Clinical Decision Support Statistics 2024 – Everything You Need to Know

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Are you looking to add Clinical Decision Support to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Clinical Decision Support statistics of 2024.

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Best Clinical Decision Support Statistics

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Clinical Decision Support Usage Statistics

  • CDSS usage rates ranged from 68.5% to 100% among solo or non solo primary care practices owned by physicians or physician groups that have electronic medical records /electronic health records and 44.7% to 96.1%, regardless of EMR/EHR status. [0]

Clinical Decision Support Market Statistics

  • Vancouver, British Columbia, June 14, 2021 The global water treatment chemicals market is expected to reach USD 71.48 Billion by 2027, according to a new report by Emergen… [1]

Clinical Decision Support Latest Statistics

  • all recommended guidelines were adhered to) was accomplished for 15% of AOM and 5% of OME visits during the baseline period. [2]
  • There was marked variation in use of the CDS system, ranging from 5% to 45% visits across practices. [2]
  • In 2013, an estimated 41% of U.S. hospitals with an EHR, also had a CDSS, and in 2017, 40.2% of US hospitals had advanced CDS capability. [3]
  • Errors involving drug drug interactions are cited as common and preventable, with up to 65% of inpatients being exposed to one or more potentially harmful combinations. [3]
  • The CDSS could switch 91.6% of 202 medication consultations automatically, with no errors, increasing safety, reducing workload and reducing cost for providers. [3]
  • Through 24 input fields which include symptoms and diagnostic test outputs, they achieved 93% accuracy compared to experts at identifying motor, sensory, mixed neuropathies, or normal cases. [3]
  • This was done for urinary bladder tumor grading and estimating recurrence, with up to 93% accuracy. [3]
  • Studies have found that practitioners with more experiential knowledge are less likely to use, and more likely to override CDSS.84 Studies have found up to 95% of CDSS alerts are inconsequential, and often times physicians disagree with or distrust alerts. [3]
  • Up to 74% of those with a CDSS said that financial viability remains a struggle. [3]
  • ConclusionIn the USA, a CDSS, especially under the categories of basic preventive reminders and drug interaction alerts, is used routinely between 68% and 100% in primary care if a practice is entirely EMR/EHR based. [0]
  • The CDS function with the greatest use is basic medication screening, which increased from 52% of clinics nationally in 2014 to 61% in 2016. [4]
  • Use rates increased between 2014 and 2016 for all 7 CDS functions, with increases ranging from 4 percentage points to 13 percentage points. [4]
  • In 2016, the rate of use was highest for basic medication screening (61%), clinical guidelines and protocols (54%), and preventive medicine (57%). [4]
  • Lower use rates were observed for diagnostic result alerts (42%), remote device alerts (19%), incorporation of community based electronic health record data into rules engines (26%), and genomics profiling in orders (9%). [4]
  • A 2015 survey found that 77.9% of office based physicians reported using a certified EHR.4 EHRs include a range of health IT functionalities that can be adopted separately. [4]
  • The survey covered health system affiliated clinics in 50 states, the District of Columbia, and Puerto Rico, providing information on more than 75% of US health system–associated ambulatory care clinics. [4]
  • We excluded clinics (n = 980; 4.85%). [4]
  • We used the HIMSS survey descriptive data to classify ambulatory clinics according to size , clinic type , and health system type. [4]
  • The number of reporting health systems declined by 113 (7%). [4]
  • Nearly all ambulatory clinics (96%). [4]
  • The majority of clinics provided primary care (64% in 2016) and were associated with a multihospital system (72% in 2016). [4]
  • For every function of CDS, ambulatory clinic use increased between 2014 and 2016, ranging from an increase of 4 percentage points in genetics profiling used in orders to an increase of 13 percentage points for diagnostic result alerts. [4]
  • The portion of ambulatory clinics using any form of CDS increased from 53% in 2014 to 62% in 2016. [4]
  • Use of CDS was greatest for basic medication screening (61% in 2016), clinical guidelines and protocols (54% in 2016), and preventive medicine (57% in 2016). [4]
  • CDS use was least for genomics profiling used in orders (9% in 2016). [4]
  • This percentage decreased from 61% of health systems reporting no CDS use in 2014 to 55% in 2016. [4]
  • Of the 7 CDS functions, basic medication screening had the largest share of health systems reporting use by all their clinics (32% in 2016) and reporting use by some or all their clinics (44% in 2016). [4]
  • Clinic size was associated with a small to moderate increased likelihood of use for all CDS functions, except remote device alerts, ranging from 2.1% to 10.2%. [4]
  • Of health system–affiliated ambulatory clinics, 62% were using 1 or more of the 7 examined CDS functions. [4]
  • The portion of health systems with at least some clinics using 1 or more CDS function was 45%. [4]
  • Use and yield of CT pulmonary angiography were compared before and after CDS implementation in August 2007. [5]
  • Of 338,230 patients presenting to the ED, 6838 (2.0%). [5]
  • Quarterly CT pulmonary angiography use increased 82.1% before CDS implementation, from 14.5 to 26.4 examinations per 1000 patients between October 10, 2003, and July 31, 2007. [5]
  • After CDS implementation, quarterly use decreased 20.1%, from 26.4 to 21.1 examinations per 1000 patients between August 1, 2007, and September 30, 2009. [5]
  • Overall, 686 (10.0%). [5]
  • Subject data such as Apnea Hypopnea Index, number of epochs/% of total overnight sleep study to be reviewed by clinician, and Cohen’s Kappa are shown in Table 1. [6]
  • These automated + substitution results are illustrated in Fig. 1a, for all 20 subjects, stratified by their OSA severity class, along the % of each respective study marked for uncertainty review. [6]
  • For example, the epoch could be labeled with the most probable output, or it could be labeled with the two most likely outputs, e.g. wake vs. N1 sleep. [6]
  • 20 One the one hand, the abundance of data likely to be generated by wearable technologies will overwhelm existing capabilities of human review. [6]
  • After wholenight sleep hypnograms were estimated on a 30 s basis, the a posteriori probabilities of being in any of the five states were calculated for each epoch using the forward−backward algorithm for hidden Markov models. [6]
  • We specified the threshold of uncertainty at Sthreshold = 1 bit, pertaining to a scenario where two of the possible states are equally likely and all other states are unlikely to occur . [6]
  • To assess the accuracy of uncertainty classification, we created a confusion matrix illustrating the percentage of all epochs in which the algorithm was certain about its sleep stage estimate. [6]
  • We observed that of those epochs scored with certainty, stages W (87%) and N2 (90%). [6]
  • For the remaining three stages , we found the algorithm estimated these epochs with certainty, but fell short in accuracy (73, 60, and 65%, respectively). [6]
  • Similarly and conversely, we analyzed the percentage of all epochs for which the algorithm was uncertain. [6]
  • Another way to interpret this uncertain matrix is that the algorithm correctly marked uncertain estimates in REM and N3 staging that would lead to a different stage 75 and 76% of the time, respectively. [6]
  • After a 16 min manual review of only 18% of the study , Cohen’s Kappa increased to K = 0.76. [6]
  • K = 0.70, 18% of study to review. [6]
  • For example, ) can be estimated by dividing to , where represents the count of data instances where Y = y1, and , and represents the count of data instances where and. [7]
  • In Fig. 6, the variables shown by ellipses are unknown variables that will be estimated by the model. [7]
  • When operated at 80% and 90% sensitivity, the specificity was 81% and 70% respectively. [7]
  • The AUROC was 0.68, the specificity was 29% and 45% at the 90% and 80% sensitivity levels, and the HL test indicated poor calibration. [7]
  • When operated at 90% and 80% sensitivity, its specificity was 40% and 60% respectively. [7]
  • Moreover, the variance estimated from the auxiliary parameter learning model could be used to show how well the parameters are understood. [7]
  • Multivariate anomaly detection methods can detect changes that involve a subset of observables.22 Bayesian changepoint models provide an estimated probability of occurrence of a changepoint at each instant in a time series. [8]
  • This rule identifies immunocompromised adults <65 years old who did not receive Pneumococcal Conjugate Vaccine according to existing guidelines and suggests that be ordered for these adults. [8]
  • There has been a rapid expansion of EHR use since the enactment of HITECH, with an increase from 48 percent EHR use in officebased practices in 2009 to 72 percent office based practice use by 2012 [1]. [9]
  • Intervention physicians complied at 61 percent vs. 49 percent. [9]
  • in controls for fecal occult blood testing and at 54 percent vs. 47 percent for ordering mammograms [9]. [9]
  • At 6 months post fracture, reminders resulted in 51.5 percent of patients receiving recommended osteoporosis care vs. 5.9 percent in controls. [9]
  • Seventyfour percent of the patients in the reminder group vs. 66 percent of patients in the control group were given β. [9]
  • TREAT prescribed appropriate antibiotic use in 70 percent of cases vs. 57 percent of cases for physicians. [9]
  • When patients were treated based on TREAT advice on hospital wards, the odds ratio for receiving appropriate treatment was 3.40 compared with controls . [9]
  • Intervention providers ordered the recommended tests 46.3 percent of the time vs. 21.9 percent in the control physician group. [9]
  • Fewer intervention patients initiated practice consultations with their providers during this period, 22 percent in intervention vs. 34 percent in controls OR .59. [9]
  • , and fewer intervention patients suffered acute asthma exacerbations, 8 percent vs. 17 percent, OR .43. [9]
  • Having an up to date asthma care plan increased 14 percent and use of spirometry increased by 6 percent in the intervention suburban practices [22]. [9]
  • A statistically significant cost savings of 11 percent for hospitalizations and 27 percent for ER visits was also realized [23]. [9]
  • The mean HbA1c level was 9.9 percent in the intervention group prior to this program and decreased by 1.9 percent in the intervention group over a year vs. controls decreasing .7 percent. [9]
  • According to the National Academy of Medicine, there’s a 17 year gap between the discovery of potentially lifesaving information and its widespread translation into practice. [10]
  • In addition, a couple of studies in the New England Journal of Medicine discovered that patients are treated with care consistent with the evidence only about 50 percent of the time. [10]
  • Add to that equation studies in JAMA showing about a third of all healthcare costs are waste, and 10 percent of healthcare is overtreatment, in which the harm exceeds the benefit. [10]
  • It concluded that when an organization provides CDS is part of its workflow, it’s 112 times more likely to improve care than without CDS. [10]
  • A 2019 study showed that antidepressants and antipsychotics with high anticholinergic properties can increase the risk of dementia by 50 percent when taken over a three. [10]
  • The mean number of ED visits per patient in the tested vs. untested group was 0.25 vs. 0.40 at 30 days (RR, 0.62; 95% CI, 0.31–1.21; P = 0.16). [11]
  • (RR, 0.58; 95% CI, 0.34–0.99; P = 0.045). [11]
  • Of the total 124 drug therapy recommendations passed on to clinicians, 96 (77%). [11]
  • More than 85% of patients have significant genetic variation in the cytochrome P 450 genes that metabolize the majority of the most commonly prescribed medications [4, 5]. [11]
  • An estimated 35% of seniors experience ADEs, nearly half of these preventable [10] and 10–17% of hospitalizations of older patients are directly related to ADRs [11]. [11]
  • Additionally, patients aged 60 years and older account for 51% of ADR related deaths [12, 13]. [11]
  • Interactions involving genes cause approximately 47% (25% were DGIs and 22% were DDGIs). [11]
  • Pharmacogenetic profiling reduced hospitalizations (39%) and ED visits (71%). [11]
  • Pharmacogenetic testing included PCR based assays to detect all common and rare variants with known clinical significance at analytical sensitivity and specificity greater than 99% [18]. [11]
  • The timetoevent outcomes were further described using Kaplan Meier estimates and hazard ratios estimated from the Cox proportional hazards model. [11]
  • 81.8% of the patients were 65 years and older. [11]
  • The percentage of female patients in the tested group and untested group were 56.1% and 67.9% respectively. [11]
  • The population in the area where the trial was conducted is predominately white—86.8% in 2010 [25]. [11]
  • The average pharmacogenetic risk, the likelihood that testing would reveal substantial gene based drug interactions, for the tested and untested groups was 33.2% and 34.3% respectively. [11]
  • As shown in Table 3, the mean number of outcomes per patient in the tested vs. untested group for re hospitalizations was 0.33 vs. 0.70 , 0.48; 95% confidence interval , 0.27–0.82; p = 0.007); ED visits was 0.39 vs. 0.66. [11]
  • (RR, 0.58; 95% CI, 0.34–0.99; p = 0.045); re hospitalizations + ED visits was 0.54 vs. 1.04 (RR, 0.52; 95% C, 0.32–0.86; p = 0.01). [11]
  • The testing reduced the number of rehospitalizations, ED visits, and composite number of re hospitalization + ED visits at 60 days by 52%, 42% and 48% respectively. [11]
  • The true rehospitalization, ED visits, and composite number of re hospitalization + ED visits reduction in a population similar to this study will likely fall between 18% to 73%, 1% to 66%, and 14% to 68% (95% CI). [11]
  • Six deaths were observed in the untested group versus one death in the tested group, an 85% reduction in the risk of death in the tested group (RR, 0.15; 95% CI, 0.01–0.87; p = 0.054). [11]
  • At 60 days, the mean composite number of re hospitalizations + ED visits + deaths was 0.54 for the tested group versus 1.10 for the untested group (RR, 0.50; 95% CI, 0.30–0.81; p = 0.005). [11]
  • The HR for the timetocomposite event outcomes for rehospitalizations + ED visits and re hospitalizations + ED visits + deaths was 0.59 (95% CI, 0.34–1.02; P = 0.056). [11]
  • (95% CI, 0.33–0.99; P = 0.041), respectively. [11]
  • At 60days, the cumulative rate in the tested versus untested group for rehospitalization rate was 28% vs. 43% . [11]
  • ; cumulative ED visit rate was 32% vs. 49%. [11]
  • A major 52% reduction in the number of rehospitalizations and a 42% reduction in the number of ED visits at 60 days post discharge was observed in the tested versus the untested group. [11]
  • This finding occurred in an HHA that, according to 2015 Home Health Quality Improvement reporting was in the 5th12thpercentile group for the CMS re. [11]
  • Six deaths were observed in the untested group versus one death in the tested group, an 85% reduction in the risk of death. [11]
  • Interestingly, this study had many more severe interactions than the Hocum et al study [16] likely because of higher average drug count. [11]
  • In addition, the mean age, percentage of patients who were 65+ years, and the racial differences were higher in this study. [11]
  • 2028* 47% 25% 19% 9% 2018 52% 23% 18% 7%. [12]
  • %0 Conference Proceedings %T Biomedical Document Retrieval for Clinical Decision Support System %. [13]
  • A Sankhavara, Jainisha %S Proceedings of ACL 2018. [13]
  • , Student Research Workshop %D 2018 %8 jul %I Association for Computational Linguistics %C Melbourne, Australia %F sankhavara2018. [13]
  • Of the studies available, health care costs appear to be more likely to decrease than increase after CDSS implementation, but the usefulness of this evidence is limited by incomplete and inconsistent data.5. [14]
  • These changes improved workflow at the clinic and led to a 25% increase in patient visits since the start of the collaboration. [14]
  • 2027 – North America Dominated the Market with a Share Of 48.1% In 201… [1]

I know you want to use Clinical Decision Support, thus we made this list of best Clinical Decision Support. We also wrote about how to learn Clinical Decision Support and how to install Clinical Decision Support. Recently we wrote how to uninstall Clinical Decision Support for newbie users. Don’t forgot to check latest Clinical Decision Supportstatistics of 2024.

Reference

  1. nih – https://pubmed.ncbi.nlm.nih.gov/31818828/.
  2. globenewswire – https://www.globenewswire.com/news-release/2021/02/02/2167921/0/en/Clinical-Decision-Support-Systems-Market-Size-to-Reach-USD-2-563-7-Million-by-2028-Global-Analysis-CDSS-Industry-Statistics-Revenue-Demand-and-Trend-Analysis-Research-Report-by-Eme.html.
  3. nih – https://pubmed.ncbi.nlm.nih.gov/23478860/.
  4. nature – https://www.nature.com/articles/s41746-020-0221-y.
  5. ajmc – https://www.ajmc.com/view/trends-in-the-use-of-clinical-decision-support-by-health-system-affiliated-ambulatory-clinics-in-the-united-states-20142016.
  6. nih – https://pubmed.ncbi.nlm.nih.gov/22187633/.
  7. nature – https://www.nature.com/articles/s41746-021-00515-3.
  8. sciencedirect – https://www.sciencedirect.com/science/article/pii/S1532046414001816.
  9. oup – https://academic.oup.com/jamia/article/25/7/862/4995314.
  10. nih – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4031792/.
  11. healthcatalyst – https://www.healthcatalyst.com/insights/clinical-decision-support-patient-centered-roadmap/.
  12. plos – https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0170905.
  13. statista – https://www.statista.com/statistics/871231/cdss-market-share-by-type-worldwide/.
  14. aclanthology – https://aclanthology.org/P18-3012.
  15. cdc – https://www.cdc.gov/dhdsp/pubs/guides/best-practices/clinical-decision-support.htm.

How Useful is Clinical Decision Support

At its core, CDS aims to assist healthcare providers in making well-informed decisions by presenting them with relevant clinical information and guidelines tailored to specific patient cases. This support can range from drug interaction alerts and dosing recommendations to diagnostic suggestions and treatment pathways. By integrating relevant patient data, medical literature, and best practices, CDS offers a comprehensive overview of a patient’s health status and history, empowering providers to make timely and accurate decisions.

The usefulness of CDS systems is evident in the myriad of ways they benefit healthcare professionals and patients alike. For healthcare providers, CDS enhances clinical decision-making by offering timely guidance and reducing the risk of potential errors. With quick access to pertinent information and decision-making support, clinicians can streamline their workflows, optimize outcomes, and improve patient safety. Additionally, CDS helps standardize care practices across various settings, fostering consistency and adherence to evidence-based guidelines.

Furthermore, CDS systems have proven to be indispensable tools for managing complex patient cases and rare medical conditions. By offering personalized recommendations tailored to individual patient profiles, providers can explore various treatment options and interventions that may not be readily apparent. This personalized approach to care improves patient outcomes, increases satisfaction, and ultimately drives better healthcare delivery.

From a patient perspective, the utility of CDS is equally significant. Patients benefit from the increased accuracy and precision in their diagnoses and treatment plans, resulting in better health outcomes and reduced healthcare costs. With CDS facilitating communication and collaboration among providers, patients receive more coordinated and comprehensive care that considers all aspects of their health.

Moreover, CDS systems play a vital role in advancing medical research, quality improvement initiatives, and population health management. By aggregating anonymous patient data and analyzing trends and patterns, healthcare organizations can identify areas for improvement, implement targeted interventions, and track performance metrics effectively. This data-driven approach to healthcare delivery paves the way for continuous innovation and development in the field.

However, despite the numerous benefits of CDS systems, challenges exist in their implementation and adoption across healthcare settings. Factors such as usability, interoperability, training, and resistance to change can hinder the effectiveness of CDS tools and limit their utility. To maximize the potential of CDS systems, healthcare organizations must prioritize user engagement, seamless integration with existing workflows, and ongoing education and support for providers.

In conclusion, the usefulness of Clinical Decision Support cannot be understated in today’s healthcare landscape. From enhancing clinical decision-making and improving patient outcomes to driving innovation and quality improvement, CDS systems offer a wealth of advantages for healthcare providers and patients alike. Although challenges may exist in their implementation, the potential of CDS to transform healthcare delivery and achieve better outcomes is undeniable. As technology continues to evolve, so too will the capabilities and impact of CDS systems in shaping the future of healthcare.

In Conclusion

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