OBJECTIVE To explore the transformation of the dispensing and drug pickup process in traditional Chinese medicine pharmacy （TCM Pharmacy） in our hospital based on data-intelligence-driven， aiming to improve pharmacists’ work efficiency and patients’ drug pickup experience. METHODS Value stream mapping and journey mapping were used to systematically identify non-value-added links in pharmacists’ dispensing process and key pain points in patients’ drug pickup under the traditional process. An intelligent dispensing and drug pickup system for the TCM Pharmacy was developed based on the C# and Android television platforms， and a machine-learning model was adopted to predict patients’ drug pickup waiting time. A comprehensive evaluation was performed from three perspectives： system performance， prediction accuracy， and satisfaction of pharmacists and patients. RESULTS The system successfully streamlined non-value-added links such as “waiting for writing on the board” and “searching for drugs”， and realized multimodal dynamic prompts of dispensing status through auditory （number calling） and visual （television terminal） channels. The constructed model for predicting drug pickup waiting time exhibited good fitting degree and generalization ability （mean absolute error＝4.28 min， R 2 ＝0.882）. The comprehensive satisfaction scores of pharmacists and patients in the traditional mode were significantly increased from （70.99±1.74） and （73.58±1.98） to （90.02±1.30） and （88.61±2.08） in the new system， respectively （ P ＜0.01）. CONCLUSIONS The transformation of the intelligent drug dispensing and pickup system for TCM pharmacy based on data-intelligence-driven effectively improves the efficiency of pharmacists’ dispensing work， realizes process transparency and waiting time predictability， and significantly enhances patients’ drug pickup experience.

Objective:To explore the efficacy and safety of sivelestat sodium in patients with acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU).Methods:Sixty patients with ARDS admitted to the ICU of the Fuyang Hospital Affiliated to Anhui Medical University from August 1, 2023 to November 1, 2024 were selected and divided into the control group (conventional treatment, 30 cases) and the sivelestat sodium group (treated with sivelestat sodium in addition to conventional treatment, 30 cases) by the random number table method. The clinical data such as inflammatory factors, blood gas analysis indicators, Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ score and Sequential Organ Failure Assessment (SOFA) score of the two groups of patients before treatment and 3 days after treatment were compared. The prognostic indicators such as mechanical ventilation time, ICU stay time, total hospital stay time, 28-day mortality rate and clinical efficacy of the two groups of patients were compared.Results:Before treatment, there were no statistically significant differences in inflammatory factors, blood gas analysis indicators, APACHE Ⅱ score and SOFA score between the two groups of patients (all P>0.05). After 3 days of treatment, the improvement degrees of APACHE Ⅱ score, SOFA score, arterial partial pressure of oxygen (PaO 2), oxygenation index (PaO 2/FiO 2), procalcitonin (PCT), interleukin-6 (IL-6), and C-reactive protein (CRP) in the sivelestat sodium group were all greater than those in the control group. The differences were all statistically significant (all P<0.05); The mechanical ventilation time [(5.31±4.12) d vs (7.17±2.32)d] and ICU stay [(6.31±3.42)d vs (8.93±5.26)d] of patients in the sivelestat sodium group were significantly shorter than those in the control group, and the differences were statistically significant (all P<0.05). There was no statistically significant difference in the 28-day mortality rate between the sivelestat sodium group [20.00%(6/30)] and the control group [43.33%(13/30)] ( P>0.05). The total effective rate of treatment in the sivelestat sodium group was significantly higher than that in the control group [80.00%(24/30) vs 56.67%(17/30)], and the difference was statistically significant (χ 2=4.167, P=0.041). Conclusions:Sivelestat sodium is helpful in improving the physiological parameters of patients with ARDS, effectively reducing the levels of inflammatory factors in the body, shortening the duration of mechanical ventilation and ICU stay, but has no significant effect on the 28-day mortality rate.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To explore the efficacy and safety of sivelestat sodium in patients with acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU).Methods:Sixty patients with ARDS admitted to the ICU of the Fuyang Hospital Affiliated to Anhui Medical University from August 1, 2023 to November 1, 2024 were selected and divided into the control group (conventional treatment, 30 cases) and the sivelestat sodium group (treated with sivelestat sodium in addition to conventional treatment, 30 cases) by the random number table method. The clinical data such as inflammatory factors, blood gas analysis indicators, Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ score and Sequential Organ Failure Assessment (SOFA) score of the two groups of patients before treatment and 3 days after treatment were compared. The prognostic indicators such as mechanical ventilation time, ICU stay time, total hospital stay time, 28-day mortality rate and clinical efficacy of the two groups of patients were compared.Results:Before treatment, there were no statistically significant differences in inflammatory factors, blood gas analysis indicators, APACHE Ⅱ score and SOFA score between the two groups of patients (all P>0.05). After 3 days of treatment, the improvement degrees of APACHE Ⅱ score, SOFA score, arterial partial pressure of oxygen (PaO 2), oxygenation index (PaO 2/FiO 2), procalcitonin (PCT), interleukin-6 (IL-6), and C-reactive protein (CRP) in the sivelestat sodium group were all greater than those in the control group. The differences were all statistically significant (all P<0.05); The mechanical ventilation time [(5.31±4.12) d vs (7.17±2.32)d] and ICU stay [(6.31±3.42)d vs (8.93±5.26)d] of patients in the sivelestat sodium group were significantly shorter than those in the control group, and the differences were statistically significant (all P<0.05). There was no statistically significant difference in the 28-day mortality rate between the sivelestat sodium group [20.00%(6/30)] and the control group [43.33%(13/30)] ( P>0.05). The total effective rate of treatment in the sivelestat sodium group was significantly higher than that in the control group [80.00%(24/30) vs 56.67%(17/30)], and the difference was statistically significant (χ 2=4.167, P=0.041). Conclusions:Sivelestat sodium is helpful in improving the physiological parameters of patients with ARDS, effectively reducing the levels of inflammatory factors in the body, shortening the duration of mechanical ventilation and ICU stay, but has no significant effect on the 28-day mortality rate.

Objective To investigate the value of patient data-based real-time quality control(PBRTQC)in internal quality control(IQC)for blood cells analysis based on the data from patients.Methods The data of patients'blood cells,including white blood cell count(WBC),hemoglobin(Hb),red blood cell count(RBC),hematocrit(HCT),mean corpuscular volume(MCV),mean cor-puscular hemoglobin(MCH),mean corpuscular hemoglobin concentration(MCHC),and platelet(PLT)were collected from August 1,2023 to February 26,2024,and the extracted patient data were analyzed on the AI-based real-time quality control intelligent moni-toring platform.The corresponding IQC data for this period were reviewed,and the results of PBRTQC and IQC were compared and an-alyzed.The causes of the emerging warning or alarm prompts were checked and analyzed to explore the application value of PBRTQC in the IQC process of blood cell analysis.Results It is found that when the quality control product was unstable due to overlong opening time of the reagent or improper storage conditions,and the performance changes of the operating system during the detection process,the PBRTQC intelligent monitoring platform was able to issue risk warning or alarm prompt in advance.PBRTQC may have certain limi-tations,such as the error of red blood cell count,which need to be identified.Conclusion PBRTQC is superior to IQC in blood cell analysis and may play a complementary role in IQC.Meanwhile,it is necessary to exclude the possibility that PBRTQC is significantly influenced by the patient population in medical laboratories.

Objective To analyze the prescription patterns of Professor Huang Feng,a nationally renowned traditional Chinese medicine(TCM)practitioner,in treating osteomyelitis using data mining methods.Methods Prescription data from effective medical records of osteomyelitis treated by Professor Huang Feng between January 2018 and December 2022 were collected and screened.Microsoft Excel,SPSS Modeler 18.0,and SPSS Statistics 25 were used to analyze the frequency and the distribution of properties,flavors,and meridian tropism of prescribed medications,along with association rule analysis and cluster analysis of high-frequency drugs.Results A total of 137 prescriptions involving 86 Chinese medicinals were included.Eighteen high-frequency medicinals(frequency＞30 times)were identified,namely Glycyrrhizae Radix et Rhizoma,Astragali Radix,Coicis Semen,Angelicae Sinensis Radix,Smilacis Glabrae Rhizoma,Achyranthis Bidentatae Radix,Bletillae Rhizoma,Rehmanniae Radix,Paeoniae Radix Alba,Dendrobii Caulis,Polygalae Radix,Lablab Semen Album,Corydalis Rhizoma,Angelicae Dahuricae Radix,Drynariae Rhizoma,Sanguisorbae Radix,Poria,and Mume Fructus.Most of the prescribed medicinals were neutral in nature,sweet,bitter,and pungent in flavor,and had the meridian tropism of liver,spleen,and lung meridians.Association rule analysis yielded 67 drug association rules,and the high-support combinations were the drug combinations of Astragali Radix respectively with Coicis Semen,Angelicae Sinensis Radix,Smilacis Glabrae Rhizoma and Achyranthis Bidentatae Radix,reflecting the compatibility principles of supplementing and invigorating qi-blood,activating blood circulation to resolve stasis,and draining dampness to remove toxins.Cluster analysis revealed three core clusters:Cluster 1 consisted of Glycyrrhizae Radix et Rhizoma,Astragali Radix,Coicis Semen,Smilacis Glabrae Rhizoma,Angelicae Sinensis Radix,Bletillae Rhizoma,Paeoniae Radix Alba,Angelicae Dahuricae Radix,Mume Fructus,Polygalae Radix and Sanguisorbae Radix;Cluster 2 consisted of Rehmanniae Radix and Dendrobii Caulis;Cluster 3 consisted of Achyranthis Bidentatae Radix,Lablab Semen Album,Corydalis Rhizoma and Poria.Conclusion For the treatment of osteomyelitis,Professor Huang Feng follows the principle of combining supporting healthy qi with eliminating pathogens,focuses on clearing damp-heat and pathogenic toxins accompanied by activating blood circulation to resolve stasis,and lays stress on adaptation to local condition and activating spleen-stomach to reinforce vital qi.

Objective To investigate the value of patient data-based real-time quality control(PBRTQC)in internal quality control(IQC)for blood cells analysis based on the data from patients.Methods The data of patients'blood cells,including white blood cell count(WBC),hemoglobin(Hb),red blood cell count(RBC),hematocrit(HCT),mean corpuscular volume(MCV),mean cor-puscular hemoglobin(MCH),mean corpuscular hemoglobin concentration(MCHC),and platelet(PLT)were collected from August 1,2023 to February 26,2024,and the extracted patient data were analyzed on the AI-based real-time quality control intelligent moni-toring platform.The corresponding IQC data for this period were reviewed,and the results of PBRTQC and IQC were compared and an-alyzed.The causes of the emerging warning or alarm prompts were checked and analyzed to explore the application value of PBRTQC in the IQC process of blood cell analysis.Results It is found that when the quality control product was unstable due to overlong opening time of the reagent or improper storage conditions,and the performance changes of the operating system during the detection process,the PBRTQC intelligent monitoring platform was able to issue risk warning or alarm prompt in advance.PBRTQC may have certain limi-tations,such as the error of red blood cell count,which need to be identified.Conclusion PBRTQC is superior to IQC in blood cell analysis and may play a complementary role in IQC.Meanwhile,it is necessary to exclude the possibility that PBRTQC is significantly influenced by the patient population in medical laboratories.

Objective To explore how digital and intelligent technologies can optimize the storage allocation and picking paths for Traditional Chinese medicine decoction pieces.Methods Based on prescription information and medicine data from the hospital information system(HIS),this study employs MATLAB programming to use an integer linear programming(ILP)algorithm for optimizing the storage allocation of Traditional Chinese medicine decoction pieces.Additionally,a greedy algorithm is applied to optimize the picking paths to reduce the picking distance for pharmacists.Finally,an independent samples paired t-test is used to analyze the experimental data to verify the significance of the optimizations.Results A total of 7 734 prescriptions were collected from the HIS.The results of storage optimization showed that the total distance before optimization was(87.58±0.29)m,which was reduced to(85.35±0.28)m after optimization(P﹤0.000 1).The picking path optimization results showed that the picking path was(85.35±0.28)m before optimization and(40.06±0.11)m after optimization(P﹤0.000 1).The results of the independent samples paired t-test indicate that the path was significantly reduced after both storage and picking path optimizations,with statistical significance.Conclusion By using digital and intelligent methods,informatics pharmacists can effectively shorten the picking paths for Traditional Chinese medicine decoction pieces,improve dispensing efficiency,and reduce patient waiting times.

Objective To explore how digital and intelligent technologies can optimize the storage allocation and picking paths for Traditional Chinese medicine decoction pieces.Methods Based on prescription information and medicine data from the hospital information system(HIS),this study employs MATLAB programming to use an integer linear programming(ILP)algorithm for optimizing the storage allocation of Traditional Chinese medicine decoction pieces.Additionally,a greedy algorithm is applied to optimize the picking paths to reduce the picking distance for pharmacists.Finally,an independent samples paired t-test is used to analyze the experimental data to verify the significance of the optimizations.Results A total of 7 734 prescriptions were collected from the HIS.The results of storage optimization showed that the total distance before optimization was(87.58±0.29)m,which was reduced to(85.35±0.28)m after optimization(P﹤0.000 1).The picking path optimization results showed that the picking path was(85.35±0.28)m before optimization and(40.06±0.11)m after optimization(P﹤0.000 1).The results of the independent samples paired t-test indicate that the path was significantly reduced after both storage and picking path optimizations,with statistical significance.Conclusion By using digital and intelligent methods,informatics pharmacists can effectively shorten the picking paths for Traditional Chinese medicine decoction pieces,improve dispensing efficiency,and reduce patient waiting times.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.