Should an Age Cutoff Be Considered for Elective Total Knee Arthroplasty Patients? An Analysis of Operative Success Based on Patient-Reported Outcomes.

Total knee arthroplasty (TKA) is increasing in the elderly population; however, some patients, family members, and surgeons raise age-related concerns over expected improvement and risks. This study aimed to (1) evaluate the relationship between age and change in patient-reported outcome measures (PROMs); (2) model how many patients would be denied improvements in PROMs if hypothetical age cutoffs were implemented; and (3) assess length of stay (LOS), readmission, reoperation, and mortality per age group. A prospective cohort of 4,396 primary TKAs (August 2015-August 2018) was analyzed. One-year PROMs were evaluated via Knee injury and Osteoarthritis Outcome Score (KOOS)-pain, -physical function short form (-PS), and -quality of life (-QOL), as well as Veterans Rand-12 (VR-12) physical (-PCS) and mental component (-MCS) scores. Positive predictive values (PPVs) of the number of postoperative "failures" (i.e., unattained minimal clinically important difference in PROMs) relative to number of hypothetically denied "successes" from a theoretical age-group restriction was estimated. KOOS-PS and QOL median score improvements were equivalent among all age groups (p = 0.946 and p = 0.467, respectively). KOOS-pain improvement was equivalent for ≥80 and 60-69-year groups (44.4 [27.8-55.6]). Median VR-12 PCS improvements diminished as age increased (15.9, 14.8, and 13.4 for the 60-69, 70-79, and ≥80 groups, respectively; p = 0.002) while improvement in VR-12 MCS was similar among age groups (p = 0.440). PPV for failure was highest in the ≥80 group, yet remained <34% for all KOOS measures. Overall mortality was highest in the ≥80 group (2.14%, n = 9). LOS >2, non-home discharge, and 90-day readmission were highest in the ≥80 group (8.11% [n = 24], p < 0.001; 33.7% [n = 109], p < 0.001; and 34.4% [n = 111], p = 0.001, respectively). Elderly patients exhibited similar improvement in PROMs to younger counterparts despite higher LOS, non-home discharge, and 90-day readmission. Therefore, special care pathways should be implemented for those age groups.

Similar Healthcare Utilization and 1-Year Patient-Reported Outcomes between Cemented and Cementless Primary Total Knee Arthroplasty: A Propensity Score-Matched Analysis.

Cementless fixation for total knee arthroplasty (TKA) has gained traction with the advent of newer fixation technologies. This study assessed (1) healthcare utilization (length of stay (LOS), nonhome discharge, 90-day readmission, and 1-year reoperation); (2) 1-year mortality; and (3) 1-year joint-specific and global health-related patient-reported outcome measures (PROMs) among patients who received cementless versus cemented TKA. Patients who underwent cementless and cemented TKA at a single institution (July 2015-August 2018) were prospectively enrolled. A total of 424 cementless and 5,274 cemented TKAs were included. The cementless cohort was propensity score-matched to a group cemented TKAs (1:3-cementless: n = 424; cemented: n = 1,272). Within the matched cohorts, 76.9% (n = 326) cementless and 75.9% (n = 966) cementless TKAs completed 1-year PROMs. Healthcare utilization measures, mortality and the median 1-year change in knee injury and osteoarthritis outcome score (KOOS)-pain, KOOS-physical function short form (PS), KOOS-knee related quality of life (KRQOL), Veteran Rand (VR)-12 mental composite (MCS), and physical composite (PCS) scores were compared. The minimal clinically important difference (MCID) for PROMs was calculated. Cementless TKA exhibited similar rates of median LOS (p = 0.109), nonhome discharge disposition (p = 0.056), all-cause 90-day readmission (p = 0.226), 1-year reoperation (p = 0.597), and 1-year mortality (p = 0.861) when compared with cemented TKA. There was no significant difference in the median 1-year improvement in KOOS-pain (p = 0.370), KOOS-PS (p = 0.417), KOOS-KRQOL (p = 0.101), VR-12-PCS (p = 0.269), and VR-12-MCS (p = 0.191) between the cementless and cemented TKA cohorts. Rates of attaining MCID were similar in both cohorts for assessed PROMs (p > 0.05, each) except KOOS-KRQOL (cementless: n = 313 (96.0%) vs. cemented: n = 895 [92.7%]; p = 0.036). Cementless TKA provides similar healthcare-utilization, mortality, and 1-year PROM improvement versus cemented TKA. Cementless fixation in TKA may provide value through higher MCID improvement in quality of life. Future episode-of-care cost-analyses and longer-term survivorship investigations are warranted.

Blood Management in Total Knee Arthroplasty: A Nationwide Analysis from 2011 to 2018.

Both advances in perioperative blood management, anesthesia, and surgical technique have improved transfusion rates following primary total knee arthroplasty (TKA), and have driven substantial change in preoperative blood ordering protocols. Therefore, blood management in TKA has seen substantial changes with the implementation of preoperative screening, patient optimization, and intra- and postoperative advances. Thus, the purpose of this study was to examine changes in blood management in primary TKA, a nationwide sample, to assess gaps and opportunities. The American College of Surgeons National Surgical Quality Improvement Program database was used to identify TKA (n = 337,160) cases from 2011 to 2018. The following variables examined, such as preoperative hematocrit (HCT), anemia (HCT <35.5% for females and <38.5% for males), platelet count, thrombocytopenia (platelet count < 150,000/µL), international normalized ration (INR), INR > 2.0, bleeding disorders, preoperative, and postoperative transfusions. Analysis of variances were used to examine changes in continuous variables, and Chi-squared tests were used for categorical variables. There was a substantial decrease in postoperative transfusions from high of 18.3% in 2011 to a low of 1.0% in 2018, (p < 0.001), as well as in preoperative anemia from a high of 13.3% in 2011 to a low of 9.5% in 2016 to 2017 (p < 0.001). There were statistically significant, but clinically irrelevant changes in the other variables examined. There was a HCT high of 41.2 in 2016 and a low of 40.4 in 2011 to 2012 (p < 0.001). There was platelet count high of 247,400 in 2018 and a low of 242,700 in 201 (p < 0.001). There was a high incidence of thrombocytopenia of 5.2% in 2017 and a low of low of 4.4% in 2018 (p < 0.001). There was a high INR of 1.037 in 2011 and a low of 1.021 in 2013 (p < 0.001). There was a high incidence of INR >2.0 of 1.0% in 2012 to 2015 and a low of 0.8% in 2016 to 2018 (p = 0.027). There was a high incidence of bleeding disorders of 2.9% in 2013 and a low of 1.8% in 2017 to 2018 (p < 0.001). There was a high incidence of preoperative transfusions of 0.1% in 2011 to 2014 and a low of <0.1% in 2015 to 2018 (p = 0.021). From 2011 to 2018, there has been substantial decreases in patients receiving postoperative transfusions after primary TKA. Similarly, although a decrease in patients with anemia was seen, there remains 1 out 10 patients with preoperative anemia, highlighting the opportunity to further improve and address this potentially modifiable risk factor before surgery. These findings may reflect changes during TKA patient selection, optimization, or management, and emphasizes the need to further advance multimodal approaches for perioperative blood management of TKA patients. This is a Level III study.

From the Patient's Perspective: Orthopedic Virtual Rounds.

Interdisciplinary rounding on hospital inpatients is an integral part of providing high-quality, safe patient care. As orthopedic groups have grown and geographic coverage increased, surgeons are challenged to make in-person rounds on their patients every day given time constraints and physical distances. Virtual technology is being used in multiple healthcare settings to provide patients with the opportunity to connect with health care professionals when in-person options are not available. The purpose of this study was to explore the patient experience of virtual inpatient rounding. Using digital communication technology, virtual rounds were conducted by having the surgeon connect via their mobile device or laptop to the nursing unit's communication tablet. Twenty-seven patient interviews were digitally recorded and qualitatively analyzed. Results demonstrated that virtual rounds provided a positive patient experience for many. Most patients felt that virtual rounds were a good alternative when in-person rounds are not possible. Dissatisfaction was related to feeling "rushed" by the surgeon. This feedback can be used to better prepare patients and providers for virtual rounds and to enhance virtual technologies.

Optimizing Patient Experience by Improving Patient-Clinician Communication Through Hospital Electronic Systems.

Providing high value care is essential in today's healthcare context. A critical aspect of this is ensuring patients have a positive patient experience. The use of electronic systems can serve as enablers in this regard as this exploratory research highlights.

Virtual Orthopaedic Examination of the Lower Extremity: The Know-How of an Emerging Skill.

¼: Telemedicine has become an emerging necessity in the practice of orthopaedic surgery following the paradigm shift that was brought on by the COVID-19 pandemic. ¼: Physical examination is an integral component of orthopaedic care and plays a crucial role in diagnosis. ¼: Based on our experience and expert opinion in the literature, we recommend the following infrastructure for a virtual orthopaedic physical examination: a computing device with a functioning camera and high-definition input/output audio, a 720p (high-definition) display, a processing speed of 3.4 GHz, an internet connection speed range from 1 to 25 Mbps, adequate lighting, a steady camera that is positioned 3 to 6 ft (0.9 to 1.8 m) from the patient, a quiet environment for the examination, and clothing that exposes the area to be examined. ¼: When performing a virtual examination of the lower extremity, inspection, range of motion, and gait analysis can be easily translated by verbally instructing the patient to position his or her body or perform the relevant motion. Self-palpation accompanied by visual observation can be used to assess points of tenderness. Strength testing can be performed against gravity or by using household objects with known weights. Many special tests (e.g., the Thessaly test with knee flexion at 20° for meniscal tears) can also be translated to a virtual setting by verbally guiding patients through relevant positioning and motions. ¼: Postoperative wound assessment can be performed in the virtual setting by instructing the patient to place a ruler next to the wound for measuring the dimensions and using white gauze for color control. The wound can be visually assessed when the patient's camera or smartphone is positioned 6 to 18 in (15 to 46 cm) away and is held at a 45° angle to the incision.

The Potential Effects of Imposing a Body Mass Index Threshold on Patient-Reported Outcomes After Total Knee Arthroplasty.

BACKGROUND: Operative eligibility thresholds based on body mass index (BMI) alone may risk restricting access to improved pain control, function, and quality of life. This study evaluated the use of BMI-cutoffs to offering TKA in avoiding: 1) 90-day readmission, 2) one-year mortality, and 3) failure to achieve clinically important one-year PROMS improvement (MCID). METHODS: A total of 4126 primary elective unilateral TKA patients from 2015 to 2018 were prospectively collected. For specific BMI(kg/m2) cutoffs: 30, 35, 40, 45, and 50, the positive predictive value (PPV) for 90-day readmission, one-year mortality, and failure to achieve one-year MCID were calculated. The number of patients denied complication-free postoperative courses per averted adverse outcome/failed improvement was estimated. RESULTS: Rates of 90-day readmission and one-year mortality were similar across BMI categories (P > .05, each). PPVs for preventing 90-day readmission and one-year mortality were low across all models of BMI cutoffs. The highest PPV for 90-day readmission and one-year mortality was detected at cutoffs of 45 (6.4%) and 40 (0.87%), respectively. BMI cutoff of 40 would deny 18 patients 90-day readmission-free, and 194 patients one-year mortality-free postoperative courses for each averted 90-day readmission/one-year mortality. Such cutoff would also deny 11 patients an MCID per avoided failure. Implementing BMI thresholds alone did not influence the rate of improvements in KOOS-PS, KRQOL, or VR-12. CONCLUSION: Utilizing BMI cutoffs as the sole determinants of TKA ineligibility may deny patients complication-free postoperative courses and clinically important improvements. Shared decision-making supported by predictive tools may aid in balancing the potential benefit TKA offers to obese patients with the potentially increased complication risk and cost of care provision.

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Knee.

BACKGROUND: Revisions and reoperations for patients who have undergone total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and distal femoral replacement (DFR) necessitates accurate identification of implant manufacturer and model. Failure risks delays in care, increased morbidity, and further financial burden. Deep learning permits automated image processing to mitigate the challenges behind expeditious, cost-effective preoperative planning. Our aim was to investigate whether a deep-learning algorithm could accurately identify the manufacturer and model of arthroplasty implants about the knee from plain radiographs. METHODS: We trained, validated, and externally tested a deep-learning algorithm to classify knee arthroplasty implants from one of 9 different implant models from retrospectively collected anterior-posterior (AP) plain radiographs from four sites in one quaternary referral health system. The performance was evaluated by calculating the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, and accuracy when compared with a reference standard of implant model from operative reports. RESULTS: The training and validation data sets were comprised of 682 radiographs across 424 patients and included a wide range of TKAs from the four leading implant manufacturers. After 1000 training epochs by the deep-learning algorithm, the model discriminated nine implant models with an AUC of 0.99, accuracy 99%, sensitivity of 95%, and specificity of 99% in the external-testing data set of 74 radiographs. CONCLUSIONS: A deep learning algorithm using plain radiographs differentiated between 9 unique knee arthroplasty implants from four manufacturers with near-perfect accuracy. The iterative capability of the algorithm allows for scalable expansion of implant discriminations and represents an opportunity in delivering cost-effective care for revision arthroplasty.

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip.

BACKGROUND: The surgical management of complications surrounding patients who have undergone hip arthroplasty necessitates accurate identification of the femoral implant manufacturer and model. Failure to do so risks delays in care, increased morbidity, and further economic burden. Because few arthroplasty experts can confidently classify implants using plain radiographs, automated image processing using deep learning for implant identification may offer an opportunity to improve the value of care rendered. METHODS: We trained, validated, and externally tested a deep-learning system to classify total hip arthroplasty and hip resurfacing arthroplasty femoral implants as one of 18 different manufacturer models from 1972 retrospectively collected anterior-posterior (AP) plain radiographs from 4 sites in one quaternary referral health system. From these radiographs, 1559 were used for training, 207 for validation, and 206 for external testing. Performance was evaluated by calculating the area under the receiver-operating characteristic curve, sensitivity, specificity, and accuracy, as compared with a reference standard of implant model from operative reports with implant serial numbers. RESULTS: The training and validation data sets from 1715 patients and 1766 AP radiographs included 18 different femoral components across four leading implant manufacturers and 10 fellowship-trained arthroplasty surgeons. After 1000 training epochs by the deep-learning system, the system discriminated 18 implant models with an area under the receiver-operating characteristic curve of 0.999, accuracy of 99.6%, sensitivity of 94.3%, and specificity of 99.8% in the external-testing data set of 206 AP radiographs. CONCLUSIONS: A deep-learning system using AP plain radiographs accurately differentiated among 18 hip arthroplasty models from four industry leading manufacturers.

Gamer's Health Guide: Optimizing Performance, Recognizing Hazards, and Promoting Wellness in Esports.

Electronic sports (esports), or competitive video gaming, is a rapidly growing industry and phenomenon. While around 90% of American children play video games recreationally, the average professional esports athlete spends 5.5 to 10 h gaming daily. These times and efforts parallel those of traditional sports activities where individuals can participate at the casual to the professional level with the respective time commitments. Given the rapid growth in esports, greater emphasis has been placed on identification, management, and prevention of common health hazards that are associated with esports participation while also focusing on the importance of health promotion for this group of athletes. This review outlines a three-point framework for sports medicine providers, trainers, and coaches to provide a holistic approach for the care of the esports athlete. This esports framework includes awareness and management of common musculoskeletal and health hazards, opportunities for health promotion, and recommendations for performance optimization.

Transformation from a traditional model to a virtual model of care in orthopaedic surgery: COVID-19 experience and beyond.

Virtual encounters have experienced an exponential rise amid the current COVID-19 crisis. This abrupt change, seen in response to unprecedented medical and environmental challenges, has been forced upon the orthopaedic community. However, such changes to adopting virtual care and technology were already in the evolution forecast, albeit in an unpredictable timetable impeded by regulatory and financial barriers. This adoption is not meant to replace, but rather augment established, traditional models of care while ensuring patient/provider safety, especially during the pandemic. While our department, like those of other institutions, has performed virtual care for several years, it represented a small fraction of daily care. The pandemic required an accelerated and comprehensive approach to the new reality. Contemporary literature has already shown equivalent safety and patient satisfaction, as well as superior efficiency and reduced expenses with musculoskeletal virtual care (MSKVC) versus traditional models. Nevertheless, current literature detailing operational models of MSKVC is scarce. The current review describes our pre-pandemic MSKVC model and the shift to a MSKVC pandemic workflow that enumerates the conceptual workflow organization (patient triage, from timely care provision based on symptom acuity/severity to a continuum that includes future follow-up). Furthermore, specific setup requirements (both resource/personnel requirements such as hardware, software, and network connectivity requirements, and patient/provider characteristics respectively), and professional expectations are outlined. MSKVC has already become a pivotal element of musculoskeletal care, due to COVID-19, and these changes are confidently here to stay. Readiness to adapt and evolve will be required of individual musculoskeletal clinical teams as well as organizations, as established paradigms evolve. Cite this article: Bone Joint Open 2020;1-6:272-280.

Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions.

PURPOSE OF REVIEW: With the unprecedented advancement of data aggregation and deep learning algorithms, artificial intelligence (AI) and machine learning (ML) are poised to transform the practice of medicine. The field of orthopedics, in particular, is uniquely suited to harness the power of big data, and in doing so provide critical insight into elevating the many facets of care provided by orthopedic surgeons. The purpose of this review is to critically evaluate the recent and novel literature regarding ML in the field of orthopedics and to address its potential impact on the future of musculoskeletal care. RECENT FINDINGS: Recent literature demonstrates that the incorporation of ML into orthopedics has the potential to elevate patient care through alternative patient-specific payment models, rapidly analyze imaging modalities, and remotely monitor patients. Just as the business of medicine was once considered outside the domain of the orthopedic surgeon, we report evidence that demonstrates these emerging applications of AI warrant ownership, leverage, and application by the orthopedic surgeon to better serve their patients and deliver optimal, value-based care.

Can a machine learning model accurately predict patient resource utilization following lumbar spinal fusion?

BACKGROUND CONTEXT: With the increasing emphasis on value-based healthcare in Centers for Medicare and Medicaid Services reimbursement structures, bundled payment models have been adopted for many orthopedic procedures. Immense variability of patients across hospitals and providers makes these models potentially less viable in spine surgery. Machine-learning models have been shown reliable at predicting patient-specific outcomes following lumbar spine surgery and could, therefore, be applied to developing stratified bundled payment schemes. PURPOSE: (1) Can a Naïve Bayes machine-learning model accurately predict inpatient payments, length of stay (LOS), and discharge disposition, following dorsal and lumbar fusion? (2) Can such a model then be used to develop a risk-stratified payment scheme? STUDY DESIGN: A Naïve Bayes machine-learning model was constructed using an administrative database. PATIENT SAMPLE: Patients undergoing dorsal and lumbar fusion for nondeformity indications from 2009 through 2016 were included. Preoperative inputs included age group, gender, ethnicity, race, type of admission, All Patients Refined (APR) risk of mortality, APR severity of illness, and Clinical Classifications Software diagnosis code. OUTCOME MEASURES: Predicted resource utilization outcomes included LOS, discharge disposition, and total inpatient payments. Model validation was addressed via reliability, model output quality, and decision speed, based on application of training and validation sets. Risk-stratified payment models were developed according to APR risk of mortality and severity of illness. RESULTS: A Naïve Bayes machine-learning algorithm with adaptive boosting demonstrated high reliability and area under the receiver-operating characteristics curve of 0.880, 0.941, and 0.906 for cost, LOS, and discharge disposition, respectively. Patients with increased risk of mortality or severity of illness incurred costs resulting in greater inpatient payments in a patient-specific tiered bundled payment, reflecting increased risk on institutions caring for these patients. We found that a large range in expected payments due to individuals' preoperative comorbidities indicating an individualized risk-based model is warranted. CONCLUSIONS: A Naïve Bayes machine-learning model was shown to have good-to-excellent reliability and responsiveness for cost, LOS, and discharge disposition. Based on APR risk of mortality and APR severity of illness, there was a significant difference in episode costs from lowest to highest risk strata. After using normalized model error to develop a risk-adjusted proposed payment plan, it was found that institutions incur significantly more financial risk in flat bundled payment models for patients with higher rates of comorbidities.

Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring.

BACKGROUND: Driven by the recent ubiquity of big data and computing power, we established the Machine Learning Arthroplasty Laboratory (MLAL) to examine and apply artificial intelligence (AI) to musculoskeletal medicine. METHODS: In this review, we discuss the 2 core objectives of the MLAL as they relate to the practice and progress of orthopedic surgery: (1) patient-specific, value-based care and (2) human movement. RESULTS: We developed and validated several machine learning-based models for primary lower extremity arthroplasty that preoperatively predict patient-specific, risk-adjusted value metrics, including cost, length of stay, and discharge disposition, to provide improved expectation management, preoperative planning, and potential financial arbitration. Additionally, we leveraged passive, ubiquitous mobile technologies to build a small data registry of human movement surrounding TKA that permits remote patient monitoring to evaluate therapy compliance, outcomes, opioid intake, mobility, and joint range of motion. CONCLUSION: The rapid rate with which we in arthroplasty are acquiring and storing continuous data, whether passively or actively, demands an advanced processing approach: AI. By carefully studying AI techniques with the MLAL, we have applied this evolving technique as a first step that may directly improve patient outcomes and practice of orthopedics.

Predicting Inpatient Payments Prior to Lower Extremity Arthroplasty Using Deep Learning: Which Model Architecture Is Best?

BACKGROUND: Recent advances in machine learning have given rise to deep learning, which uses hierarchical layers to build models, offering the ability to advance value-based healthcare by better predicting patient outcomes and costs of a given treatment. The purpose of this study is to compare the performance of 2 common deep learning models, traditional multilayer perceptron (MLP), and the newer dense neural network (DenseNet), in predicting outcomes for primary total hip arthroplasty (THA) and total knee arthroplasty (TKA) as a foundation for future musculoskeletal studies seeking to utilize machine learning. METHODS: Using 295,605 patients undergoing primary THA and TKA from a New York State inpatient administrative database from 2009 to 2016, 2 neural network designs (MLP vs DenseNet) with different model regularization techniques (dropout, batch normalization, and DeCovLoss) were applied to compare model performance on predicting inpatient procedural cost using the area under the receiver operating characteristic curve (AUC). Models were implemented to identify high-cost surgical cases. RESULTS: DenseNet performed similarly to or better than MLP across the different regularization techniques in predicting procedural costs of THA and TKA. Applying regularization to DenseNet resulted in a significantly higher AUC as compared to DenseNet alone (0.813 vs 0.792, P = .011). When regularization methods were applied to MLP, the AUC was significantly lower than without regularization (0.621 vs 0.791, P = 1.1 × 10-15). When the optimal MLP and DenseNet models were compared in a head-to-head fashion, they performed similarly at cost prediction (P > .999). CONCLUSION: This study establishes that in predicting costs of lower extremity arthroplasty, DenseNet models improve in performance with regularization, whereas simple neural network models perform significantly worse without regularization. In light of the resource-intensive nature of creating and testing deep learning models for orthopedic surgery, particularly for value-centric procedures such as arthroplasty, this study establishes a set of key technical features that resulted in better prediction of inpatient surgical costs. We demonstrated that regularization is critically important for neural networks in arthroplasty cost prediction and that future studies should utilize these deep learning techniques to predict arthroplasty costs. LEVEL OF EVIDENCE: III.

Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review.

BACKGROUND: Driven by the rapid development of big data and processing power, artificial intelligence and machine learning (ML) applications are poised to expand orthopedic surgery frontiers. Lower extremity arthroplasty is uniquely positioned to most dramatically benefit from ML applications given its central role in alternative payment models and the value equation. METHODS: In this report, we discuss the origins and model specifics behind machine learning, consider its progression into healthcare, and present some of its most recent advances and applications in arthroplasty. RESULTS: A narrative review of artificial intelligence and ML developments is summarized with specific applications to lower extremity arthroplasty, with specific lessons learned from osteoarthritis gait models, joint-specific imaging analysis, and value-based payment models. CONCLUSION: The advancement and employment of ML provides an opportunity to provide data-driven, high performance medicine that can rapidly improve the science, economics, and delivery of lower extremity arthroplasty.

The Emergence of Distance Health Technologies.

Removing the geographic barriers to health care and extending care to the home has been the goal of the health-care system for decades as the introduction of new information technology capabilities has driven operational efficiencies in our daily lives. Patient demand for convenience and access continues to surge as these technologies are used for their personal lives. Coupled with the need to lower our health-care cost structure, distance health technologies are emerging as a care facilitator for our arthroplasty patients. A critical aspect of introducing distance health technologies is the requirement to define the entire episode of care. Once defined, metrics to assess success can be measured, and clinical and technical outcomes can be determined. Distance health technologies are emerging in the management of the arthroplasty episode of care through the preponderance of connectivity coupled with the adoption of mobile technologies, ushering in a new era of improved efficiency, efficacy, satisfaction, and outcomes while providing greater value for our patients.

Measuring User Satisfaction with Clinical Information Systems: What Really Matters?

The healthcare industry is increasingly investing in information systems / Information technology to enhance patient outcomes and organizational performance. This study qualitatively investigates the relationship between the overall satisfaction and five key aspects of clinical information systems. The results show that intuitive, easy-to-use, and collaboration enabling systems are more likely to satisfy users. The level of technical support and training also play key roles in determining user satisfaction in the clinical domain.

Evaluating Business Value of IT in Healthcare: Three Clinical Practices from Australia and the US.

Exponentially increasing costs in healthcare coupled with poor quality and limited access have motivated the healthcare industry to turn to IS/IT solutions to overcome these issues and facilitate superior healthcare delivery. In an environment of rapid development of new clinical informatics solutions claiming to provide better healthcare delivery, there is a paucity of systematic frameworks to robustly measure the actual value of these systems. The promised business value of these solutions has been elusive; hence, this study offers an approach for the evaluation of the business value of health IS/IT solutions based on a conceptual model, which has been validated using three clinical case studies.