Shengjing Hospital Quanyu: The hospital's big data application can go further

Recently, at the 2018 HIMSS Greater China Regional Conference, Quan Yu, director of the Information Center of Shengjing Hospital affiliated to China Medical University, gave a speech on the practice and application of hospital data. In the opinion of the whole director, the data in the hospital can realize the application of medical record quality control, rational drug use, doctor level evaluation, and standardization of clinical pathways.

盛京医院全宇:医院的大数据应用,可以走得更远一些

Quanyu, Director of Information Center, Shengjing Hospital, China Medical University

Electronic medical records have been implemented in China for nearly ten years. With paperless, big data can go forward.

I remember a few years ago, we often discussed in WeChat, paperless is too far away from us, the process in the hospital is too complicated, and it is impossible to achieve. However, I did not expect that more than 95% of the processes in Shengjing Hospital have achieved paperlessness.

The big data in hospitals is to further improve medical safety and improve medical quality. I have heard on many occasions that big data should provide the foundation for scientific research. But if the data we get is inaccurate, then the scientific results must be inaccurate.

Big data has already formed in hospitals, and some people are still using carrier data to compare data sizes, which makes no sense. What we really want is more than full sample data.

Most of the hospital's data was left unused and not being used effectively. Real big data is through data analysis to find problems, not just based on an existing goal, and retrospective verification after completion. That's big data application, but I think we can go further.

Why is the case of Shengjing Hospital promoted? Because all of our data comes from the clinic. Use your own hospital data to support the hospital's big data applications.

Specifically, we use big data to support medical management, improve the quality of medical records (especially subjective medical records), and based on existing data, we find that medical quality is insufficient, so that it is targeted.

At present, the hospital's medical record quality control is mostly based on objective indicators, and rarely pay attention to the quality of medical records. For the part of the subjective medical record handwritten by the doctor, it is judged by people. How can computers be used to analyze and utilize medical records based on big data to improve medical quality?

Subjective medical record quality control, can compare the content similarity of two recorded texts. Shengjing Hospital introduced the algorithm into the hospital medical record, comparing two ward round records of the same patient or two disease records to see if the coincidence degree is the same. It turns out that the two records are exactly the same, which means that the medical records in the hospital are far from being as good as we think.

For the main complaint and diagnosis, suppose a child has lobular pneumonia, corresponding to this diagnosis, if there is no fever cough in the main complaint, then it is considered that the diagnosis and the main complaint are not corresponding, why? We took out the diagnosis of a single disease for more than five years, and separated it by it technology. After that, we listed the high-frequency words and found that fever and cough appeared frequently.

If there is no fever and cough in the main complaint, then there is a problem. Doctors can improve their efficiency by returning these medical records to a subjective view. We don't have to do this 100%, as long as we achieve 50% of the effect, we can save half of the manpower.

The same is true for developing a clinical pathway. Around 2013, hospitals often organized expert groups to conduct arguments. It is up to the big experts to determine what kind of treatment plan a disease should be in the clinic. However, due to the different levels of experts, the inspection level and inspection equipment of each hospital are different, so the clinical path is ultimately different in every hospital.

How to let hospitals regulate their clinical pathways and optimize them?

In this regard, Shengjing Hospital analyzed the single diagnosis path of disease for more than five years through big data. Know what is the most common treatment, and then re-list this path. Applying historical data to the present and guiding the future is a big step forward.

The effect is also obvious. Three or four years ago, the child came to the hospital for a hospital stay. The first day was antibiotics. But now is not the case, the first choice in Shengjing Hospital is to improve the immunity of related drugs. It is not until the next day or the third day that the child begins to use antibiotics. This is the clinical diagnosis and change based on big data.

In addition, big data can also be used to evaluate the level of a doctor.

Usually, to evaluate the doctor's level, it is necessary to see his admission diagnosis, discharge diagnosis, confirmation diagnosis including pathological diagnosis, and how much is completely consistent. If they are identical, it means that the disease may be simpler or the doctor's level is higher. If they are inconsistent, it means that the doctor is not high, or the disease is complicated. We extract this information and provide it to the hospital administrator for analysis, which can reasonably and effectively evaluate the ability of a doctor.

Medical quality analysis can achieve three aspects:

1. Standardize the behavior of the clinician;

2. Improve the level of rational drug use;

3. Provide data support for standardizing medical behavior.

The variety of drugs used for data analysis can reflect the diagnostic complexity of the disease.

How to evaluate the rationality of a doctor's medication? For example, one doctor looked at A patients, and another doctor saw A+10 patients, but the workload of doctors with a large number of doctors is not necessarily large. Because doctors who see less may be more difficult to respond to the disease, they need to be judged comprehensively. The extraction medication program analyzes the quality of medication, observes trends, determines the type of antibiotics used each year, and provides them to managers to determine trends in antibiotic use.

Now that the drug is zero-added, the hospital should pay more attention to the quantity and quality of the drug.

In the process of optimizing the process and rational allocation of medical resources. We conducted an analysis of outpatient efficiency and reconfigured service resources based on different processes.

Among them, intelligent consultation is also based on big data. We analyzed the five-year complaints of outpatients and automatically recommended the patients to the departments that need to be registered. The more words the patients choose, the more accurate the recommended registration department. Of course, because the amount of data is too large, if the system on-site calculation needs to wait a long time, Shengjing Hospital will make up for it through some algorithms.

After registration, after the examination, the system will also give the patient a reminder after the return visit, and give the corresponding health training.

To do medical hospital informationization or medical management is also good, must be based on process management optimization, relying on analysis of history to find problems and make up for problems. Strengthening medical safety and quality control is an eternal theme of the hospital.

Quantifying workload and performance appraisal is worth looking forward to for doctors and nurses. The hospital should fully mobilize the subjective initiative of the medical staff and be able to give a reasonable distribution of performance. In this regard, Shengjing Hospital began to evaluate the performance of doctors and nurses through big data from December last year. Including technicians, pharmacists, anesthesiologists, operating room nurses, etc.

All in all, whether it is medical management, information construction or big data utilization, it is a process of continuous improvement.

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