Bronchopulmonary dysplasia (BPD) is a chronic lung disease affecting extremely preterm neonates, characterized by disruptions in the development of the alveolar and vascular compartments leading to long-term pulmonary complications (1–3). Despite significant progress in neonatal care, the incidence of BPD has remained relatively unchanged, particularly with the increasing survival rates of extremely preterm neonates. To put this problem into perspective, it is essential to note that BPD represents a substantial burden on both healthcare systems (e.g., up to $442,468 per neonate in the first year of life) and affected families (4). The financial healthcare costs associated with managing BPD, coupled with the emotional and physical toll it takes on families, underscore the urgency of addressing this condition.

Interestingly, some studies have suggested that the incidence of BPD may be decreasing among moderately preterm infants, possibly due to improved respiratory support techniques, antenatal steroids, and surfactant replacement therapy (5). However, this positive trend might not hold true for extremely preterm infants. This evolving scenario underscores the importance of innovative approaches as current therapies (e.g., oxygen, bronchodilators, and glucocorticoids) have limited benefits apart from symptom management. There is a critical need not only for newer, more effective therapies for BPD but also for approaches that may predict and potentially identify neonates who would benefit most from targeted or earlier interventions (6–8). By diligently tracking these trends and facilitating personalized care strategies for infants at risk of BPD, we can better navigate the complex dynamics of BPD incidence and improve both the prevention and management of this challenging condition.

Clinical informatics, a rapidly expanding field in biomedical informatics, is transforming healthcare by integrating computational methods with patient data to improve outcomes [(9–11), Figure 1]. This approach has demonstrated significant success in various areas of healthcare. For instance, clinical informatics has played a pivotal role in advancing cancer care, where it facilitates precision medicine by tailoring treatments based on genetic and clinical data, ultimately leading to more effective therapies and improved patient outcomes (12–14). In the realm of critical care medicine, clinical informatics has led to enhanced patient monitoring systems, predictive analytics for the early detection of deteriorating conditions, and streamlined communication among healthcare teams, resulting in better patient management and reduced mortality rates (15–17).

Within neonatal care, clinical informatics has made substantial contributions as well. Two notable examples include retinopathy of prematurity (ROP) screening and neonatal intensive care unit (NICU) workflow optimization. Clinical informatics has been instrumental in developing algorithms that analyze retinal images to identify preterm infants at risk of ROP. By automating this process, clinicians can make quicker and more accurate decisions about when to initiate treatment, preventing vision loss in vulnerable neonates (18–20). Clinical informatics tools have also been employed to optimize workflows in the NICU. These tools assist in managing patient data, monitoring vital signs, and coordinating care among healthcare professionals. Such optimization not only improves the quality of care but also reduces the burden on healthcare providers (21–24). These examples illustrate how clinical informatics is revolutionizing healthcare by harnessing data-driven approaches to enhance patient outcomes across a wide range of medical specialties.

In terms of BPD, clinical informatics presents new and exciting opportunities to enhance clinical therapy. For example, leveraging electronic health records (EHRs) or continuous vital sign data to capture nuanced ventilator or physiologic data can aid in identifying patients for targeted approaches. Machine learning algorithms can also analyze EHRs to identify complex patterns that may predict or subclassify BPD or provide prognostic measures for long-term respiratory outcomes [e.g., need for home oxygen, tracheostomy (25–28)]. Beyond EHRs, mobile health and clinical decision support systems are additional clinical informatics tools that can benefit neonates with BPD.

This review highlights the innovative potential of clinical informatics in improving the management of BPD, specifically in predicting extubation success, identifying clinically relevant phenotypes, and developing clinical decision support tools. While these informatics tools offer a promising avenue to provide clinicians with more accurate and timely information, several challenges need to be addressed. It is important to note that one significant hurdle is the collection of data from multiple institutions. Collaborative efforts to aggregate data from various healthcare facilities can enhance the generalizability of clinical and research findings, enabling a broader understanding of BPD. Additionally, the need for ethical and secure storage of data generated by EHRs poses a critical challenge. Ensuring patient privacy and data security is paramount in clinical informatics, especially when dealing with sensitive neonatal information. By proactively addressing these challenges, clinical informatics can play a pivotal role in advancing our understanding of BPD in neonates and improving patient outcomes. The collaboration among institutions and the implementation of robust data security measures are crucial steps toward harnessing the full potential of informatics tools in enhancing neonatal care for BPD.

The limited advances in the treatment and prevention of BPD can be attributed to several interconnected factors within the field of neonatal care. Firstly, the multifactorial nature of BPD, which involves genetic predisposition, developmental lung immaturity, and various environmental factors, makes it a complex and challenging condition to address comprehensively (29–31). These intricate interactions have hindered the development of targeted therapies, as the underlying mechanisms of BPD remain incompletely understood.

Secondly, the lack of effective interventions to modify the natural course of BPD in its early stages has contributed to the stagnation in therapeutic progress (32). Current treatment options primarily focus on managing symptoms and providing respiratory support, but there is a significant gap in therapies that can halt or reverse the disease progression. This limitation is compounded by the vulnerability of extremely preterm neonates, who often face multiple health challenges beyond BPD, making it challenging to prioritize research efforts in this specific area.

Moreover, conducting clinical trials and research involving neonates, especially extremely preterm infants, presents ethical and logistical challenges (33). The need to balance the potential benefits of experimental treatments with the vulnerability of this patient population has led to cautious and conservative approaches in clinical research. As a result, the development and testing of novel therapeutic strategies for BPD have been slow-paced.

The financial burden associated with neonatal care, particularly the management of BPD, has also played a role in the limited advances in this field (34). Healthcare systems are often strained by the high costs of caring for extremely preterm infants, diverting resources away from research and innovation. This financial constraint has hindered the pursuit of new therapeutic avenues for BPD.

In summary, the relatively slow progress in treating and preventing BPD can be attributed to the complexity of the condition, the lack of effective interventions, ethical considerations in neonatal research, and financial constraints on healthcare systems. Addressing these challenges will be crucial in advancing our understanding of BPD and developing more effective therapies for affected neonates.

Clinical informatics plays a pivotal role in healthcare by integrating advanced computational methods with patient data to improve patient outcomes and enhance the overall quality of care. This rapidly evolving field harnesses the power of data analysis, EHRs, machine learning, and artificial intelligence (AI) to transform healthcare delivery across various medical specialties. This paradigm shift toward data-driven decision-making has proven particularly effective in oncology, where precision medicine and personalized treatment plans are revolutionizing cancer care.

In cancer treatment, clinical informatics has emerged as a game-changer. By analyzing vast datasets encompassing genetic, genomic, and clinical information, healthcare providers can tailor treatments to the specific genetic and molecular characteristics of a patient's cancer. This approach, known as precision oncology, has led to the development of targeted therapies that are more effective and less toxic than traditional chemotherapy. For example, drugs like Herceptin (trastuzumab) have been successful in treating breast cancer patients with HER2-positive tumors (35).

Furthermore, clinical informatics aids in the identification of novel therapeutic targets and the prediction of treatment responses. Through the analysis of genomic data, researchers can pinpoint genetic mutations that drive cancer growth and progression. This knowledge enables the development of new drugs and treatment strategies that directly target these mutations, such as the use of tyrosine kinase inhibitors in the treatment of chronic myeloid leukemia (CML) (36).

Additionally, clinical informatics facilitates the management of large patient data within oncology departments and research institutions. EHRs help streamline the recording and retrieval of patient information, ensuring that healthcare providers have access to up-to-date and comprehensive data and protocols to make informed decisions. This not only improves patient care but also aids in research by providing a wealth of structured data for retrospective analyses and future clinical trials (37). In summary, clinical informatics is reshaping the landscape of healthcare, with cancer treatment serving as a prime example of its transformative potential. By leveraging data-driven insights, precision medicine, and streamlined data management, clinical informatics is improving patient outcomes and transforming the way healthcare is delivered.

Neonatal nutrition is a critical aspect of the care of premature infants, who often require specialized feeding plans. Clinical informatics has helped optimize the nutritional support provided to these vulnerable neonates. EHRs enable healthcare providers to monitor and track the nutritional intake of neonates accurately. These systems allow for the precise recording of feeding volumes, nutritional supplements, growth curves, and caloric intake. By analyzing this data, healthcare teams can tailor nutrition plans to meet the specific needs of each neonate, ensuring they receive adequate nourishment to support growth and development (38). Through data analysis, informatics tools can identify trends in neonatal growth and nutritional status. If a neonate's growth trajectory deviates from the expected, the system can generate alerts, prompting healthcare providers to assess and adjust the nutrition plan accordingly. This proactive approach helps prevent undernutrition and its associated complications in neonates (39, 40).

Another practical example of clinical informatics in neonatology involves neonatal infection control, a significant concern when caring for premature infants. EHRs play a central role in tracking and monitoring neonatal infections. These systems allow healthcare providers to record and analyze data related to infection risk factors, microbiology results, antibiotic usage, and infection outcomes (41). Informatics tools can automatically generate alerts when certain infection risk factors are identified, such as prolonged antibiotic use or central line-associated bloodstream infections (CLABSIs). These alerts prompt healthcare teams to take real-time action, such as adjusting antibiotic regimens or implementing infection control measures. Additionally, informatics systems facilitate the timely retrieval of microbiology results, aiding clinicians in diagnosing and treating infections promptly (42).

Furthermore, clinical informatics supports the surveillance and reporting of neonatal infection rates. By aggregating and analyzing infection data from multiple NICUs, informatics tools enable healthcare professionals to identify trends, outbreaks, and areas for improvement in infection control practices. This data-driven approach helps NICUs implement evidence-based strategies to reduce infection rates and improve neonatal outcomes (43). The Kaiser Permanente sepsis calculator, which has now been implemented in some EHRs, has also helped clinicians calculate risk (44).

Clinical informatics has demonstrated its transformative potential in various healthcare domains, making it prudent to explore its role in BPD prevention and treatment.

BPD is a heterogeneous condition in terms of its pathophysiology, clinical presentation, and treatment response. Hence, there is a compelling rationale for categorizing BPD patients into distinct subgroups or phenotypes based on their unique clinical and molecular profiles. Phenotypes are artificial constructs consisting of observable characteristics or traits of disease (53). Traditionally, BPD severity has been classified based on the requirement for oxygen therapy or positive pressure ventilation at 28 days and/or 36 weeks postmenstrual age (3, 54). However, these approaches fail to capture the heterogeneity of the disease and its underlying mechanisms.

Clinical phenotyping offers an alternative strategy for optimizing therapeutic interventions and refining risk stratification, mirroring the successful subcategorization of conditions like asthma based on clinical presentation. This approach enables more precise guidance for tailored therapies (55). The intricate interplay of prenatal risk factors, including but not limited to pregnancy-induced maternal hypertension, intrauterine growth restriction, genetic predisposition, chorioamnionitis, and other causes of fetal systemic inflammation, creates a dynamic landscape that influences the future clinical phenotype of a neonate (56, 57). These factors, combined with postnatal influences such as prematurity, birth weight, oxidative stress, mechanical ventilation, sepsis, the presence of a clinically significant patent ductus arteriosus, and respiratory microbial dysbiosis, contribute not only to the development of BPD but also shape the challenges clinicians must address. This interplay informs the need for tailored interventions to meet the unique needs of each neonate (58).

An approach to classifying BPD based on phenotypes holds considerable clinical implications, such as clinical trial recruitment, retrospective cohort analysis, outcome studies, and cost analyses (5, 6). Recent studies have applied clustering techniques to describe BPD phenotypes based on various clinical and molecular features. In a study of 76 neonates with severe BPD, Wu et al. stratified phenotypic presentations according to 1) moderate-severe parenchymal lung disease, 2) pulmonary hypertension, and 3) large airway disease (59). The authors defined parenchymal lung disease by scoring severity via chest tomography with angiography. Specifically, the criteria assessed the existence and intensity of ten distinct lung radiographic attributes, employing a scale that spans from 0 to 2. The attributes include hyper-expansion, mosaic attenuation pattern, intercostal bulging, air cysts, bullae, blebs, cyst size, triangular subpleural opacities, distortion and thickening of bronchovascular bundle, consolidation, and a subjective assessment of overall lung disease severity. Pulmonary hypertension was defined by having one or more of the following: a systolic pulmonary artery pressure ≥40 mm Hg, a bidirectional or right-to-left shunt through a patent ductus arteriosus, or a flattened or bowing interventricular septum at the end of systole. Large airway disease included tracheomalacia and/or bronchomalacia documented on bronchoscopy and/or tracheoscopy by a pediatric otolaryngologist or pulmonologist. Outcomes were then described according to the three prespecified phenotypes. Neonates with pulmonary hypertension or large airway disease phenotypes had higher odds of mortality, tracheostomy, or pulmonary vasodilator use at discharge when compared to those classified with parenchymal lung disease (OR 5.4 vs. 5.1 vs. 0.59, respectively).

bpd phenotyping plays a crucial role in not only enhancing the precision of BPD definition but also in improving clinical care and guiding future intervention studies. By employing unsupervised machine learning algorithms, we can achieve a finer stratification of BPD severity, allowing for more tailored and effective therapeutic approaches. Additionally, it is imperative that future models consider the dynamic shifts in clinical phenotypes that occur as interventions are administered or discontinued during the course of hospitalization. This adaptive approach ensures that the therapeutic strategies remain aligned with the evolving needs of the neonate.

Clinical informatics has the potential to make significant strides in the prevention and reduction of the severity of BPD among neonates. One of the key applications is early risk prediction. Informatics tools can analyze a multitude of data points, including gestational age, birth weight, respiratory parameters, and maternal health records, to identify neonates at high risk of developing BPD. By flagging these high-risk cases, healthcare teams can implement preventive measures promptly, such as optimizing respiratory support strategies or initiating lung-protective ventilation strategies. In addition to early risk prediction, clinical informatics can also play a crucial role in improving communication and coordination among healthcare providers involved in the care of neonates with BPD. Informatics tools can facilitate seamless sharing of patient data, treatment plans, and progress updates among neonatologists, pulmonologists, nurses, and other healthcare professionals. This enhanced collaboration can lead to more effective and timely interventions, ultimately reducing the severity of BPD (4).

Clinical informatics can offer a valuable tool in the form of automated weekly reminders and graphical representation of ventilator parameters to assess respiratory progress in neonates with BPD. These reminders can prompt healthcare providers to systematically evaluate the infant’s respiratory status on a regular basis, ensuring that no critical changes or deteriorations go unnoticed. Furthermore, informatics systems can generate graphical displays of ventilator parameters over the past few weeks, allowing clinicians to visualize trends and deviations in respiratory support. Such visualizations can highlight improvements or declines in a neonate's lung function, enabling healthcare teams to make informed decisions regarding adjustments to ventilation strategies or other therapeutic interventions. This proactive approach not only supports timely interventions but also aids in fine-tuning treatment plans to optimize respiratory care and minimize the severity of BPD.

Informatics systems enable continuous monitoring and data analysis of neonatal patients in real-time. By tracking physiological parameters, like oxygen saturation levels and respiratory rates, clinical informatics can detect subtle changes that may indicate evolving BPD (85). When these early warning signs are identified, healthcare providers can adjust treatment plans and interventions, potentially preventing or mitigating the progression of the disease (86). Another critical aspect is the personalization of care plans. Clinical informatics can assist in tailoring therapeutic approaches based on individual patient data. For instance, informatics tools can analyze a neonate's response to specific treatments and medications, enabling the adjustment of therapy to maximize effectiveness while minimizing potential side effects. This personalized approach can optimize outcomes and decrease the overall severity of BPD (47).

Clinical informatics can also contribute to the care of neonates with established BPD by streamlining medication management, integrating clinical data, enabling remote consultations, supporting quality assurance efforts, and engaging patients and their families.

Current data collection and storage methods supporting BPD research are varied. Some databases aim to collect general demographic, clinical, and outcomes data on very low-birthweight infants, while others are dedicated to sponsored multi-site trials investigating BPD as a primary or secondary outcome. Additionally, some databases focus on more granular data derived from a single-site center. Large databases targeting VLBW infants exist both in the US and internationally, and most data is entered manually by principal investigators or trained data abstractors at participating sites (92–96). However, the United Kingdom's National Neonatal Database stands out as an exception due to its use of a common EHR shared among participating sites, allowing data to flow automatically into the database on a quarterly basis. Quality assessment of the extracted data is then published for participants to review (95). A preliminary search conducted on PubMed on January 8, 2024, using the keywords “National Neonatal Research Database (NNRD)” returned 26 publications, primarily centered around neonatal nutrition. Surprisingly, none of the studies identified in this initial search were specifically focused on BPD. We believe future research questions will delve into the utilization of the NNRD to investigate BPD's epidemiology, risk factors, outcomes, and potential interventions.

Despite the existence of these databases, challenges remain in data collection, including the labor-intensive nature of manual data entry, incomplete data, evolving definitions of disease, inability to capture longitudinal data, inherent data bias, data governance, and privacy concerns. These challenges highlight the need for continued work on improving data collection and storage methods to support BPD research (refer to Table 1).

Moreover, without consensus on a minimum standard dataset, comparing and combining data across databases becomes challenging. In addition, the lack of standardized definitions and terminologies hinders cross-site analysis and increases the risk of errors in data interpretation. To address this, initiatives such as the Neonatal Research Network (NRN) have developed standardized definitions for outcomes and clinical variables, which have been adopted by many large databases. Despite these initiatives, there is still a need for more widespread adoption of standardized definitions and terminologies to improve interoperability and facilitate collaboration across different databases and research groups. The use of common data models, such as the OMOP CDM, may help in achieving this goal by providing a standardized structure for data collection and storage that can be shared across different institutions and studies.

Improving the collection and storage of BPD data is crucial for advancing our knowledge of the disease and enhancing patient outcomes. To achieve this goal, it is necessary to leverage automated data extraction and mapping of a minimum dataset to standard definitions and terms. The lack of BPD-specific data granularity in current VLBW databases limits their research potential in answering questions about candidate treatments or management approaches. Standardization of data elements has been recognized by some registries, but many remain unmapped to accepted standard dictionaries such as International Statistical Classification of Diseases and Related Health Problems-10 (ICD-10) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), which limits collaboration and interoperability (96, 97). The UK model demonstrates the potential for automated data extraction with high fidelity and provides a roadmap for streamlining epidemiologic data collection (98, 99). By better capturing demographic, clinical, and outcome data, we can perform retrospective investigations and explore disease incidence and risk factors. Ultimately, this can inform the development of improved management approaches and more effective treatments for patients with BPD.

Due to center-specific variations in patient care and outcomes, multi-center collaborations are necessary to advance our understanding of the disease, identify best practices, and improve patient outcomes (1, 70, 100). Multi-center collaborations offer numerous benefits in BPD research, including a better understanding of disease mechanisms, lung repair, and regeneration. Through these collaborations, disease phenotypes can be better classified and novel therapeutic targets can be better explored. Multi-center collaborations can identify variations in neonatal outcomes between centers, leading to national and international benchmarking, quality improvement activities, and implementation of “best practice” strategies (1, 7, 100).

Multi-center collaborations, exemplified by initiatives like the BPD Collaborative encompassing 27 centers across the USA and one in Sweden, have undeniably propelled BPD research forward and have improved patient care outcomes (7, 58, 100, 101). Within these collaborations, clinical informatics has emerged as a critical component, serving to enhance the efficiency and effectiveness of research endeavors. Clinical informatics systems provide a secure and standardized platform for the exchange of patient data and research findings among participating centers. This data sharing facilitates the aggregation of extensive datasets from diverse clinical settings, empowering researchers to conduct comprehensive analyses spanning multiple institutions. By harnessing the power of data integration, clinical informatics enables the identification of trends and insights that might otherwise remain elusive when working solely with isolated datasets. Furthermore, informatics tools play a central role in harmonizing clinical practices and standardizing data collection protocols across collaborating institutions, ensuring consistent data collection and adhering to predefined standards (58). This standardization minimizes variations in data quality and enhances the reliability of research outcomes. In the context of the BPD Collaborative, clinical informatics has been instrumental in facilitating the implementation of essential strategies, such as optimizing ventilator management and nutritional practices. By tracking patient responses and outcomes using informatics tools, the collaborative has identified best practices and areas for improvement, contributing to the enhanced quality of care and improved patient outcomes (100, 101).

In the study by Guaman et al, clinical informatics played a key role in understanding neonates with severe BPD. For instance, clinical informatics facilitated the uniform collection of clinical data across the eight NICUs. A standardized clinical data form was designed and implemented, ensuring that essential patient information, including gestational age, respiratory support requirements, and medication usage, was consistently recorded for all inpatients born at <32 weeks. The study defined severe BPD based on specific criteria, including the need for ≥30% supplemental oxygen and/or positive pressure ventilation at 36 weeks postmenstrual age. Clinical informatics played a key role in applying these criteria uniformly across participating NICUs. Clinical informatics also facilitated the analysis of management practices across the participating NICUs. The study examined differences in the use of interventions such as mechanical ventilation, diuretics, inhaled corticosteroids, and inhaled β-agonists. By collecting and analyzing these data using informatics tools, the researchers identified variations in the management of infants with severe BPD among different centers (102).

In addressing the significant challenges that persist in multi-center collaborations for BPD research, it is essential to recognize the multifaceted nature of this condition. The complex and multifactorial etiology of BPD, encompassing factors such as prematurity, inflammation, and genetic predisposition, contributes to the intricate clinical landscape (58, 100). Furthermore, BPD is not a one-size-fits-all condition. It presents a wide spectrum of patient characteristics, including variations in gestational age, birth weight, and comorbidities, necessitating diverse and tailored management approaches that can evolve over the course of a patient's hospitalization and even throughout their lifetime (103).

The challenges extend to data collection and management practices. Current data collection methods are labor-intensive and often fraught with incompleteness, preventing a comprehensive and real-world representation of the clinical phenotype of BPD (1, 79). Moreover, the evolving definitions of disease pose an ongoing challenge. The field continuously refines diagnostic criteria and treatment strategies, making it critical to capture longitudinal data to track changes in patient care and outcomes over time. However, this longitudinal perspective remains elusive due to existing data limitations.

Data governance and privacy concerns also loom large, impeding the seamless sharing of critical information among institutions. Protecting patient privacy while facilitating data sharing demands sophisticated solutions and adherence to stringent regulations (1, 79). Additionally, BPD lacks a universally accepted “single” therapy, further complicating research efforts. Tailoring treatments to individual patients requires nuanced real-world data to inform decision-making and optimize care.

Efforts such as the OHDSI (Observational Health Data Sciences and Informatics) OMOP CDM (Common Data Model) present a promising avenue to overcome the substantial challenges in multi-center collaborations for bronchopulmonary dysplasia (BPD) research (104). The OHDSI initiative is at the forefront of harnessing clinical informatics to standardize and streamline data collection and analysis. The OMOP CDM, an integral component of OHDSI, serves as a standardized clinical data framework that allows the extraction, ingestion, and collation of pertinent variables from diverse sources, enabling the creation of disease-specific observational research registries like a BPD registry (105, 106).

Central to the success of these initiatives are widely recognized clinical terminologies, such as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) and LOINC (Logical Observation Identifiers Names and Codes) (107, 108). SNOMED CT provides a comprehensive and standardized clinical vocabulary that enhances interoperability and consistency in healthcare data, while LOINC facilitates the uniform identification of medical laboratory observations, ensuring data accuracy and comparability across institutions.

These standardized clinical data frameworks empower researchers to securely store data, conduct rigorous analyses, and efficiently share information among participating sites, thereby reducing the barriers to collaboration and facilitating data-driven research efforts. The concept of a “living BPD registry,” driven by OHDSI OMOP CDM and bolstered by SNOMED CT and LOINC, holds the potential to revolutionize BPD research and collaborative endeavors. It offers the opportunity for reproducible research in phenotype definitions and comparative analyses, leveraging a wealth of standardized real-world data across institutions. Ultimately, these initiatives not only lower the activation energy required for collaboration but also hold the promise of driving meaningful advancements in BPD care and research outcomes.

BPD research and the integration of clinical informatics have undoubtedly made significant progress, yet several formidable challenges persist (106). The current definition of BPD, based on respiratory support at 36 weeks postmenstrual age, represents a crucial step forward in standardizing diagnostic criteria and understanding long-term patient-centered outcomes (79). However, it remains an oversimplified representation of the intricate clinical reality and heterogeneity that exists within the BPD spectrum.

To address these challenges effectively, it is imperative to delve into the nuances that distinguish children with varying degrees of BPD severity. A more granular understanding can guide the development of interventions tailored to specific subgroups of patients while minimizing potential harm. This nuanced approach acknowledges the multifaceted nature of BPD and recognizes that each neonate’s clinical phenotype is unique.

Despite the existence of risk stratification tools for BPD, their full potential has not been realized due to limited integration with EHRs and concerns regarding prediction accuracy. This stands in stark contrast to the robust adoption of other neonatal tools, such as those used for predicting early-onset sepsis or guiding phototherapy recommendations (52, 78). To comprehensively address the complexities of defining, preventing, and treating BPD, CDS tools must not only seamlessly integrate with EHRs but also offer comprehensive treatment guidance. Achieving this requires a strategic approach focused on tailored care rather than advocating for a one-size-fits-all reduction in care.

Our proposed clinical informatics approach aims to empower clinicians with timely, individualized information for optimized decision-making. By harnessing data-driven insights and predictive models, we provide care providers with the tools to make informed decisions that align with each neonate's specific risk profile. This approach takes into account both clinical severity and individual risk factors. While some moderate-risk neonates may indeed benefit from escalated care, applying the same approach universally could lead to overutilization of resources and unnecessary interventions. Clinical informatics enables the identification of moderate-risk neonates most likely to benefit from escalated care, thereby maximizing the utility of interventions while minimizing potential harm or resource inefficiency.

Collaborative efforts involving multiple centers are imperative for advancing our understanding of BPD. Such collaborations bring together larger sample sizes, diverse patient populations, and a broader range of expertise. Nevertheless, challenges related to data collection, storage, and the lack of standardization of terms and definitions persist. To facilitate seamless collaboration and interoperability, the standardization of data elements and mapping to recognized clinical terminologies such as ICD-10 and SNOMED-CT is essential. Additionally, the establishment of a “living BPD registry” is crucial, capable of efficiently collecting and storing data while incorporating more granular information such as clinical biomarkers, echocardiography/radiographic data, and high-resolution vital signs and ventilator data. This approach enables more accurate clinical phenotyping, thereby advancing our understanding of BPD and facilitating tailored care.

In conclusion, BPD is a complex disease that affects premature infants and is responsible for significant morbidity and healthcare costs. While clinical informatics holds promise in improving BPD prevention, management, and outcomes through the use of clinical decision support tools, we are still in the very early stages of using these applications effectively. Research efforts utilizing multi-center collaborations and state-of-the-art analysis techniques may help identify clinical phenotypes of BPD. However, many challenges remain in BPD research and clinical informatics, including the need for standardized data collection and definitions, full integration with EMR, the development of precise and personalized treatments for different BPD subtypes, and the logistics and governance issues with data storage, utilization, and collaboration. Overall, while there is promise in the integration of clinical informatics into BPD management and research, much work remains to be done to fully realize its potential in improving outcomes for these vulnerable patients.