DRG (Diagnosis-Related Group) determines how medical expenses are paid by grouping patients according to diagnostic and surgical data. However, there are some cases that cannot be successfully entered into the DRG group, which is the so-called non-enrolled cases (0000 group, refers to the cases that cannot be enrolled normally due to disease diagnosis, surgery or operation coding and other reasons, including those who cannot enter any MDC and those who have entered a certain MDC but cannot enter any internal medicine ADRG in the MDC).
To a certain extent, the non-enrolled cases reflect the quality of the medical records, and its existence may bring some other harms and problems, which doctors and functional department personnel need to understand, such as the following three points:
1.Payments are unfair:
Non-enrolled cases cannot be settled according to the payment method of the DRG system, which may lead to the inability of medical institutions to obtain the due payment amount, thus affecting the operation and development of medical institutions.
2.Data inaccuracies:
The presence of non-enrolled cases may lead to inaccuracies in the data in the DRG system, which can affect the results of medical statistics and analysis. This can create difficulties in healthcare decision-making and policymaking.
3.Quality Assessment Deviations:
The presence of non-enrolled cases may lead to bias in the assessment of the quality of care. If some severe cases are not included in the DRG group, it is not possible to accurately assess the level of effectiveness and quality of the medical facility.
The existence of non-enrolled cases has brought certain challenges to medical payment and management, but this is the easiest to find the reason and quickly improve the quality, and the medical insurance is about to be liquidated.
So, how should hospitals analyze non-enrolled cases?
First of all, it is a description of the overall situation, and descriptive statistical analysis is carried out on indicators such as the total number, proportion, time distribution, department distribution, and overspending balance, so as to find abnormal or valuable points.
For example, the 2-month non-enrollment rate of a hospital is 45%, a total of 325 cases could not be enrolled, this part of the cases from the department distribution, obviously found concentrated in the three wards of the neonatal department and the second ward of the pediatric internal medicine ward, it is necessary to focus on the analysis of the situation of these two departments, if necessary, to do admission. The number of other departments is small, and the department can self-check and supervise the medical record department.
Secondly, it is necessary to conduct a cause analysis to clarify the specific reasons for non-enrollment and formulate targeted solutions.
There are a variety of common reasons for non-enrollment, mainly including the following five aspects:
1.Wrong or incomplete data:
In the event that the uploaded data is inaccurate or incomplete, cases may not be grouped correctly. For example, the main diagnosis is missing, the number of days in hospital is missing, and the weight of the newborn does not meet the filling requirements.
2.Diagnosis (especially major diagnosis) and surgical procedures are not in accordance with the regulations
The DRG system requires the use of Medicare 2 for diagnosis and operationVersion 0 of the ICD code, otherwise it cannot be recognized by the grouper and cannot be included in the corresponding DRG group. For example, the use of hospital code, group code, national version code, etc.
3.The main diagnosis is a gray code for medical insurance or a code that cannot be used as the main diagnosis:
Medicare 2Version 0 of the diagnosis and surgery have deliberately filled the background with gray codes, which are mostly used for statistical use, not used for the expression of clinical diseases, and are not recommended for diagnosis and surgical operations.
In most areas, it is regarded as an invalid code in the grouper and cannot be recognized by the grouper, and the main diagnosis is selected as the medical insurance gray code, and it will not be enrolled.
Certain disease codes cannot be used as the primary diagnosis, such as personal history of malignancy, ostomy status, birth outcome, gestational age, parity, etc., and if these diagnoses are used as the primary diagnosis, they cannot be enrolled.
4.Clinical features do not conform to:
The DRG system also takes into account the patient's clinical characteristics, such as age, gender, disease severity, etc. If the patient's clinical characteristics do not meet the appropriate DRG requirements, the case may be excluded from the DRG group.
5.Grouper coverage is limited, coding jumps:
The grouper is derived from historical data if there is no corresponding primary diagnosis in the historical data (even Medicare 2Version 0 with and not gray), the main operation, or the main diagnostic-action combination, may be classified as unenrolled.
The case is not enrolled if the primary diagnosis does not belong to any MDC in the local grouper or does not have access to any ADRG;The main diagnosis is MDC, the main surgery is not, and it is divided into the internal medicine group of the main clinicThe main diagnosis is the MDC to which it belongs, and there is also the main surgery, but the two are not in the same MDC, and there is a high probability that they will enter the QY group. The specific rules vary from place to place, and need to be analyzed on the actual situation.
After the analysis of the non-enrolled cases in the hospital, the main reasons for the above 3 main diagnoses were the use of the medical insurance gray code and the clinical characteristics of the 4 reasons did not match.
The primary diagnosis with 105 medical records was z51100 is the first course of tumor chemistry, but this code is gray code, can not be selected, should be clear preoperative and postoperative and chemotherapy purposes, adjusted to the specific chemotherapy diagnosis.
Something similar is S61100 open finger wounds with nail lesions, p59900 neonatal jaundice etc.
The main diagnostic non-enrollment statistics table due to gray code.
Medicare version 20 code table.
The other category is non-enrollment caused by non-compliance with clinical characteristics. For example, p05102 full-term sample low weight infants, p05101 samples These two diagnoses were grouped into the PT1 preterm infant (birth weight 1500-2499 g) group, but the birth weight of the newborns in these cases was not between 1500 and 2499 g, and the clinical features did not match, so they could not be enrolled.
CHS-DRG should consider age, weight, gestational age, and symptoms at the same time when enrolling neonates, and special attention should be paid to filling in the first page.
The non-enrollment statistics table due to limited grouper coverage is limited.
Finally, develop a solution based on the cause.
In view of the problem of medical insurance gray code, it is necessary for the medical record room and the information department to block or warn the medical insurance gray code, and the medical record department can combine the data to the pediatric and neonatal departments to preach and remind doctors to correctly understand the medical insurance gray code and choose the appropriate diagnosis.
In view of the non-enrollment caused by the non-compliance of clinical characteristics, it is necessary to jointly sort out the special precautions of each specialty in the medical records and medical insurance, do a good job in admission training, and if conditions permit, logic verification rules can be formed, so that the information system can automatically verify and warn.
In addition, the authors suggest that doctors and functional departments take the following measures for non-enrolled cases:
Physicians can modify the questions for non-enrolled cases in the following ways:
1.Improving the accuracy of diagnostic and surgical procedures:
Doctors should strengthen the control of the accuracy of diagnosis and surgical procedures to avoid erroneous data that may prevent cases from entering the DRG group.
2.Enhance the recording and reporting of clinical features:
Physicians should document and report the patient's clinical characteristics in detail to ensure compliance with DRG requirements, thereby increasing the chances of a case being admitted to the DRG group.
3.Improve the understanding and application of DRG systems:
Physicians should have an in-depth understanding of the principles and requirements of the DRG system and familiarize themselves with the characteristics and regulations of different DRG groups in order to better manage and classify cases.
Functions can take the following steps to address the issue of non-enrolled cases:
1.Regular training and mentoring:
The functional department introduces the relevant knowledge and requirements of the DRG system to medical personnel to help them better understand and apply the DRG system.
2.Review and Supervision:
Functional departments can review and supervise the diagnosis and surgical operations of medical institutions, and can assess the enrollment rate to ensure that it meets the requirements of the DRG system and reduce the occurrence of non-enrolled cases.
3.Support & Assistance:
Functional departments can provide technical guidance, data analysis, and consulting services to medical institutions.
4.MDT Collaboration:
In view of the limited coverage of the grouper, it is necessary for the medical insurance joint performance department and the quality control department to remove this part of the cases from the negative assessment of doctors, and doctors cannot be punished.
Secondly, the medical insurance should make a good record and give feedback to the medical insurance bureau in a timely manner to promote the improvement and optimization of the regional grouper.
It is believed that through the joint efforts of doctors, functional departments and medical insurance departments, these problems can be gradually improved and solved, the accuracy and fairness of the DRG system can be improved, medical resources can be better managed, and the quality and efficiency of medical care can be improved.
* |Health community.
Edit |Fu Meiru Zhang Wenqing.
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