.

    Recently, there have been several initiatives to rectify the situation, which are in varying stages of implementation:

    • Electronic Health Records (EHR) and Electronic Medical Records (EMR)
    • Patient-centred care
    • Evidence-based medicine
    • Outcomes-based remuneration

    All of these initiatives create, add to, store and retrieve data from “big” databases. These databases were initially categorized as “big” because of the variety of data in them. They will soon become very large by volume. A report by the IDC estimated that by 2020, there will be 25,000 petabytes of healthcare data, or fifty times the data available in 2012.

    It goes without saying that artificial intelligence is required to scrutinize this big data and each of the initiatives above has an AI component.

    EHR: the Good, the Bad and the Ugly

    Traditionally, doctors have always kept handwritten records of their interactions with patients during consultations. The problem with that is that the data was completely siloed. There was a general realization by the industry that all healthcare data had to be electronic if the industry was to survive, and a move to Electronic Health Records (EHR) started. EMR means the same thing, but tends to apply to software used to store the physician’s notes about the patient. This is good, as data can be shared and aggregated (there are huge confidentiality issues here, but they will be discussed in another article). However, this is also bad as it is estimated that doctors spend more time on administrative work than actually consulting patients. What is more, in this crazy world in which we live today, there are recommendations and pressures from medical schemes among others, that a doctor should spend about 7-8 minutes with a patient. This is ugly, and does not bode well for patient-centred care.

    What needs to happen here is that a more effective way of capturing patient data needs to be developed. Universities and medical schools recognise that aspirant and qualified medics actually need training on how to use EMRs and have instituted courses for this problem.

    The Patient is the Customer – Patient Centred Care

    Until recently, the patient has been regarded as a collection of symptoms housed in a human being. Drugs and treatments were (and still are) administered based on the malady and not the person. While there is still a way to go, there is a move to understanding the uniqueness of the patient, based on his genetics and other factors that should define the appropriate regime and drugs.

    The problem with attaining this goal is that it requires that all the healthcare data for that patient must be in one, easily accessible place. Additionally, a large base of data about people who are genetically, socially, and ethnically like himself, presenting the same symptoms and in the same geolocation needs to be accessed too, to draw comparisons. This requires access to a repository of this data, and there are very few places where this is actually available now.

    However, once a healthcare professional is presented with the approximately 5,000 gigabytes of data he needs to understand his patient, he will be able to provide targeted and economic treatments with a positive outcome.

    It is estimated that in the US today, millions, if not billions of dollars are wasted on misdiagnosis. A recent study estimated that about $4 billion is wasted annually on breast cancer misdiagnosis alone. Misdiagnoses are not just about money, a study by the Board on Healthcare Services of the US, estimated that there are approximately 12 million misdiagnoses resulting in 40,000 to 80,000 deaths per annum. The cost implications of misdiagnosis are staggering, and an indication of why evidence-based medicine is so critical.

    Weighing the Evidence – a Scientific Approach to Diagnosis

    Evidence-based medicine will reduce misdiagnosis to a great extent, and is based on using all the research and science available for diagnosing a patient’s illness and selecting the appropriate treatment. This requires that the physician must be able to do data mining and data discovery, either using his own knowledge of statistical analysis or artificial intelligence embedded in relevant software.

    The Carrot or the Stick – Value-based Payments

    Change is difficult, and there needs to be a motivator for change. A new approach to remunerating healthcare workers is based on successful outcomes. An example of this in action is the Arkansas Health Care Payment Improvement Initiative (AHCPII), which is a plan to provide patient-centred care and reduce misdiagnoses by compensation based on successful treatment and not on volume. This change in approach requires physicians to enter the progression of the patient care over a period of up to 12 months. If the treatment was successful and the cost of achieving this outcome was below the predicted cost, the “Principal Accountable Provider” (PAP) is eligible for a share in the savings. If the costs are above the expected cost, he will be required to pay an excess.

    This model is being adopted by other public and private sector bodies who foot the bill for healthcare. While it could be uncomfortable initially for the PAP, Arkansas hopes to improve patient care, manage healthcare costs and improve population health in general by instituting this plan. Part of the plan is the implementation of comprehensive analytics to gauge the effectiveness of healthcare provided by healthcare professionals and the general health of the Arkansas population.


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