National Healthcare Security Administration Reveals Typical Cases of Abnormal Data: 73-Year-Old's "Painless Egg Retrieval"

Deep News
Sep 29

In the era of big data, any illegal or irregular activities cannot escape the "watchful eye of data." Leveraging the national unified medical insurance information platform, the National Healthcare Security Administration has precisely investigated multiple typical cases of abnormal data, ensuring that each case discovered is investigated, handled, and rectified. The empowering role of medical insurance data in supervision continues to be prominent.

**Mysterious Unknown Doctors Prescribing Large Quantities of Drugs**

Routine drug data monitoring revealed that a certain doctor prescribed nicotinamide with a total amount more than 200 times higher than the national average. This abnormal data caught the attention of medical insurance departments, which quickly organized regulatory forces to conduct focused screening of the corresponding hospital's medical insurance settlement data, discovering numerous suspicious data points. A medical insurance fund inspection team was immediately deployed.

The investigation found that the abnormal prescribing volume of nicotinamide was caused by a "data quality collapse." Some inpatient expense data uploaded by the hospital did not include real doctor names in the prescribing doctor field, but were filled with "unknown" during data processing. After the data was uploaded to the national medical insurance information platform, the combined nicotinamide prescribing volume of multiple "unknown" doctors at the hospital triggered abnormal alerts, exposing the overlapping problems of irregular data reporting by designated medical institutions and the absence of data verification mechanisms.

The inspection team maintained a fact-based approach, provided feedback on data issues to the inspected hospital and local medical insurance departments, and required rectification without imposing penalties. For other illegal and irregular uses of medical insurance funds discovered during the inspection, they were handled in accordance with laws and regulations.

**Elderly Patients Undergoing Assisted Reproduction**

Daily data monitoring discovered that one hospital conducted "painless egg retrieval" for a 73-year-old patient, while another hospital performed "in vitro fertilization" for an 86-year-old patient. After discovering these issues, medical insurance departments dispatched personnel for on-site verification the same day.

Verification revealed that the 73-year-old patient was actually undergoing "painless gastroenteroscopy," but when the doctor filled in the diagnosis, they only entered the words "painless" and mistakenly selected "painless egg retrieval" from the dropdown options. The 86-year-old patient actually suffered from "renal failure," but due to the same initial letters "SGNSJ" as "in vitro fertilization," the doctor only entered the initial letters when filling in the diagnosis and did not carefully check the dropdown options, leading to the error.

Inspection personnel identified the problems on-site and required designated medical institutions to improve data verification mechanisms, assist medical personnel in accurately filling out diagnostic and treatment information, and upload accurate data to medical insurance departments to avoid wasting regulatory resources.

**Batch Prescriptions Exposing Problem Clues**

Medical insurance fund supervision data analysis found that a certain doctor prescribed multiple semaglutide prescriptions for different patients within one minute. The medical insurance fund inspection team conducted targeted investigations based on these clues, precisely identifying suspected illegal activities such as forging medical records.

The investigation revealed that a pharmaceutical representative had collected dozens of social security cards and used these insured persons' identities to visit hospitals. The doctor, knowing about the identity fraud, still cooperated long-term by issuing outpatient follow-up prescriptions diagnosed as diabetes with poor metformin treatment effectiveness and recommending semaglutide use. The pharmaceutical representative subsequently went to designated retail pharmacies to use the cards collectively, purchasing large quantities of semaglutide injection using outpatient pooling funds. The inspection team further retrieved historical medical records of some insured persons and found that some had never had diabetes history or diabetes medication records.

The hospital conducted comprehensive self-inspection based on the discovered problems and took measures against the doctor including suspending contract renewal procedures, requiring on-duty training, and deducting personal performance bonuses. Suspected illegal issues have been transferred to relevant departments for further investigation.

**Male Patients Treated for Female Conditions Triggering Logical Contradictions**

Medical insurance fund supervision data analysis found that over a hundred male patients at a certain hospital had hysteroscopy usage fee settlement records, triggering gender and treatment project logical contradiction alerts. The medical insurance fund inspection team conducted targeted investigations based on these clues.

Through on-site verification of the hospital's information system and expense lists, the problem was found to be incorrect code correspondence: when the hospital entered the "ureteroscopy" service item, it should have correctly matched national and local project codes to generate local medical service project codes and uploaded them to the medical insurance center for settlement. However, in actual operation, the local code for "ureteroscopy" was confused with the local code for "hysteroscopy," leading to incorrect local code associations and erroneously generating "male patients receiving hysteroscopy examination" abnormal records in the medical insurance system. It should be noted that patients actually received ureteroscopy treatment, and related charges also met standards, but due to incorrect code correspondence, obvious contradictions appeared at the data level.

The medical insurance fund inspection team verified that medical insurance funds suffered no losses and required the hospital to immediately rectify technical issues. Local medical insurance departments organized relevant training for designated medical institutions' informatization construction and conducted self-inspection and self-correction citywide to further promote precise standard implementation.

**Strengthening Data Quality Defense Lines**

The above cases reveal that some designated medical and pharmaceutical institutions still have shortcomings in the most basic and critical aspect of daily management—data quality management. Any slight deviation in data quality automatically triggers medical insurance alerts, leading to on-site medical insurance inspections, ultimately resulting in dual waste of medical insurance supervision and medical and pharmaceutical institution management costs.

As the primary responsible parties for data quality, designated medical and pharmaceutical institutions must strengthen strict auditing before data upload and routine dynamic verification, promptly addressing discovered problems and eliminating risks at the source. Medical insurance departments at all levels need to leverage technical advantages, improve intelligent monitoring and cross-verification mechanisms, and build closed-loop management systems for problem discovery, reminders, and handling. Designated medical and pharmaceutical institutions and medical insurance departments should collaborate closely, respectively managing "entry checkpoints" and "monitoring checkpoints" to jointly protect the public's "medical expenses" and "life-saving money."

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