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The potential of data stored in health facilities often remains unexplored.

How can you make better use of the data in the EHR?

Data that physicians enter into the electronic health records (EHRs) are used mainly to view the medical history of a patient. We suggest how to better manage the data in the “small data” model.

There is a lot of truth in this saying. The scientific study “Physician Time Spent Using the Electronic Health Record During Outpatient Encounters,” 2020, which covered 100,000 appointments in the US, revealed that for each appointment, 16 minutes must be spent on entering data into the EHR. The greatest amount of time is spent on reviewing the appointment history (33%) and its documentation (24%).

This is quite a lot of time, but still significantly less than with paper documentation. The EHR has quite a few other advantages: quick access to patient data from other points of care, secure data exchange, easier viewing and sorting of information, automation through note templates and standards, secure prescribing, and control of pharmacotherapy.

In addition to the benefits arising directly from the digital form of data (primary processing), there is a second group of benefits concerning the secondary use of the data. We are talking about the information that can be obtained by analyzing patient datasets within a healthcare facility or individual medical practice. In order to achieve this potential of data, a “data management” strategy should be implemented.

The data use strategy includes two elements:

  • a way in which a physician interacts with patient data to support clinical and preventive decision-making (data sorting);
  • a methodology for processing the data to take specific care actions for specific patient groups or individuals.

Patient summary 

Every physician has an individual way of working. Still, during an appointment of a returning patient, they need the same data: previous diagnoses, laboratory and imaging diagnostic results, prescribed medications, appointment notes, allergies, trends in measurable health parameters, alerts for exceeding reference standards, etc. A so-called EHR dashboard organizes this information so that the physician can get a complete picture of the health condition quickly.

Well-designed IT systems allow for individualization of patient summaries, as these vary according to the patient cohort, specialty, and physicians’ expectations. Pre-configuration will help to save time spent on reviewing data and opening subsequent windows in the system. When deciding on a particular view of the system, the effect of bias must be kept in mind. While data selected and visible at a glance can speed up decision-making, at the same time there is a risk of overlooking other essential facts that are either not highlighted or simply not included in the EHR. Therefore, a good physician and nurse dashboard is subject to systematic evaluation. While it sounds like something obvious, in practice, it often gets forgotten right after implementing the IT system.

Population health and risk groups

It is much less common for healthcare facilities to use data resources to make decisions about care or prevention. Yet, they can increase patient satisfaction with services and even improve patient treatment outcomes. A data management strategy should specify which reports are produced on a regular basis, who has access to them – in line with patient data privacy rules and GDPR procedures – and what actions they entail.

The way doctors interact with EHR influences patient outcomes and satisfaction

A classic example is a list of patients for whom the results of laboratory tests ordered by a physician are already available. If this is supplemented with information on whether a follow-up appointment has already been arranged for the patient, the Primary Health Care can more easily decide what action should be taken: a phone call to the patient informing them that everything is fine; a call for an appointment; no action – the results are stable and the appointment is arranged.

Similar procedures apply to other statements generated from the system. The number of potential statistics and statements is large, all depending on the objectives of the analyses, which could be, for example, improved care, increased patient satisfaction, revenue optimization, identification of bottlenecks in workflows, etc.

  • Patients with chronic diseases in stable condition. For this group, the facility can offer similar services, but in a different form. This involves online consultations during which a physician prescribes an e-prescription. Appropriate management of the flow of patient groups is also important from a revenue perspective (online consultation service for chronic patients – more free appointments for patients requiring on-site care).
  • For example, a medical facility can analyze a list of diabetic patients and check whether they were referred for an eye test (or there are recorded results) in the previous year to exclude neuropathy.
  • Preventive campaigns. This could be a list of patients with chronic diseases, who should arrange follow-up appointments but do not; or those who, because of their risk group, should be given preventive vaccinations.
  • Patients with a high compliance rate. Those who are engaged in the treatment process, attend appointments regularly, purchase the prescribed medication and follow the instructions given. Appropriate annotation in the file helps to match communication and care.
  • Frequency of visits. A high level of returning patients may result from serving a specific cohort of patients (seniors with multiple morbidities), dissatisfaction with the level of service (appointments of the same patient with different GPs), or inappropriate use of available technology (e-prescriptions for chronically ill patients). There are many other statistics that can identify abnormal workflows: the missed appointment rate, an increasing average patient service time, the need to fill in the EHR beyond the end of the appointment, etc.
  • Analysis for scientific research purposes. Anonymous, high-quality EHR data are often used in scientific publications. Small data – data collected locally but representing a large cohort of patients; they can contain unique information about regional population health trends.

Whixx

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