Business model and strategy analysis for radiologists to use electronic health records (EHR)

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 90-94).Radiology is a medical specialty that employs imaging to diagnose and treat disease. It has long been an advance user of technology to capture, store, share, and use images electronically. In 2009, President Obama signed into law a measure, the HITECH Act (part of the stimulus package), that incentivizes healthcare providers to use electronic health records (EHR) in care delivery to improve quality, efficiency, safety, and reduce cost. The meaningful use (MU) program's Stage 1 requirements (part of HITECH Act) did not include imaging requirements, leading to confusion among radiologists and other specialties with regard to what MU offers to and requires of them. This thesis attempts to clarify the contribution radiology can make to MU by understanding radiology as a system, including its surrounding issues and its drivers, using Stage 1 MU requirements, data from qualitative research, and results from analysis. It answers the following question: Should Radiologists be considered part of the care team, leveraging EHR for meaningful use and hence eligible for incentive payments? It does so via the following methods: a) Discussing in detail current issues surrounding radiology systems from quality, safety, efficiency, and cost perspectives; b) Discussing MU in the context of radiology and reviewing what is missing in it for radiologists; c) Providing deeper systems analysis of current behaviors and why they have this form at this time; and d) Explaining how MU objectives can help to overcome many current issues and ultimately help to improve health outcomes. Specific changes to MU criteria to achieve these benefits are recommended. This thesis employs systems concepts and tools including system architecture and system dynamics for research and analysis to understand the system and derive hypotheses. A system dynamics model is used to analyze current drivers in imaging and to clarify the impact MU can have on these drivers. Thesis conclusions are supported by the analysis performed using the model as well as information gathered through industry interviews, online articles, academic and industry journals, and blogs.by Palani Perumal.S.M.in Engineering and Managemen

Structured reporting in cardiovascular computed tomography

While investigation techniques and image modalities become more and more advanced, radiology reports have remained in their classic form for the past decades. Structured reporting has shown its potential to increase the clarity, correctness, confidence, concision, completeness, consistency, communication, consultation and standardization of radiology reports. The increased report quality can mostly be attributed to a complete checklist like approach, standardized vocabulary through RadLex and RSNA provided templates which can be adapted to address very specific inquiries. Especially the interdisciplinary approach necessary to design and adapt those templates can ensure that all therapy influencing criteria are evaluated in the report. This may lead to a different therapy and outcome. Structured reporting also harbors great teaching opportunities, such as a checklist-like approach for young radiology residents and an image database of pathological findings. With a large analyzable database of reports, a statistical analysis becomes possible, which can e.g. lead to increasingly better screening algorithms. Technological challenges however, different data formats, varying degrees of quality of structured reporting systems and the concerns about work flow efficiency and report rigidity remain difficulties of structured reporting itself. Despite of this it also provides many future possibilities such as the implementation of medical guide lines into the report format, multi media reports, evaluation of radiation dose, management of follow-up appointments, automatic invoice and reimbursement systems and the improvement of data mining. Given the potential of structured reporting and its impact on patient care, we decided to evaluate its so far unknown benefit for patients with acute PE and PAD. For patients with APE, the structured reports were evaluated by two pulmonologists and two general internists and compared to the reports from the clinical routine of the same patient group. While all four referring clinicians perceived the structured CTPA reports as superior in clarity, only the pulmonologists found additional benefit in content and clinical utility. The structured reports did not alter patients’ management in patients with acute PE significantly. In the study concerning patients with diagnosed or suspected PAD the structured reports (run-off CTA/ lower extremities) were evaluated by two vascular surgeons and two vascular medicine specialists. The results showed, both groups regarded structured reports as superior in clarity, completeness, clinical relevance and usefulness. Especially vascular medicine specialists seemed to appreciate the structured reporting format. As in our PE study, structured reporting did not seem to alter further testing or therapy for the patients included in our study. Both studies demonstrate that referring clinicians prefer structured reporting of cardiovascular CT examinations over conventional reports

The risks of medical imaging: a survey of doctors' knowledge and consenting practice

Background: Diagnostic imaging forms an integral part of patient evaluation and its use has increased dramatically. Not only is medical imaging a source of increased radiation dose, but also poses other risks such as those related to the procedure performed, the contrast and drugs administered, acoustic and heat deposition and para-magnetic risks. While many studies have assessed doctors' knowledge of radiation risk, data regarding doctors' knowledge of the remaining risks of medical imaging and doctors' attitudes toward consenting practice for imaging is lacking. Aim: To survey and compare the levels of knowledge between referring clinicians and radiologists regarding the risks to patients undergoing medical imaging and to explore doctors' attitudes toward consenting practice. Method: A cross sectional, observational, descriptive study design was employed. The study was conducted using a non-validated, piloted, self-administered three-page questionnaire. The questionnaire was distributed to doctors in various stages of their medical careers at a tertiary level hospital. The questionnaire was constructed in sections including demographics, risks of medical imaging and consent practice. The maximum score potentially attainable was 79, with a point given for each correct answer. No points were given for incorrect, unsure or blank responses. Results: A total of 431 questionnaires were distributed but only 85 doctors (19 radiologists and 66 clinicians) returned a completed survey, yielding a response rate of 19,7%. Older respondents with more years of experience had greater levels of knowledge regarding the risks of medical imaging. There were no significant differences according to gender or university. Although the levels of knowledge of risk was poor overall, radiologists had greater levels of knowledge (mean knowledge score expressed as a percentage =79% compared to that of clinicians= 71%). The largest proportion of doctors' (49%) were of the opinion that clinicians should be responsible for obtaining consent for medical imaging. Only 18% of doctors (radiologists and clinicians) and 5% of clinicians admitted to feeling adequately prepared to obtain consent for medical imaging. Conclusion: We successfully surveyed and compared the levels of knowledge of medical imaging risks amongst doctors and determined their attitudes toward responsibility for consent. The levels of knowledge of the risks of medical imaging is inadequate among radiologists and poor amongst non-radiologists. While statutory body guidelines recommend that the performing health care provider obtain consent, there remains varying opinion as to who should obtain consent. The largest proportion of doctors' were of the opinion that clinicians should obtain consent for medical imaging - this despite clinicians' feelings of inadequacy when consenting patients to the risks of imaging. It is therefore important to take into consideration the levels of knowledge and comfort when making decisions as to who is best suited to obtain consent for medical imaging. With the increased dependence on medical imaging as part of the diagnostic work up, awareness of the risks of medical imaging is of tantamount importance. It is essential to review educational curricula and local policies in order to improve the levels of knowledge of risks of medical imaging amongst healthcare providers, thereby ensuring improved patient safety

Novel imaging and image-guided therapy of prostate cancer

Whole-gland prostate surgery and radiotherapy, the established approaches to localised prostate cancer (PCa), usually cause substantial adverse effects. Targeted image-guided cancer therapy has gained acceptance through improved PCa detection, localization and characterization by magnetic resonance imaging (MRI) and prostate-specific membrane antigen positron emission tomography-computed tomography (PSMA PET-CT). Focal therapy offers a potentially better trade-off between disease control and preservation of genitourinary and bowel function. MRI-guided transurethral ultrasound ablation (TULSA), a recently introduced treatment modality, uses therapeutic ultrasound directed through the urethra to thermally ablate the prostate under real-time MRI control. The applicability of TULSA to focal therapy of primary PCa, palliative therapy of symptomatic locally advanced PCa, and treatment of locally radiorecurrent PCa was investigated in a prospective setting. TULSA was shown to be a safe and effective method for local PCa control. Thermal injury was restricted to the planned treatment volume. This method enabled whole-gland ablation and focal ablation anywhere in the prostate. Furthermore, TULSA achieved local symptom relief in palliative care and encouraging preliminary oncological control in salvage care. These promising phase 1 study results enabled progression to phase 2 studies of patients with localised PCa and salvage of patients with radiorecurrent PCa. The diagnostic accuracy of MRI and PSMA PET-CT was studied to determine the extent of primary PCa, to plan TULSA treatment and evaluate treatment response. PSMA PET-CT was found to be a more sensitive method for detecting metastatic disease and appeared to accurately reflect the extent of local disease before and after TULSA treatment. PSMA PET-CT appears to detect some falsepositive bone lesions. The advantages of using MRI and PSMA PET-CT in treatment planning and monitoring treatment response are under further investigation. These studies have shown 18F-PSMA-1007 PET-CT to be effective in PCadiagnosis and TULSA to be effective in PCa therapyModernit kuvantamismenetelmät ja kuvantamisohjatut hoidot eturauhassyövässä Vakiintuneet paikallisen eturauhassyövän (PCa) hoitomenetelmät, leikkaus ja sädehoito, kohdistuvat koko rauhaseen ja aiheuttavat merkittäviä haittavaikutuksia. Magneettikuvantamisella (MRI) ja eturauhassyövän entsyymikuvantamisella (PSMA PET-TT) PCa:n havaitseminen, paikallistaminen ja karakterisointi ovat tarkentuneet. Kohdennetut kuvantamisohjatut syöpähoidot ovat siksi saaneet hyväksynnän ja tarjoavat mahdollisesti optimaalisemman vaihtoehdon hoidon hyödyn ja sen virtsa- ja sukupuolielimiin kohdistuvien haittojen suhdetta ajatellen. MRI-ohjattu eturauhasen kuumennushoito (TULSA) on uusi menetelmä, jossa virtsaputken kautta kudosta tuhoavaa ultraääntä ohjataan eturauhaseen reaaliaikaisessa MRI-ohjauksessa ja -valvonnassa. TULSA:n käyttökelpoisuutta primaarin PCa:n kohdennetussa hoidossa, paikallisesti edenneen PCa:n palliatiivisessa hoidossa ja sädehoidon jälkeen paikallisesti uusiutuneen PCa:n hoidossa tutkittiin prospektiivisessa tutkimusasetelmassa. TULSA-menetelmän todettiin tuhoavan turvallisesti ja tehokkaasti eturauhaskudosta. Lämpövaurio rajautui suunnitellulle hoitoalueelle. Menetelmä mahdollisti kuumennushoidon käytön kaikkialla eturauhasessa, koko rauhasessa tai paikallisemmin. Lisäksi TULSA-hoito lievensi paikallisoireita palliatiivisilla potilailla ja oli tehokas sädehoidon jälkeen paikallisesti uusiutuneessa PCa:ssä. Lupaavien ensimmäisen vaiheen tutkimustulosten takia olemme siirtyneet toisen vaiheen tutkimuksiin näillä uusilla indikaatioilla. MRI:n ja PSMA PET-TT:n diagnostista tarkuutta tutkittiin primaarin PCa:n levinneisyyden selvittelyssä ja TULSA-hoidon suunnittelussa sekä hoitovasteen arvioinnissa. PSMA PET-TT:n havaittiin olevan herkempi menetelmä etäpesäkkeiden tunnistamisessa ja se näytti tarkasti taudin laajuuden ennen ja jälkeen TULSAhoidon. PSMA PET-TT tunnistaa myös vääriä positiivisia luustomuutoksia. MRI:n ja PSMA PET-TT:n kliinistä hyötyä TULSA-hoidon suunnittelussa ja hoitovasteen seurannassa tutkitaan edelleen. Tutkimuksemme ovat osoittaneet PSMA PET-TT:n hyödyllisyyden PCa:n diagnostiikassa ja TULSA:n turvallisuuden ja tehon PCa:n hoidossa

An evaluation of a checklist in Musculoskeletal (MSK) radiographic image interpretation when using Artificial Intelligence (AI)

Background: AI is being used increasingly in image interpretation tasks. There are challenges for its optimal use in reporting environments. Human reliance on technology and bias can cause decision errors. Trust issues exist amongst radiologists and radiographers in both over-reliance (automation bias) and reluctance in AI use for decision support. A checklist, used with the AI to mitigate against such biases, may optimise the use of AI technologies and promote good decision hygiene. Method: A checklist, to be used in image interpretation with AI assistance, was developed. Participants interpreted 20 examinations with AI assistance and then re- interpreted the 20 examinations with AI and a checklist. The MSK images were presented to radiographers as patient examinations to replicate the image interpretation task in clinical practice. Image diagnosis and confidence levels on the diagnosis provided were collected following each interpretation. The participant perception of the use of the checklist was investigated via a questionnaire.Results: Data collection and analysis are underway and will be completed at the European Congress of Radiology in Vienna, March 2023. The impact of the use of a checklist in image interpretation with AI will be evaluated. Changes in accuracy and confidence will be investigated and results will be presented. Participant feedback will be analysed to determine perceptions and impact of the checklist also. Conclusion: A novel checklist has been developed to aid the interpretation of images when using AI. The checklist has been tested for its use in assisting radiographers in MSK image interpretation when using AI.<br/

Hierarchical clustering-based segmentation (HCS) aided diagstic image interpretation monitoring.

Machines are good at operations which require precision and computing objective measures. In contrast, humans are good at generalisation and making decisions based on their past experience and heuristics. Hence, to solve any problem with a solution involving human-machine interaction, it is imperative that the tasks are shared appropriately. However, the boundary which divides these two different set of tasks is not well defined in domains such as medical image interpretation. Therefore, one needs a versatile tool which is flexible enough to accommodate the varied requirements of the user. The aim of this study is to design and implement such a software tool to aid the radiologists in the interpretation of diagnostic images.Tissue abnormality in a medical image is usually related to a dissimilar part of an otherwise homogeneous image. The dissimilarity may be subtle or strong depending on the medical modality and the type of abnormal tissue. Hierarchical Clustering-based Segmentation (HCS) process is a dissimilarity highlighting process that yields a hierarchy of segmentation results. In this study, the HCS process was investigated for offering the user a versatile and flexible environment to perceive the varied dissimilarities that might be present in diagnostic images. Consequently, the user derives the maximum benefit from the computational capability (perception) of the machine and at the same time incorporate their own decision process (interpretation) at the appropriate places.As a result of the above investigation, this study demonstrates how HCS process can be used to aid radiologists in their interpretive tasks. Specifically this study has designed the following HCS process aided diagnostic image interpretation applications: interpretation of computed tomography (CT) images of the lungs to quantitatively measure the dimensions of the airways and the accompanying blood vessels; Interpretation of X-ray mammograms to quantitatively differentiate benign from malignant abnormalities. One of the major contribution of this study is to demonstrate how the above HCS process aided interpretation of diagnostic images can be used to monitor disease conditions. This thesis details the development and evaluation of the novel computer aided monitoring (CAM) system. The designed CAM system is used to objectively measure the properties of suspected abnormal areas in the CT images of the lungs and in X-ray mammogram. Thus, the CAM system can be used to assist the clinician to objectively monitor the abnormality. For instance, its response to treatment and consequently its prognosis. The implemented CAM system to monitor abnormalities in X-ray mammograms is briefly described below. Using the approximate location and size of the abnormality, obtained from the user, the HCS process automatically identifies the more appropriate boundaries of the different regions within a region of interest (ROI), centred at the approximate location. From the set of, HCS process segmented, regions the user identifies the regions which most likely represent the abnormality and the healthy areas. Subsequently, the CAM system compares the characteristics of the user identified abnormal region with that of the healthy region; to differentiate malignant from benign abnormality. In processing sixteen mammograms, the designed CAM system demonstrated the possibility of successfully differentiating malignant from benign abnormalities

The impact of AI on radiographic image reporting – perspectives of the UK reporting radiographer population

Background: It is predicted that medical imaging services will be greatly impacted by AI in the future. Developments in computer vision have allowed AI to be used for assisted reporting. Studies have investigated radiologists' opinions of AI for image interpretation (Huisman et al., 2019 a/b) but there remains a paucity of information in reporting radiographers' opinions on this topic.Method: A survey was developed by AI expert radiographers and promoted via LinkedIn/Twitter and professional networks for radiographers from all specialities in the UK. A sub analysis was performed for reporting radiographers only.Results: 411 responses were gathered to the full survey (Rainey et al., 2021) with 86 responses from reporting radiographers included in the data analysis. 10.5% of respondents were using AI tools? as part of their reporting role. 59.3% and 57% would not be confident in explaining an AI decision to other healthcare practitioners and 'patients and carers' respectively. 57% felt that an affirmation from AI would increase confidence in their diagnosis. Only 3.5% would not seek second opinion following disagreement from AI. A moderate level of trust in AI was reported: mean score = 5.28 (0 = no trust; 10 = absolute trust). 'Overall performance/accuracy of the system', 'visual explanation (heatmap/ROI)', 'Indication of the confidence of the system in its diagnosis' were suggested as measures to increase trust.Conclusion: AI may impact reporting professionals' confidence in their diagnoses. Respondents are not confident in explaining an AI decision to key stakeholders. UK radiographers do not yet fully trust AI. Improvements are suggested